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Silvia Goncalves

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Silvia Goncalves & Ana María Herrera & Lutz Kilian & Elena Pesavento, 2022. "When Do State-Dependent Local Projections Work?," Working Papers 2205, Federal Reserve Bank of Dallas.

    Cited by:

    1. Sarah Arndt & Zeno Enders, 2023. "The Transmission of Supply Shocks in Different Inflation Regimes," CESifo Working Paper Series 10839, CESifo.
    2. Sheng, Xin & Kim, Won Joong & Gupta, Rangan & Ji, Qiang, 2023. "The impacts of oil price volatility on financial stress: Is the COVID-19 period different?," International Review of Economics & Finance, Elsevier, vol. 85(C), pages 520-532.
    3. De Santis, Roberto A. & Tornese, Tommaso, 2023. "Energy supply shocks’ nonlinearities on output and prices," Working Paper Series 2834, European Central Bank.
    4. Taylor, Alan M. & Cloyne, James & Jordà , Òscar, 2023. "State-Dependent Local Projections: Understanding Impulse Response Heterogeneity," CEPR Discussion Papers 17903, C.E.P.R. Discussion Papers.
    5. Jessen, Jonas & Jessen, Robin & Galecka-Burdziak, Ewa & Góra, Marek & Kluve, Jochen, 2023. "The Micro and Macro Effects of Changes in the Potential Benefit Duration," IZA Discussion Papers 15978, Institute of Labor Economics (IZA).
    6. Kole, Erik & van Dijk, Dick, 2023. "Moments, shocks and spillovers in Markov-switching VAR models," Journal of Econometrics, Elsevier, vol. 236(2).
    7. Syed Sadaqat Ali Shah & Muhammad Asim Afridi, 2023. "Cyclical variation of fiscal multipliers in Caucasus and Central Asia economies: an empirical evidence," Economic Change and Restructuring, Springer, vol. 56(6), pages 4531-4563, December.
    8. Bunce, Alan & Carrillo-Maldonado, Paul, 2023. "Asymmetric effect of the oil price in the ecuadorian economy," Energy Economics, Elsevier, vol. 124(C).
    9. Finck, David & Hoffmann, Mathias & Hürtgen, Patrick, 2023. "On the empirical relevance of the exchange rate as a shock absorber at the zero lower bound," Discussion Papers 10/2023, Deutsche Bundesbank.

  2. Silvia Goncalves & Ana María Herrera & Lutz Kilian & Elena Pesavento, 2020. "Impulse Response Analysis for Structural Dynamic Models with Nonlinear Regressors," Working Papers 2019, Federal Reserve Bank of Dallas.

    Cited by:

    1. Pablo Guerrón-Quintana & Alexey Khazanov & Molin Zhong, 2023. "Financial and Macroeconomic Data Through the Lens of a Nonlinear Dynamic Factor Model," Finance and Economics Discussion Series 2023-027, Board of Governors of the Federal Reserve System (U.S.).
    2. Lutz Kilian & Xiaoqing Zhou, 2023. "Oil Price Shocks and Inflation," Working Papers 2312, Federal Reserve Bank of Dallas.
    3. Kilian, Lutz & Goncalves, Silvia & Herrera, Ana Maria & Pesavento, Elena, 2022. "When do state-dependent local projections work?," CEPR Discussion Papers 17265, C.E.P.R. Discussion Papers.
    4. Martin Bruns & Michele Piffer, 2021. "Monetary policy shocks over the business cycle: Extending the Smooth Transition framework," University of East Anglia School of Economics Working Paper Series 2021-07, School of Economics, University of East Anglia, Norwich, UK..
    5. Leonardo Nogueira Ferreira, 2023. "Monetary Policy Surprises, Financial Conditions, and the String Theory Revisited," Working Papers Series 573, Central Bank of Brazil, Research Department.
    6. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano & Mertens, Elmar, 2023. "Shadow-rate VARs," Discussion Papers 14/2023, Deutsche Bundesbank.
    7. Giovanni Ballarin, 2023. "Impulse Response Analysis of Structural Nonlinear Time Series Models," Papers 2305.19089, arXiv.org, revised Aug 2023.
    8. Dario Caldara & Chiara Scotti & Molin Zhong, 2021. "Macroeconomic and Financial Risks: A Tale of Mean and Volatility," International Finance Discussion Papers 1326, Board of Governors of the Federal Reserve System (U.S.).

  3. Sílvia GONÇALVES & Benoit PERRON, 2018. "Bootstrapping Factor Models With Cross Sectional Dependence," Cahiers de recherche 10-2018, Centre interuniversitaire de recherche en économie quantitative, CIREQ.

    Cited by:

    1. Ercument Cahan & Jushan Bai & Serena Ng, 2021. "Factor-Based Imputation of Missing Values and Covariances in Panel Data of Large Dimensions," Papers 2103.03045, arXiv.org, revised Feb 2022.
    2. Hounyo, Ulrich & Lahiri, Kajal, 2023. "Estimating the variance of a combined forecast: Bootstrap-based approach," Journal of Econometrics, Elsevier, vol. 232(2), pages 445-468.
    3. Shunan Zhao & Man Jin & Subal C. Kumbhakar, 2021. "Estimation of firm productivity in the presence of spillovers and common shocks," Empirical Economics, Springer, vol. 60(6), pages 3135-3170, June.
    4. Hou, Zhezhi & Zhao, Shunan & Kumbhakar, Subal C., 2023. "The GMM estimation of semiparametric spatial stochastic frontier models," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1450-1464.
    5. Huang, Haitao & Jiang, Lei & Leng, Xuan & Peng, Liang, 2023. "Bootstrap analysis of mutual fund performance," Journal of Econometrics, Elsevier, vol. 235(1), pages 239-255.
    6. Yohei Yamamoto & Naoko Hara, 2022. "Identifying factor‐augmented vector autoregression models via changes in shock variances," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 722-745, June.
    7. Jianqing Fan & Kunpeng Li & Yuan Liao, 2020. "Recent Developments on Factor Models and its Applications in Econometric Learning," Papers 2009.10103, arXiv.org.
    8. Min Seong Kim, 2021. "Robust Inference for Diffusion-Index Forecasts with Cross-Sectionally Dependent Data," Working papers 2021-04, University of Connecticut, Department of Economics.
    9. Javier Maldonado & Esther Ruiz, 2021. "Accurate Confidence Regions for Principal Components Factors," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(6), pages 1432-1453, December.

  4. Silvia Gonçalves & Benoit Perron & Antoine Djogbenou, 2016. "Bootstrap prediction intervals for factor models," CIRANO Working Papers 2016s-19, CIRANO.

    Cited by:

    1. Sílvia GONÇALVES & Benoit PERRON, 2018. "Bootstrapping Factor Models With Cross Sectional Dependence," Cahiers de recherche 10-2018, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    2. Allayioti, Anastasia & Venditti, Fabrizio, 2024. "The role of comovement and time-varying dynamics in forecasting commodity prices," Working Paper Series 2901, European Central Bank.
    3. Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Antoine Djogbenou & Sílvia Gonçalves & Benoit Perron, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 481-502, May.
    4. Michael W. McCracken & Serena Ng, 2021. "FRED-QD: A Quarterly Database for Macroeconomic Research," Review, Federal Reserve Bank of St. Louis, vol. 103(1), pages 1-44, January.
    5. Gagliardini, Patrick & Ossola, Elisa & Scaillet, Olivier, 2019. "A diagnostic criterion for approximate factor structure," Journal of Econometrics, Elsevier, vol. 212(2), pages 503-521.
    6. Cheng, Tingting & Gao, Jiti & Yan, Yayi, 2019. "Regime switching panel data models with interactive fixed effects," Economics Letters, Elsevier, vol. 177(C), pages 47-51.
    7. Hande Karabiyik & Joakim Westerlund, 2021. "Forecasting using cross-section average–augmented time series regressions," The Econometrics Journal, Royal Economic Society, vol. 24(2), pages 315-333.
    8. Antoine Djogbenou & Silvia Gonçalves & Benoit Perron, 2015. "Bootstrap inference in regressions with estimated factors and serial correlation," CIRANO Working Papers 2015s-20, CIRANO.
    9. Knut Are Aastveit & Claudia Foroni & Francesco Ravazzolo, 2014. "Density forecasts with MIDAS models," Working Paper 2014/10, Norges Bank.
    10. Xingyu Li & Yan Shen & Qiankun Zhou, 2022. "Confidence Intervals of Treatment Effects in Panel Data Models with Interactive Fixed Effects," Papers 2202.12078, arXiv.org.
    11. Yohei Yamamoto & Naoko Hara, 2022. "Identifying factor‐augmented vector autoregression models via changes in shock variances," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 722-745, June.
    12. Christis Katsouris, 2023. "Optimal Estimation Methodologies for Panel Data Regression Models," Papers 2311.03471, arXiv.org, revised Nov 2023.
    13. Min Seong Kim, 2021. "Robust Inference for Diffusion-Index Forecasts with Cross-Sectionally Dependent Data," Working papers 2021-04, University of Connecticut, Department of Economics.
    14. Javier Maldonado & Esther Ruiz, 2021. "Accurate Confidence Regions for Principal Components Factors," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(6), pages 1432-1453, December.
    15. Federico Bassetti & Roberto Casarin & Francesco Ravazzolo, 2019. "Density Forecasting," BEMPS - Bozen Economics & Management Paper Series BEMPS59, Faculty of Economics and Management at the Free University of Bozen.

  5. Prosper Dovonon & Silvia Gonçalves & Ulrich Hounyo & Nour Meddahi, 2016. "Bootstrapping high-frequency jump tests," CIRANO Working Papers 2016s-24, CIRANO.

    Cited by:

    1. Hounyo, Ulrich & Varneskov, Rasmus T., 2020. "Inference for local distributions at high sampling frequencies: A bootstrap approach," Journal of Econometrics, Elsevier, vol. 215(1), pages 1-34.
    2. Kim Christensen & Ulrich Hounyo & Mark Podolskij, 2017. "Is the diurnal pattern sufficient to explain the intraday variation in volatility? A nonparametric assessment," CREATES Research Papers 2017-30, Department of Economics and Business Economics, Aarhus University.
    3. Yuma Uehara, 2023. "Bootstrap method for misspecified ergodic Lévy driven stochastic differential equation models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(4), pages 533-565, August.
    4. Barunik, Jozef & Krehlik, Tomas & Vacha, Lukas, 2016. "Modeling and forecasting exchange rate volatility in time-frequency domain," European Journal of Operational Research, Elsevier, vol. 251(1), pages 329-340.
    5. Barunik, Jozef & Vacha, Lukas, 2018. "Do co-jumps impact correlations in currency markets?," Journal of Financial Markets, Elsevier, vol. 37(C), pages 97-119.
    6. Milan Ficura & Jiri Witzany, 2016. "Estimating Stochastic Volatility and Jumps Using High-Frequency Data and Bayesian Methods," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 66(4), pages 278-301, August.
    7. Ulrich Hounyo & Rasmus T. Varneskov, 2018. "Inference for Local Distributions at High Sampling Frequencies: A Bootstrap Approach," CREATES Research Papers 2018-16, Department of Economics and Business Economics, Aarhus University.
    8. Zhang, Chuanhai & Liu, Zhi & Liu, Qiang, 2021. "Jumps at ultra-high frequency: Evidence from the Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    9. Markus Bibinger & Nikolaus Hautsch & Alexander Ristig, 2024. "Jump detection in high-frequency order prices," Papers 2403.00819, arXiv.org.
    10. Camponovo, Lorenzo & Matsushita, Yukitoshi & Otsu, Taisuke, 2019. "Empirical likelihood for high frequency data," LSE Research Online Documents on Economics 100320, London School of Economics and Political Science, LSE Library.
    11. Kim Christensen & Ulrich Hounyo & Mark Podolskij, 2016. "Testing for heteroscedasticity in jumpy and noisy high-frequency data: A resampling approach," CREATES Research Papers 2016-27, Department of Economics and Business Economics, Aarhus University.
    12. Jozef Barunik & Pavel Fiser, 2019. "Co-jumping of Treasury Yield Curve Rates," Papers 1905.01541, arXiv.org.

  6. Silvia Goncalves & Michael W. McCracken & Benoit Perron, 2015. "Tests of Equal Accuracy for Nested Models with Estimated Factors," Working Papers 2015-25, Federal Reserve Bank of St. Louis.

    Cited by:

    1. Marine Carrasco & Barbara Rossi, 2016. "In-sample inference and forecasting in misspecified factor models," Economics Working Papers 1530, Department of Economics and Business, Universitat Pompeu Fabra.
    2. Giovannelli, Alessandro & Massacci, Daniele & Soccorsi, Stefano, 2021. "Forecasting stock returns with large dimensional factor models," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 252-269.
    3. Arabinda Basistha, 2023. "Estimation of short‐run predictive factor for US growth using state employment data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 34-50, January.
    4. Arabinda Basistha & Richard Startz, 2023. "Measuring Persistent Global Economic Factors with Output, Commodity Price, and Commodity Currency Data," Working Papers 23-05, Department of Economics, West Virginia University.
    5. Luiz Renato Lima & Lucas Lúcio Godeiro, 2023. "Equity‐premium prediction: Attention is all you need," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(1), pages 105-122, January.
    6. Luiz Renato Lima & Lucas Lúcio Godeiro & Mohammed Mohsin, 2021. "Time-Varying Dictionary and the Predictive Power of FED Minutes," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 149-181, January.
    7. Karen Miranda & Pilar Poncela & Esther Ruiz, 2022. "Dynamic factor models: Does the specification matter?," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(1), pages 397-428, May.
    8. Daniel Borup & Bent Jesper Christensen & Yunus Emre Ergemen, 2019. "Assessing predictive accuracy in panel data models with long-range dependence," CREATES Research Papers 2019-04, Department of Economics and Business Economics, Aarhus University.
    9. Daniel Borup & Martin Thyrsgaard, 2017. "Statistical tests for equal predictive ability across multiple forecasting methods," CREATES Research Papers 2017-19, Department of Economics and Business Economics, Aarhus University.
    10. Fosten, Jack, 2017. "Confidence intervals in regressions with estimated factors and idiosyncratic components," Economics Letters, Elsevier, vol. 157(C), pages 71-74.
    11. Michael W. McCracken, 2020. "Tests of Conditional Predictive Ability: Existence, Size, and Power," Working Papers 2020-050, Federal Reserve Bank of St. Louis.
    12. Antoine A. Djogbenou, 2017. "Model Selection In Factor-augmented Regressions With Estimated Factors," Working Paper 1391, Economics Department, Queen's University.
    13. Andreou, Elena & Ghysels, Eric, 2021. "Predicting the VIX and the volatility risk premium: The role of short-run funding spreads Volatility Factors," Journal of Econometrics, Elsevier, vol. 220(2), pages 366-398.
    14. In Choi & Hanbat Jeong, 2020. "Differencing versus nondifferencing in factor‐based forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(6), pages 728-750, September.
    15. Daniel Borup & Jonas N. Eriksen & Mads M. Kjær & Martin Thyrsgaard, 2020. "Predicting bond return predictability," CREATES Research Papers 2020-09, Department of Economics and Business Economics, Aarhus University.

  7. Antoine Djogbenou & Silvia Gonçalves & Benoit Perron, 2015. "Bootstrap inference in regressions with estimated factors and serial correlation," CIRANO Working Papers 2015s-20, CIRANO.

    Cited by:

    1. Sílvia GONÇALVES & Benoit PERRON, 2018. "Bootstrapping Factor Models With Cross Sectional Dependence," Cahiers de recherche 10-2018, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    2. Hounyo, Ulrich & Lahiri, Kajal, 2023. "Estimating the variance of a combined forecast: Bootstrap-based approach," Journal of Econometrics, Elsevier, vol. 232(2), pages 445-468.
    3. Silvia Gonçalves & Benoit Perron & Antoine Djogbenou, 2016. "Bootstrap prediction intervals for factor models," CIRANO Working Papers 2016s-19, CIRANO.
    4. Antoine A. Djogbenou, 2020. "Comovements in the real activity of developed and emerging economies: A test of global versus specific international factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(3), pages 344-370, April.
    5. Maldonado, Javier & Ruiz Ortega, Esther, 2017. "Accurate Subsampling Intervals of Principal Components Factors," DES - Working Papers. Statistics and Econometrics. WS 23974, Universidad Carlos III de Madrid. Departamento de Estadística.
    6. Fosten, Jack, 2017. "Confidence intervals in regressions with estimated factors and idiosyncratic components," Economics Letters, Elsevier, vol. 157(C), pages 71-74.
    7. Stauskas, Ovidijus & De Vos, Ignace, 2024. "Handling Distinct Correlated Effects with CCE," MPRA Paper 120194, University Library of Munich, Germany.
    8. Hande Karabiyik & Joakim Westerlund, 2021. "Forecasting using cross-section average–augmented time series regressions," The Econometrics Journal, Royal Economic Society, vol. 24(2), pages 315-333.
    9. Knut Are Aastveit & Claudia Foroni & Francesco Ravazzolo, 2014. "Density forecasts with MIDAS models," Working Paper 2014/10, Norges Bank.
    10. Yohei Yamamoto & Naoko Hara, 2022. "Identifying factor‐augmented vector autoregression models via changes in shock variances," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 722-745, June.
    11. Antoine A. Djogbenou, 2017. "Model Selection In Factor-augmented Regressions With Estimated Factors," Working Paper 1391, Economics Department, Queen's University.
    12. Djogbenou, Antoine & Sufana, Razvan, 2024. "Tests for group-specific heterogeneity in high-dimensional factor models," Journal of Multivariate Analysis, Elsevier, vol. 199(C).
    13. Javier Maldonado & Esther Ruiz, 2021. "Accurate Confidence Regions for Principal Components Factors," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(6), pages 1432-1453, December.
    14. Lixiong Yang, 2020. "State-dependent biases and the quality of China’s preliminary GDP announcements," Empirical Economics, Springer, vol. 59(6), pages 2663-2687, December.
    15. Federico Bassetti & Roberto Casarin & Francesco Ravazzolo, 2019. "Density Forecasting," BEMPS - Bozen Economics & Management Paper Series BEMPS59, Faculty of Economics and Management at the Free University of Bozen.

  8. Prosper Dovonon & Silvia Gonçalves, 2014. "Bootstrapping the GMM overidentification test Under first-order underidentification," CIRANO Working Papers 2014s-25, CIRANO.

    Cited by:

    1. Pablo Guerron-Quintana & Atsushi Inoue & Lutz Kilian, 2016. "Impulse Response Matching Estimators for DSGE Models," CESifo Working Paper Series 5730, CESifo.
    2. Atsushi Inoue & Lutz Kilian, 2016. "Joint Confidence Sets for Structural Impulse Responses," CESifo Working Paper Series 5746, CESifo.
    3. Doko Tchatoka, Firmin & Wang, Wenjie, 2021. "Uniform Inference after Pretesting for Exogeneity with Heteroskedastic Data," MPRA Paper 106408, University Library of Munich, Germany.
    4. Prosper Donovon & Alastair R. Hall, 2015. "GMM and Indirect Inference: An appraisal of their connections and new results on their properties under second order identification," Economics Discussion Paper Series 1505, Economics, The University of Manchester.
    5. Firmin Doko Tchatoka & Wenjie Wang, 2020. "Uniform Inference after Pretesting for Exogeneity," School of Economics and Public Policy Working Papers 2020-05, University of Adelaide, School of Economics and Public Policy.
    6. Prosper Dovonon & Alastair R. Hall, 2017. "The Asymptotic Properties of GMM and Indirect Inference Under Second-Order Identification," Economics Discussion Paper Series 1705, Economics, The University of Manchester.
    7. Dovonon, Prosper & Hall, Alastair R., 2018. "The asymptotic properties of GMM and indirect inference under second-order identification," Journal of Econometrics, Elsevier, vol. 205(1), pages 76-111.
    8. Guo, Shaojun & Li, Dong & Li, Muyi, 2019. "Strict stationarity testing and GLAD estimation of double autoregressive models," Journal of Econometrics, Elsevier, vol. 211(2), pages 319-337.
    9. Wang, Wenjie & Doko Tchatoka, Firmin, 2018. "On Bootstrap inconsistency and Bonferroni-based size-correction for the subset Anderson–Rubin test under conditional homoskedasticity," Journal of Econometrics, Elsevier, vol. 207(1), pages 188-211.
    10. Giuseppe Cavaliere & Rasmus Søndergaard Pedersen & Anders Rahbek, 2018. "The Fixed Volatility Bootstrap for a Class of Arch(q) Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(6), pages 920-941, November.
    11. Enrique Sentana, 2015. "Finite Underidentification," Working Papers wp2015_1508, CEMFI.
    12. Prosper Dovonon & Alastair Hall, 2018. "The Asymptotic Properties of GMM and Indirect Inference under Second-order Identi?cation," CIRANO Working Papers 2018s-37, CIRANO.
    13. Bing Su & Fukang Zhu & Ke Zhu, 2023. "Statistical inference for the logarithmic spatial heteroskedasticity model with exogenous variables," Papers 2301.06658, arXiv.org.
    14. Chen, Qihui & Fang, Zheng, 2019. "Inference on functionals under first order degeneracy," Journal of Econometrics, Elsevier, vol. 210(2), pages 459-481.
    15. Woosik Gong & Myung Hwan Seo, 2022. "Bootstraps for Dynamic Panel Threshold Models," Papers 2211.04027, arXiv.org, revised Nov 2023.
    16. Giovanni Angelini & Giuseppe Cavaliere & Luca Fanelli, 2022. "Bootstrap inference and diagnostics in state space models: With applications to dynamic macro models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 3-22, January.

  9. Sílvia Gonçalves & Ulrich Hounyo & Nour Meddahi, 2013. "Bootstrap inference for pre-averaged realized volatility based on non-overlapping returns," CREATES Research Papers 2013-07, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Ulrich Hounyo & Sílvia Goncalves & Nour Meddahi, 2013. "Bootstrapping pre-averaged realized volatility under market microstructure noise," CREATES Research Papers 2013-28, Department of Economics and Business Economics, Aarhus University.
    2. Hounyo, Ulrich, 2017. "Bootstrapping integrated covariance matrix estimators in noisy jump–diffusion models with non-synchronous trading," Journal of Econometrics, Elsevier, vol. 197(1), pages 130-152.
    3. Kim Christensen & Ulrich Hounyo & Mark Podolskij, 2017. "Is the diurnal pattern sufficient to explain the intraday variation in volatility? A nonparametric assessment," CREATES Research Papers 2017-30, Department of Economics and Business Economics, Aarhus University.
    4. Podolskij, Mark & Veliyev, Bezirgen & Yoshida, Nakahiro, 2017. "Edgeworth expansion for the pre-averaging estimator," Stochastic Processes and their Applications, Elsevier, vol. 127(11), pages 3558-3595.
    5. Meng, Bo & Vijh, Anand M., 2021. "Stock merger activity and industry performance," Journal of Banking & Finance, Elsevier, vol. 129(C).
    6. Hwang, Eunju & Shin, Dong Wan, 2018. "Two-stage stationary bootstrapping for bivariate average realized volatility matrix under market microstructure noise and asynchronicity," Journal of Econometrics, Elsevier, vol. 202(2), pages 178-195.

  10. Ulrich Hounyo & Sílvia Goncalves & Nour Meddahi, 2013. "Bootstrapping pre-averaged realized volatility under market microstructure noise," CREATES Research Papers 2013-28, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Giuseppe Cavaliere & Iliyan Georgiev, 2019. "Inference under random limit bootstrap measures," Papers 1911.12779, arXiv.org, revised Dec 2019.
    2. Hounyo, Ulrich, 2017. "Bootstrapping integrated covariance matrix estimators in noisy jump–diffusion models with non-synchronous trading," Journal of Econometrics, Elsevier, vol. 197(1), pages 130-152.
    3. Ulrich Hounyo & Bezirgen Veliyev, 2015. "Validity of Edgeworth expansions for realized volatility estimators," CREATES Research Papers 2015-21, Department of Economics and Business Economics, Aarhus University.
    4. Kim Christensen & Ulrich Hounyo & Mark Podolskij, 2017. "Is the diurnal pattern sufficient to explain the intraday variation in volatility? A nonparametric assessment," CREATES Research Papers 2017-30, Department of Economics and Business Economics, Aarhus University.
    5. Dovonon, Prosper & Taamouti, Abderrahim & Williams, Julian, 2022. "Testing the eigenvalue structure of spot and integrated covariance," Journal of Econometrics, Elsevier, vol. 229(2), pages 363-395.
    6. Hounyo, Ulrich & Varneskov, Rasmus T., 2017. "A local stable bootstrap for power variations of pure-jump semimartingales and activity index estimation," Journal of Econometrics, Elsevier, vol. 198(1), pages 10-28.
    7. Hounyo, Ulrich & Lahiri, Kajal, 2023. "Estimating the variance of a combined forecast: Bootstrap-based approach," Journal of Econometrics, Elsevier, vol. 232(2), pages 445-468.
    8. Camponovo, Lorenzo & Matsushita, Yukitoshi & Otsu, Taisuke, 2019. "Empirical likelihood for high frequency data," LSE Research Online Documents on Economics 100320, London School of Economics and Political Science, LSE Library.
    9. Zongwu Cai & Chaoqun Ma & Xianhua Mi, 2020. "Realized Volatility Forecasting Based on Dynamic Quantile Model Averaging," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202016, University of Kansas, Department of Economics, revised Sep 2020.
    10. Hwang, Eunju & Shin, Dong Wan, 2018. "Two-stage stationary bootstrapping for bivariate average realized volatility matrix under market microstructure noise and asynchronicity," Journal of Econometrics, Elsevier, vol. 202(2), pages 178-195.
    11. Ulrich Hounyo, 2014. "The wild tapered block bootstrap," CREATES Research Papers 2014-32, Department of Economics and Business Economics, Aarhus University.

  11. Silvia Gonçalves & Benoit Perron, 2012. "Bootstrapping factor-augmented regression models," CIRANO Working Papers 2012s-12, CIRANO.

    Cited by:

    1. Jack Fosten, 2016. "Model selection with factors and variables," University of East Anglia School of Economics Working Paper Series 2016-07, School of Economics, University of East Anglia, Norwich, UK..
    2. Marc K. Chan & Simon S. Kwok, 2022. "The PCDID Approach: Difference-in-Differences When Trends Are Potentially Unparallel and Stochastic," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1216-1233, June.
    3. Gonçalves, Sílvia & McCracken, Michael W. & Perron, Benoit, 2017. "Tests of equal accuracy for nested models with estimated factors," Journal of Econometrics, Elsevier, vol. 198(2), pages 231-252.
    4. Barigozzi, Matteo & Cho, Haeran & Fryzlewicz, Piotr, 2018. "Simultaneous multiple change-point and factor analysis for high-dimensional time series," Journal of Econometrics, Elsevier, vol. 206(1), pages 187-225.
    5. George Kapetanios & Laura Serlenga & Yongcheol Shin, 2019. "Testing for Correlated Factor Loadings in Cross Sectionally Dependent Panels," SERIES 02-2019, Dipartimento di Economia e Finanza - Università degli Studi di Bari "Aldo Moro", revised Jun 2019.
    6. Ashoka Mody & Milan Nedeljkovic, 2018. "Central Bank Policies and Financial Markets: Lessons from the Euro Crisis," CESifo Working Paper Series 7400, CESifo.
    7. Götz, T.B. & Hecq, A.W., 2014. "Testing for Granger causality in large mixed-frequency VARs," Research Memorandum 028, Maastricht University, Graduate School of Business and Economics (GSBE).
    8. Sium Bodha Hannadige & Jiti Gao & Mervyn J Silvapulle & Param Silvapulle, 2021. "Time Series Forecasting Using a Mixture of Stationary and Nonstationary Predictors," Monash Econometrics and Business Statistics Working Papers 6/21, Monash University, Department of Econometrics and Business Statistics.
    9. Sium Bodha Hannadige & Jiti Gao & Mervyn J. Silvapulle & Param Silvapulle, 2020. "Forecasting a Nonstationary Time Series with a Mixture of Stationary and Nonstationary Factors as Predictors," Monash Econometrics and Business Statistics Working Papers 19/20, Monash University, Department of Econometrics and Business Statistics.
    10. Gonçalves, Sílvia & Kaffo, Maximilien, 2015. "Bootstrap inference for linear dynamic panel data models with individual fixed effects," Journal of Econometrics, Elsevier, vol. 186(2), pages 407-426.
    11. Sílvia GONÇALVES & Benoit PERRON, 2018. "Bootstrapping Factor Models With Cross Sectional Dependence," Cahiers de recherche 10-2018, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    12. Bicu, A.C. & Lieb, L.M., 2015. "Cross-border effects of fiscal policy in the Eurozone," Research Memorandum 019, Maastricht University, Graduate School of Business and Economics (GSBE).
    13. Allayioti, Anastasia & Venditti, Fabrizio, 2024. "The role of comovement and time-varying dynamics in forecasting commodity prices," Working Paper Series 2901, European Central Bank.
    14. Karen Miranda & Pilar Poncela & Esther Ruiz, 2022. "Dynamic factor models: Does the specification matter?," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(1), pages 397-428, May.
    15. Cheng, Xu & Hansen, Bruce E., 2015. "Forecasting with factor-augmented regression: A frequentist model averaging approach," Journal of Econometrics, Elsevier, vol. 186(2), pages 280-293.
    16. González-Rivera, Gloria & Maldonado, Javier & Ruiz, Esther, 2019. "Growth in stress," International Journal of Forecasting, Elsevier, vol. 35(3), pages 948-966.
    17. Hounyo, Ulrich & Lahiri, Kajal, 2023. "Estimating the variance of a combined forecast: Bootstrap-based approach," Journal of Econometrics, Elsevier, vol. 232(2), pages 445-468.
    18. Yohei Yamamoto, 2012. "Bootstrap Inference for Impulse Response Functions in Factor-Augmented Vector Autoregressions," Global COE Hi-Stat Discussion Paper Series gd12-249, Institute of Economic Research, Hitotsubashi University.
    19. Silvia Gonçalves & Benoit Perron & Antoine Djogbenou, 2016. "Bootstrap prediction intervals for factor models," CIRANO Working Papers 2016s-19, CIRANO.
    20. Antoine A. Djogbenou, 2020. "Comovements in the real activity of developed and emerging economies: A test of global versus specific international factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(3), pages 344-370, April.
    21. Knut Are Aastveit & Hilde C. Bjørnland & Leif Anders Thorsrud, 2012. "What drives oil prices? Emerging versus developed economies," Working Paper 2012/11, Norges Bank.
    22. Maldonado, Javier & Ruiz Ortega, Esther, 2017. "Accurate Subsampling Intervals of Principal Components Factors," DES - Working Papers. Statistics and Econometrics. WS 23974, Universidad Carlos III de Madrid. Departamento de Estadística.
    23. Ashoka Mody & Milan Nedeljkovic, 2018. "Central Bank Policies and Financial Markets: Lessons from the Euro Crisis," Working Papers 253, Princeton University, Department of Economics, Center for Economic Policy Studies..
    24. Fosten, Jack, 2017. "Confidence intervals in regressions with estimated factors and idiosyncratic components," Economics Letters, Elsevier, vol. 157(C), pages 71-74.
    25. Bai, Jushan & Li, Kunpeng & Lu, Lina, 2014. "Estimation and inference of FAVAR models," MPRA Paper 60960, University Library of Munich, Germany.
    26. Stauskas, Ovidijus & De Vos, Ignace, 2024. "Handling Distinct Correlated Effects with CCE," MPRA Paper 120194, University Library of Munich, Germany.
    27. Artūras Juodis, 2022. "A regularization approach to common correlated effects estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 788-810, June.
    28. Mingjing Chen, 2021. "Tests for the explanatory power of latent factors," Statistical Papers, Springer, vol. 62(6), pages 2825-2856, December.
    29. Norman R. Swanson & Weiqi Xiong, 2018. "Big data analytics in economics: What have we learned so far, and where should we go from here?," Canadian Journal of Economics, Canadian Economics Association, vol. 51(3), pages 695-746, August.
    30. Hande Karabiyik & Joakim Westerlund, 2021. "Forecasting using cross-section average–augmented time series regressions," The Econometrics Journal, Royal Economic Society, vol. 24(2), pages 315-333.
    31. Jack Fosten, 2016. "Forecast evaluation with factor-augmented models," University of East Anglia School of Economics Working Paper Series 2016-05, School of Economics, University of East Anglia, Norwich, UK..
    32. Huang, Haitao & Jiang, Lei & Leng, Xuan & Peng, Liang, 2023. "Bootstrap analysis of mutual fund performance," Journal of Econometrics, Elsevier, vol. 235(1), pages 239-255.
    33. Knut Are Aastveit & Claudia Foroni & Francesco Ravazzolo, 2014. "Density forecasts with MIDAS models," Working Paper 2014/10, Norges Bank.
    34. Xingyu Li & Yan Shen & Qiankun Zhou, 2022. "Confidence Intervals of Treatment Effects in Panel Data Models with Interactive Fixed Effects," Papers 2202.12078, arXiv.org.
    35. Yohei Yamamoto & Naoko Hara, 2022. "Identifying factor‐augmented vector autoregression models via changes in shock variances," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 722-745, June.
    36. Antoine A. Djogbenou, 2017. "Model Selection In Factor-augmented Regressions With Estimated Factors," Working Paper 1391, Economics Department, Queen's University.
    37. Bai, Jushan & Han, Xu & Shi, Yutang, 2020. "Estimation and inference of change points in high-dimensional factor models," Journal of Econometrics, Elsevier, vol. 219(1), pages 66-100.
    38. Zongwu Cai & Xiyuan Liu, 2021. "Solving the Price Puzzle Via A Functional Coefficient Factor-Augmented VAR Model," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202106, University of Kansas, Department of Economics, revised Jan 2021.
    39. In Choi & Hanbat Jeong, 2020. "Differencing versus nondifferencing in factor‐based forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(6), pages 728-750, September.
    40. Shintani, Mototsugu & Guo, Zi-Yi, 2011. "Finite Sample Performance of Principal Components Estimators for Dynamic Factor Models: Asymptotic vs. Bootstrap Approximations," EconStor Preprints 167627, ZBW - Leibniz Information Centre for Economics.
    41. Valentina Corradi & Norman Swanson, 2013. "Testing for Structural Stability of Factor Augmented Forecasting Models," Departmental Working Papers 201314, Rutgers University, Department of Economics.
    42. Leif Anders Thorsrud, 2013. "Global and regional business cycles. Shocks and propagations," Working Paper 2013/08, Norges Bank.
    43. Min Seong Kim, 2021. "Robust Inference for Diffusion-Index Forecasts with Cross-Sectionally Dependent Data," Working papers 2021-04, University of Connecticut, Department of Economics.
    44. Javier Maldonado & Esther Ruiz, 2021. "Accurate Confidence Regions for Principal Components Factors," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(6), pages 1432-1453, December.
    45. Antoine A. Djogbenou, 2024. "Identifying oil price shocks with global, developed, and emerging latent real economy activity factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 128-149, January.
    46. Federico Bassetti & Roberto Casarin & Francesco Ravazzolo, 2019. "Density Forecasting," BEMPS - Bozen Economics & Management Paper Series BEMPS59, Faculty of Economics and Management at the Free University of Bozen.

  12. Dovonon, Prosper & Goncalves, Silvia & Meddahi, Nour, 2010. "Bootstrapping realized multivariate volatility measures," MPRA Paper 40123, University Library of Munich, Germany.

    Cited by:

    1. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2011. "Multivariate realised kernels: Consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading," Post-Print hal-00815564, HAL.
    2. BAUWENS, Luc & STORTI, Giuseppe, 2013. "Computationally efficient inference procedures for vast dimensional realized covariance models," LIDAM Reprints CORE 2469, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Fresoli, Diego E. & Ruiz, Esther, 2016. "The uncertainty of conditional returns, volatilities and correlations in DCC models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 170-185.
    4. Hounyo, Ulrich, 2017. "Bootstrapping integrated covariance matrix estimators in noisy jump–diffusion models with non-synchronous trading," Journal of Econometrics, Elsevier, vol. 197(1), pages 130-152.
    5. Michela Verardo & Andrew Patton, 2009. "Does Beta Move with News? Systematic Risk and Firm-Specific Information Flows," FMG Discussion Papers dp630, Financial Markets Group.
    6. H. Peter Boswijk & Giuseppe Cavaliere & Anders Rahbek & Iliyan Georgiev, 2021. "Bootstrapping Non-Stationary Stochastic Volatility," Papers 2101.03562, arXiv.org.
    7. Dovonon, Prosper & Taamouti, Abderrahim & Williams, Julian, 2022. "Testing the eigenvalue structure of spot and integrated covariance," Journal of Econometrics, Elsevier, vol. 229(2), pages 363-395.
    8. Liu, Li & Bu, Ruijun & Pan, Zhiyuan & Xu, Yuhua, 2019. "Are financial returns really predictable out-of-sample?: Evidence from a new bootstrap test," Economic Modelling, Elsevier, vol. 81(C), pages 124-135.
    9. Peter R. Hansen & Asger Lunde & Valeri Voev, 2010. "Realized Beta GARCH: A Multivariate GARCH Model with Realized Measures of Volatility and CoVolatility," CREATES Research Papers 2010-74, Department of Economics and Business Economics, Aarhus University.
    10. Matteo Bonato & Luca Taschini, 2016. "Comovement and the financialization of commodities," GRI Working Papers 215, Grantham Research Institute on Climate Change and the Environment.
    11. Ulrich Hounyo, 2014. "Bootstrapping integrated covariance matrix estimators in noisy jump-diffusion models with non-synchronous trading," CREATES Research Papers 2014-35, Department of Economics and Business Economics, Aarhus University.
    12. Djellout, Hacène & Guillin, Arnaud & Samoura, Yacouba, 2017. "Estimation of the realized (co-)volatility vector: Large deviations approach," Stochastic Processes and their Applications, Elsevier, vol. 127(9), pages 2926-2960.
    13. Mykland, Per A. & Zhang, Lan, 2016. "Between data cleaning and inference: Pre-averaging and robust estimators of the efficient price," Journal of Econometrics, Elsevier, vol. 194(2), pages 242-262.
    14. Camponovo, Lorenzo & Matsushita, Yukitoshi & Otsu, Taisuke, 2019. "Empirical likelihood for high frequency data," LSE Research Online Documents on Economics 100320, London School of Economics and Political Science, LSE Library.
    15. Li, Jia & Todorov, Viktor & Tauchen, George & Chen, Rui, 2017. "Mixed-scale jump regressions with bootstrap inference," Journal of Econometrics, Elsevier, vol. 201(2), pages 417-432.
    16. Golosnoy, Vasyl & Schmid, Wolfgang & Seifert, Miriam Isabel & Lazariv, Taras, 2020. "Statistical inferences for realized portfolio weights," Econometrics and Statistics, Elsevier, vol. 14(C), pages 49-62.
    17. Hwang, Eunju & Shin, Dong Wan, 2013. "Stationary bootstrapping realized volatility," Statistics & Probability Letters, Elsevier, vol. 83(9), pages 2045-2051.
    18. Vincenzo Candila, 2013. "A Comparison of the Forecasting Performances of Multivariate Volatility Models," Working Papers 3_228, Dipartimento di Scienze Economiche e Statistiche, Università degli Studi di Salerno.
    19. Hacène Djellout & Arnaud Guillin & Yacouba Samoura, 2017. "Large Deviations Of The Realized (Co-)Volatility Vector," Post-Print hal-01082903, HAL.
    20. Ulrich Hounyo, 2013. "Bootstrapping realized volatility and realized beta under a local Gaussianity assumption," CREATES Research Papers 2013-30, Department of Economics and Business Economics, Aarhus University.
    21. Hwang, Eunju & Shin, Dong Wan, 2018. "Two-stage stationary bootstrapping for bivariate average realized volatility matrix under market microstructure noise and asynchronicity," Journal of Econometrics, Elsevier, vol. 202(2), pages 178-195.
    22. Hwang, Eunju & Shin, Dong Wan, 2014. "A bootstrap test for jumps in financial economics," Economics Letters, Elsevier, vol. 125(1), pages 74-78.

  13. Silvia Goncalves & Massimo Guidolin, 2005. "Predictable dynamics in the S&P 500 index options implied volatility surface," Working Papers 2005-010, Federal Reserve Bank of St. Louis.

    Cited by:

    1. Chen, Si & Zhou, Zhen & Li, Shenghong, 2016. "An efficient estimate and forecast of the implied volatility surface: A nonlinear Kalman filter approach," Economic Modelling, Elsevier, vol. 58(C), pages 655-664.
    2. Fearghal Kearney & Han Lin Shang & Lisa Sheenan, 2019. "Implied volatility surface predictability: the case of commodity markets," Papers 1909.11009, arXiv.org.
    3. Chalamandaris, Georgios & Tsekrekos, Andrianos E., 2010. "Predictable dynamics in implied volatility surfaces from OTC currency options," Journal of Banking & Finance, Elsevier, vol. 34(6), pages 1175-1188, June.
    4. Bernales, Alejandro & Guidolin, Massimo, 2014. "Can we forecast the implied volatility surface dynamics of equity options? Predictability and economic value tests," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 326-342.
    5. Xiaolan Jia & Xinfeng Ruan & Jin E. Zhang, 2021. "The implied volatility smirk of commodity options," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(1), pages 72-104, January.
    6. Chunbo Liu & Cheng Zhang & Zhiping Zhou, 2018. "From funding liquidity to market liquidity: Evidence from the index options market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(10), pages 1189-1205, October.
    7. Chen, Ying & Han, Qian & Niu, Linlin, 2018. "Forecasting the Term Structure of Option Implied Volatility: The Power of an Adaptive Method," IRTG 1792 Discussion Papers 2018-046, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    8. Dunis, Christian & Kellard, Neil M. & Snaith, Stuart, 2013. "Forecasting EUR–USD implied volatility: The case of intraday data," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 4943-4957.
    9. George Kapetanios & Michael Neumann & George Skiadopoulos, 2014. "Jumps in Option Prices and Their Determinants: Real-time Evidence from the E-mini S&P 500 Option Market," Working Papers 730, Queen Mary University of London, School of Economics and Finance.
    10. Le, Van & Zurbruegg, Ralf, 2014. "Forecasting option smile dynamics," International Review of Financial Analysis, Elsevier, vol. 35(C), pages 32-45.
    11. Bernales, Alejandro & Guidolin, Massimo, 2015. "Learning to smile: Can rational learning explain predictable dynamics in the implied volatility surface?," Journal of Financial Markets, Elsevier, vol. 26(C), pages 1-37.
    12. Chalamandaris, Georgios & Tsekrekos, Andrianos E., 2011. "How important is the term structure in implied volatility surface modeling? Evidence from foreign exchange options," Journal of International Money and Finance, Elsevier, vol. 30(4), pages 623-640, June.
    13. Goulas, Lambros & Skiadopoulos, George, 2012. "Are freight futures markets efficient? Evidence from IMAREX," International Journal of Forecasting, Elsevier, vol. 28(3), pages 644-659.
    14. Chalamandaris, Georgios & Rompolis, Leonidas S., 2012. "Exploring the role of the realized return distribution in the formation of the implied volatility smile," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 1028-1044.
    15. Helena Chuliá & Hipòlit Torró, 2008. "The economic value of volatility transmission between the stock and bond markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 28(11), pages 1066-1094, November.
    16. Michel van der Wel & Sait R. Ozturk & Dick van Dijk, 2015. "Dynamic Factor Models for the Volatility Surface," CREATES Research Papers 2015-13, Department of Economics and Business Economics, Aarhus University.
    17. Alejandro Bernales & Diether W. Beuermann & Gonzalo Cortazar, 2014. "Thinly traded securities and risk management," Estudios de Economia, University of Chile, Department of Economics, vol. 41(1 Year 20), pages 5-48, June.
    18. Konstantinidi, Eirini & Skiadopoulos, George, 2016. "How does the market variance risk premium vary over time? Evidence from S&P 500 variance swap investment returns," Journal of Banking & Finance, Elsevier, vol. 62(C), pages 62-75.
    19. Shang, Han Lin & Kearney, Fearghal, 2022. "Dynamic functional time-series forecasts of foreign exchange implied volatility surfaces," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1025-1049.
    20. Psaradellis, Ioannis & Sermpinis, Georgios, 2016. "Modelling and trading the U.S. implied volatility indices. Evidence from the VIX, VXN and VXD indices," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1268-1283.
    21. Pham, Linh & Do, Hung Xuan, 2022. "Green bonds and implied volatilities: Dynamic causality, spillovers, and implications for portfolio management," Energy Economics, Elsevier, vol. 112(C).
    22. Francesco Audrino & Dominik Colagelo, 2007. "Forecasting Implied Volatility Surfaces," University of St. Gallen Department of Economics working paper series 2007 2007-42, Department of Economics, University of St. Gallen.
    23. Biao Guo & Qian Han & Hai Lin, 2018. "Are there gains from using information over the surface of implied volatilities?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(6), pages 645-672, June.
    24. Shi, Yukun & Stasinakis, Charalampos & Xu, Yaofei & Yan, Cheng, 2022. "Market co-movement between credit default swap curves and option volatility surfaces," International Review of Financial Analysis, Elsevier, vol. 82(C).
    25. Zihao Chen & Yuyang Li & Cindy Long Yu, 2024. "Modeling Implied Volatility Surface Using B-Splines with Time-Dependent Coefficients Predicted by Tree-Based Machine Learning Methods," Mathematics, MDPI, vol. 12(7), pages 1-30, April.
    26. Mihir Dash, 2019. "Modeling of implied volatility surfaces of nifty index options," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 6(03), pages 1-11, September.
    27. Quaye, Enoch & Tunaru, Radu, 2022. "The stock implied volatility and the implied dividend volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    28. Le, Van & Zurbruegg, Ralf, 2010. "The role of trading volume in volatility forecasting," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 20(5), pages 533-555, December.
    29. Andrada-Félix, Julián & Fernández-Rodríguez, Fernando & Fuertes, Ana-Maria, 2016. "Combining nearest neighbor predictions and model-based predictions of realized variance: Does it pay?," International Journal of Forecasting, Elsevier, vol. 32(3), pages 695-715.
    30. Cortazar, Gonzalo & Beuermann, Diether & Bernales, Alejandro, 2013. "Risk Management with Thinly Traded Securities: Methodology and Implementation," IDB Publications (Working Papers) 4647, Inter-American Development Bank.
    31. Jiayi Luo & Cindy Long Yu, 2023. "The Application of Symbolic Regression on Identifying Implied Volatility Surface," Mathematics, MDPI, vol. 11(9), pages 1-28, April.
    32. Lim, Kian Guan & Chen, Ying & Yap, Nelson K.L., 2019. "Intraday information from S&P 500 Index futures options," Journal of Financial Markets, Elsevier, vol. 42(C), pages 29-55.
    33. Bernales, Alejandro & Chen, Louisa & Valenzuela, Marcela, 2017. "Learning and forecasts about option returns through the volatility risk premium," Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 312-330.
    34. Hollstein, Fabian & Nguyen, Duc Binh Benno & Prokopczuk, Marcel, 2019. "Asset prices and “the devil(s) you know”," Journal of Banking & Finance, Elsevier, vol. 105(C), pages 20-35.
    35. Wenyong Zhang & Lingfei Li & Gongqiu Zhang, 2021. "A Two-Step Framework for Arbitrage-Free Prediction of the Implied Volatility Surface," Papers 2106.07177, arXiv.org, revised Jan 2022.
    36. Guidolin, Massimo & Wang, Kai, 2023. "The empirical performance of option implied volatility surface-driven optimal portfolios," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    37. Liu, Xialu & Xiao, Han & Chen, Rong, 2016. "Convolutional autoregressive models for functional time series," Journal of Econometrics, Elsevier, vol. 194(2), pages 263-282.
    38. Georgios Chalamandaris & Andrianos Tsekrekos, 2013. "Explanatory Factors and Causality in the Dynamics of Volatility Surfaces Implied from OTC Asian–Pacific Currency Options," Computational Economics, Springer;Society for Computational Economics, vol. 41(3), pages 327-358, March.
    39. Guo, Biao & Han, Qian & Lin, Hai, 2015. "Forecasting the Term Structure of Implied Volatilities," Working Paper Series 20148, Victoria University of Wellington, School of Economics and Finance.
    40. Konstantinidi, Eirini & Skiadopoulos, George & Tzagkaraki, Emilia, 2008. "Can the evolution of implied volatility be forecasted? Evidence from European and US implied volatility indices," Journal of Banking & Finance, Elsevier, vol. 32(11), pages 2401-2411, November.
    41. Pascal François & Rémi Galarneau‐Vincent & Geneviève Gauthier & Frédéric Godin, 2022. "Venturing into uncharted territory: An extensible implied volatility surface model," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(10), pages 1912-1940, October.
    42. Markopoulou, Chryssa & Skintzi, Vasiliki & Refenes, Apostolos, 2016. "On the predictability of model-free implied correlation," International Journal of Forecasting, Elsevier, vol. 32(2), pages 527-547.
    43. Chen, Ding & Guo, Biao & Zhou, Guofu, 2023. "Firm fundamentals and the cross-section of implied volatility shapes," Journal of Financial Markets, Elsevier, vol. 63(C).
    44. Wang, Jinzhong & Chen, Shijiang & Tao, Qizhi & Zhang, Ting, 2017. "Modelling the implied volatility surface based on Shanghai 50ETF options," Economic Modelling, Elsevier, vol. 64(C), pages 295-301.
    45. Yue, Tian & Gehricke, Sebastian A. & Zhang, Jin E. & Pan, Zheyao, 2021. "The implied volatility smirk in the Chinese equity options market," Pacific-Basin Finance Journal, Elsevier, vol. 69(C).
    46. Shengli Chen & Zili Zhang, 2019. "Forecasting Implied Volatility Smile Surface via Deep Learning and Attention Mechanism," Papers 1912.11059, arXiv.org.
    47. Bedendo, Mascia & Hodges, Stewart D., 2009. "The dynamics of the volatility skew: A Kalman filter approach," Journal of Banking & Finance, Elsevier, vol. 33(6), pages 1156-1165, June.
    48. Jia, Xiaolan & Ruan, Xinfeng & Zhang, Jin E., 2023. "Carr and Wu’s (2020) framework in the oil ETF option market," Journal of Commodity Markets, Elsevier, vol. 31(C).

  14. Peter Christoffersen & Silvia Gonçalves, 2004. "Estimation Risk in Financial Risk Management," CIRANO Working Papers 2004s-15, CIRANO.

    Cited by:

    1. Nieto, María Rosa & Ruiz Ortega, Esther, 2008. "Measuring financial risk : comparison of alternative procedures to estimate VaR and ES," DES - Working Papers. Statistics and Econometrics. WS ws087326, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Wagner Piazza Gaglianone & Luiz Renato Lima & Oliver Linton & Daniel R. Smith, 2011. "Evaluating Value-at-Risk Models via Quantile Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 150-160, January.
    3. A. Gabrielsen & P. Zagaglia & A. Kirchner & Z. Liu, 2012. "Forecasting Value-at-Risk with Time-Varying Variance, Skewness and Kurtosis in an Exponential Weighted Moving Average Framework," Papers 1206.1380, arXiv.org.
    4. Hartz, Christoph & Mittnik, Stefan & Paolella, Marc, 2006. "Accurate value-at-risk forecasting based on the normal-GARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2295-2312, December.
    5. Silvia Stanescu & Radu Tunaru, 2013. "Quantifying the uncertainty in VaR and expected shortfall estimates," Chapters, in: Adrian R. Bell & Chris Brooks & Marcel Prokopczuk (ed.), Handbook of Research Methods and Applications in Empirical Finance, chapter 15, pages 357-372, Edward Elgar Publishing.
    6. Hasan Mahmoud & Vian Ahmed & Salwa Beheiry, 2021. "Construction Cash Flow Risk Index," JRFM, MDPI, vol. 14(6), pages 1-17, June.
    7. Genest, Benoit & Cao, Zhili, 2014. "Value-at-Risk in turbulence time," MPRA Paper 62906, University Library of Munich, Germany.
    8. Loriano Mancini & Fabio Trojani, 2011. "Robust Value at Risk Prediction," Journal of Financial Econometrics, Oxford University Press, vol. 9(2), pages 281-313, Spring.
    9. Giuseppe Storti & Luc Bauwens, 2006. "A component GARCH model with time varying weights," Computing in Economics and Finance 2006 388, Society for Computational Economics.
    10. International Monetary Fund, 2014. "Switzerland: Technical Note-Systemic Risk and Contagion Analysis," IMF Staff Country Reports 2014/268, International Monetary Fund.
    11. Hartz, Christoph & Mittnik, Stefan & Paolella, Marc S., 2006. "Accurate Value-at-Risk forecast with the (good old) normal-GARCH model," CFS Working Paper Series 2006/23, Center for Financial Studies (CFS).
    12. Nieto, María Rosa & Ruiz Ortega, Esther, 2010. "Bootstrap prediction intervals for VaR and ES in the context of GARCH models," DES - Working Papers. Statistics and Econometrics. WS ws102814, Universidad Carlos III de Madrid. Departamento de Estadística.
    13. Wasel Shadat, 2011. "On the Nonparametric Tests of Univariate GARCH Regression Models," Economics Discussion Paper Series 1115, Economics, The University of Manchester.
    14. Imola Drigă, 2012. "Financial Risks Analysis For A Commercial Bank In The Romanian Banking System," Annales Universitatis Apulensis Series Oeconomica, Faculty of Sciences, "1 Decembrie 1918" University, Alba Iulia, vol. 1(14), pages 1-14.
    15. Chen, Yi-Hsuan & Tu, Anthony H., 2013. "Estimating hedged portfolio value-at-risk using the conditional copula: An illustration of model risk," International Review of Economics & Finance, Elsevier, vol. 27(C), pages 514-528.
    16. Dannenberg, Henry, 2011. "The Importance of Estimation Uncertainty in a Multi-Rating Class Loan Portfolio," IWH Discussion Papers 11/2011, Halle Institute for Economic Research (IWH).

  15. Silvia Gonçalves & Lutz Kilian, 2003. "Asymptotic and Bootstrap Inference for AR( Infinite ) Processes with Conditional Heteroskedasticity," CIRANO Working Papers 2003s-28, CIRANO.

    Cited by:

    1. Tommaso Proietti & Alessandro Giovannelli, 2017. "A Durbin-Levinson Regularized Estimator of High Dimensional Autocovariance Matrices," CEIS Research Paper 410, Tor Vergata University, CEIS, revised 19 Jul 2017.
    2. Kilian, Lutz & Gonçalves, Sílvia, 2002. "Bootstrapping Autoregressions with Conditional Heteroskedasticity of Unknown Form," Discussion Paper Series 1: Economic Studies 2002,26, Deutsche Bundesbank.
    3. Bauer, Dietmar, 2009. "Estimating ARMAX systems for multivariate time series using the state approach to subspace algorithms," Journal of Multivariate Analysis, Elsevier, vol. 100(3), pages 397-421, March.
    4. Xu, Ke-Li & Phillips, Peter C.B., 2008. "Adaptive estimation of autoregressive models with time-varying variances," Journal of Econometrics, Elsevier, vol. 142(1), pages 265-280, January.
    5. Serguei Zernov & Victoria Zindle-Walsh & John Galbraith, 2006. "Asymptotics For Estimation Of Truncated Infinite-Dimensional Quantile Regressions," Departmental Working Papers 2006-16, McGill University, Department of Economics.
    6. Giuseppe Cavaliere & Anders Rahbek & A. M. Robert Taylor, 2009. "Co-integration rank tests under conditional heteroskedasticity," Discussion Papers 09/02, University of Nottingham, Granger Centre for Time Series Econometrics.

  16. Silvia Gonçalves & Lutz Kilian, 2003. "Bootstrapping Autoregressions with Conditional Heteroskedasticity of Unknown Form," CIRANO Working Papers 2003s-17, CIRANO.

    Cited by:

    1. Mario Alloza, 2017. "Is fiscal policy more effective in uncertain times or during recessions?," Working Papers 1730, Banco de España.
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    277. Ralf Brüggemann & Markus Glaser & Stefan Schaarschmidt & Sandra Stankiewicz, 2014. "The Stock Return - Trading Volume Relationship in European Countries: Evidence from Asymmetric Impulse Responses," Working Paper Series of the Department of Economics, University of Konstanz 2014-24, Department of Economics, University of Konstanz.
    278. Pascal Paul, 2020. "The Time-Varying Effect of Monetary Policy on Asset Prices," The Review of Economics and Statistics, MIT Press, vol. 102(4), pages 690-704, October.
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    281. Rüth, Sebastian K. & Simon, Camilla, 2020. "How Do Income and the Debt Position of Households Propagate Public into Private Spending?," Working Papers 0676, University of Heidelberg, Department of Economics.
    282. Erdenebat Bataa & Denise R.Osborn & Marianne Sensier, 2016. "China's Increasing Global Influence: Changes in International Growth Spillovers," Centre for Growth and Business Cycle Research Discussion Paper Series 221, Economics, The University of Manchester.
    283. Clerides, Sofronis & Krokida, Styliani-Iris & Lambertides, Neophytos & Tsouknidis, Dimitris, 2022. "What matters for consumer sentiment in the euro area? World crude oil price or retail gasoline price?," Energy Economics, Elsevier, vol. 105(C).
    284. Berthold, Brendan, 2023. "The macroeconomic effects of uncertainty and risk aversion shocks," European Economic Review, Elsevier, vol. 154(C).
    285. Herrera, Ana María & Karaki, Mohamad B. & Rangaraju, Sandeep Kumar, 2017. "Where do jobs go when oil prices drop?," Energy Economics, Elsevier, vol. 64(C), pages 469-482.
    286. Herwartz, Helmut & Plödt, Martin, 2016. "The macroeconomic effects of oil price shocks: Evidence from a statistical identification approach," Journal of International Money and Finance, Elsevier, vol. 61(C), pages 30-44.
    287. Efthymios Pavlidis & Ivan Paya & David Peel, 2010. "Further empirical evidence on the consumption-real exchange rate anomaly," Working Papers 447022, Lancaster University Management School, Economics Department.
    288. Lambertides, Neophytos & Savva, Christos S. & Tsouknidis, Dimitris A., 2017. "The effects of oil price shocks on U.S. stock order flow imbalances and stock returns," Journal of International Money and Finance, Elsevier, vol. 74(C), pages 137-146.
    289. Filippo Lechthaler & Lisa Leinert, 2019. "Moody oil: What is driving the crude oil price?," Empirical Economics, Springer, vol. 57(5), pages 1547-1578, November.
    290. Giovanni Angelini & Giuseppe Cavaliere & Luca Fanelli, 2022. "Bootstrap inference and diagnostics in state space models: With applications to dynamic macro models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 3-22, January.
    291. Kilian, Lutz & Kim, Yun Jung, 2009. "Do Local Projections Solve the Bias Problem in Impulse Response Inference?," CEPR Discussion Papers 7266, C.E.P.R. Discussion Papers.
    292. Michele La Rocca & Cira Perna, 2022. "Opening the Black Box: Bootstrapping Sensitivity Measures in Neural Networks for Interpretable Machine Learning," Stats, MDPI, vol. 5(2), pages 1-18, April.
    293. Ben Ammar, Imen & Hellara, Slaheddine, 2021. "Intraday interactions between high-frequency trading and price efficiency," Finance Research Letters, Elsevier, vol. 41(C).
    294. A. Melander & G. Sismanidis & D. Grenouilleau, 2007. "The track record of the Commission's forecasts - an update," European Economy - Economic Papers 2008 - 2015 291, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    295. Kerssenfischer, Mark, 2022. "Information effects of euro area monetary policy," Economics Letters, Elsevier, vol. 216(C).
    296. Etienne, Xiaoli L. & Irwin, Scott H. & Garcia, Philip, 2014. "Bubbles in food commodity markets: Four decades of evidence," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 129-155.
    297. Herwartz, Helmut & Maxand, Simone & Rohloff, Hannes, 2018. "Lean against the wind or float with the storm? Revisiting the monetary policy asset price nexus by means of a novel statistical identification approach," University of Göttingen Working Papers in Economics 354, University of Goettingen, Department of Economics.
    298. Zeina Alsalman, 2021. "Does the source of oil supply shock matter in explaining the behavior of U.S. consumer spending and sentiment?," Empirical Economics, Springer, vol. 61(3), pages 1491-1518, September.
    299. Lin, Jilei & Eck, Daniel J., 2021. "Minimizing post-shock forecasting error through aggregation of outside information," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1710-1727.
    300. Federico Bassetti & Roberto Casarin & Francesco Ravazzolo, 2019. "Density Forecasting," BEMPS - Bozen Economics & Management Paper Series BEMPS59, Faculty of Economics and Management at the Free University of Bozen.
    301. William J. Procasky & Anwen Yin, 2022. "Forecasting high‐yield equity and CDS index returns: Does observed cross‐market informational flow have predictive power?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(8), pages 1466-1490, August.
    302. Helmut Lütkepohl, 2012. "Identifying Structural Vector Autoregressions via Changes in Volatility," Discussion Papers of DIW Berlin 1259, DIW Berlin, German Institute for Economic Research.
    303. Todd E. Clark & Michael W. McCracken, 2013. "Evaluating the accuracy of forecasts from vector autoregressions," Working Papers 2013-010, Federal Reserve Bank of St. Louis.
    304. Adjemian, Michael K. & Janzen, Joseph & Carter, Colin A. & Smith, Aaron, 2014. "Deconstructing Wheat Price Spikes: A Model of Supply and Demand, Financial Speculation, and Commodity Price Comovement," Economic Research Report 167369, United States Department of Agriculture, Economic Research Service.

  17. Silvia Gonçalves & Halbert White, 2002. "Maximum Likelihood and the Bootstrap for Nonlinear Dynamic Models," CIRANO Working Papers 2002s-41, CIRANO.

    Cited by:

    1. Seojeong Lee, 2014. "Asymptotic Refinements of a Misspecification-Robust Bootstrap for GEL Estimators," Discussion Papers 2014-02, School of Economics, The University of New South Wales.
    2. Norman R. Swanson & Nii Ayi Armah, 2011. "Predictive Inference Under Model Misspecification with an Application to Assessing the Marginal Predictive Content of Money for Output," Departmental Working Papers 201103, Rutgers University, Department of Economics.
    3. Barnett, Alina & Mumtaz, Haroon & Theodoridis, Konstantinos, 2014. "Forecasting UK GDP growth and inflation under structural change. A comparison of models with time-varying parameters," International Journal of Forecasting, Elsevier, vol. 30(1), pages 129-143.
    4. Paulo Parente & Richard J. Smith, 2019. "Quasi-maximum likelihood and the kernel block bootstrap for nonlinear dynamic models," CeMMAP working papers CWP60/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Doko Tchatoka, Firmin & Wang, Wenjie, 2021. "Uniform Inference after Pretesting for Exogeneity with Heteroskedastic Data," MPRA Paper 106408, University Library of Munich, Germany.
    6. Dahl Christian M & Iglesias Emma, 2011. "Modeling the Volatility-Return Trade-Off When Volatility May Be Nonstationary," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-32, February.
    7. Kilian, Lutz & Inoue, Atsushi, 2005. "How Useful is Bagging in Forecasting Economic Time Series? A Case Study of US CPI Inflation," CEPR Discussion Papers 5304, C.E.P.R. Discussion Papers.
    8. João Henrique G. Mazzeu & Gloria González-Rivera & Esther Ruiz & Helena Veiga, 2020. "A bootstrap approach for generalized Autocontour testing Implications for VIX forecast densities," Econometric Reviews, Taylor & Francis Journals, vol. 39(10), pages 971-990, November.
    9. Doko Tchatoka, Firmin & Wang, Wenjie, 2021. "Size-corrected Bootstrap Test after Pretesting for Exogeneity with Heteroskedastic or Clustered Data," MPRA Paper 110899, University Library of Munich, Germany.
    10. Neil Shephard & Kevin Sheppard & Robert F. Engle, 2008. "Fitting vast dimensional time-varying covariance models," Economics Series Working Papers 403, University of Oxford, Department of Economics.
    11. Firmin Doko Tchatoka & Wenjie Wang, 2020. "Uniform Inference after Pretesting for Exogeneity," School of Economics and Public Policy Working Papers 2020-05, University of Adelaide, School of Economics and Public Policy.
    12. Bravo, Francesco & Crudu, Federico, 2012. "Efficient bootstrap with weakly dependent processes," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3444-3458.
    13. Arulampalam, Wiji & Corradi, Valentina & Gutknecht, Daniel, 2014. "Modelling Heaped Duration Data: An Application to Neonatal Mortality," IZA Discussion Papers 8493, Institute of Labor Economics (IZA).
    14. Corradi, Valentina & Fernandez, Andres & Swanson, Norman R., 2009. "Information in the Revision Process of Real-Time Datasets," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 455-467.
    15. Stan Hurn & Ralf Becker, 2006. "Testing for nonlinearity in mean in the presence of heteroskedasticity," Stan Hurn Discussion Papers 2006-02, School of Economics and Finance, Queensland University of Technology.
    16. Inoue, Atsushi & Shintani, Mototsugu, 2006. "Bootstrapping GMM estimators for time series," Journal of Econometrics, Elsevier, vol. 133(2), pages 531-555, August.
    17. Valentina Corradi & Norman R. Swanson, 2003. "Bootstrap Specification Tests for Diffusion Processes," Departmental Working Papers 200321, Rutgers University, Department of Economics.
    18. Goncalves, Silvia & White, Halbert, 2004. "Maximum likelihood and the bootstrap for nonlinear dynamic models," Journal of Econometrics, Elsevier, vol. 119(1), pages 199-219, March.
    19. Valentina Corradi & Norman Swanson, 2013. "A Survey of Recent Advances in Forecast Accuracy Comparison Testing, with an Extension to Stochastic Dominance," Departmental Working Papers 201309, Rutgers University, Department of Economics.
    20. Richard G. Anderson & Hailong Qian & Robert H. Rasche, 2006. "Analysis of panel vector error correction models using maximum likelihood, the bootstrap, and canonical-correlation estimators," Working Papers 2006-050, Federal Reserve Bank of St. Louis.
    21. Jason Allen & Allan Gregory & Katsumi Shimotsu, 2008. "Empirical Likelihood Block Bootstrapping," Staff Working Papers 08-18, Bank of Canada.
    22. Corradi, Valentina & Iglesias, Emma M., 2008. "Bootstrap refinements for QML estimators of the GARCH(1,1) parameters," Journal of Econometrics, Elsevier, vol. 144(2), pages 500-510, June.
    23. Kilian, Lutz & Inoue, Atsushi, 2004. "Bagging Time Series Models," CEPR Discussion Papers 4333, C.E.P.R. Discussion Papers.
    24. Wang, Wenjie, 2020. "On the Inconsistency of Nonparametric Bootstraps for the Subvector Anderson-Rubin Test," MPRA Paper 99109, University Library of Munich, Germany.
    25. Valentina Corradi & Norman Swanson, 2004. "Bootstrap Procedures for Recursive Estimation Schemes With Applications to Forecast Model Selection," Departmental Working Papers 200418, Rutgers University, Department of Economics.
    26. James G. MacKinnon, 2006. "Bootstrap Methods In Econometrics," Working Paper 1028, Economics Department, Queen's University.
    27. Armstrong, Timothy B. & Bertanha, Marinho & Hong, Han, 2014. "A fast resample method for parametric and semiparametric models," Journal of Econometrics, Elsevier, vol. 179(2), pages 128-133.
    28. Irving Arturo De Lira Salvatierra & Andrew J. Patton, 2013. "Dynamic Copula Models and High Frequency Data," Working Papers 13-28, Duke University, Department of Economics.
    29. Valentina Corradi & Norman R. Swanson, 2003. "A Test for Comparing Multiple Misspecified Conditional Distributions," Departmental Working Papers 200314, Rutgers University, Department of Economics.
    30. Liu, Li & Bu, Ruijun & Pan, Zhiyuan & Xu, Yuhua, 2019. "Are financial returns really predictable out-of-sample?: Evidence from a new bootstrap test," Economic Modelling, Elsevier, vol. 81(C), pages 124-135.
    31. Gonçalves Mazzeu, Joao Henrique & González-Rivera, Gloria & Ruiz Ortega, Esther & Veiga, Helena, 2016. "A Bootstrap Approach for Generalized Autocontour Testing," DES - Working Papers. Statistics and Econometrics. WS 23457, Universidad Carlos III de Madrid. Departamento de Estadística.
    32. Corradi, Valentina & Swanson, Norman R., 2006. "Predictive density and conditional confidence interval accuracy tests," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 187-228.
    33. Krenar Avdulaj & Jozef Barunik, 2013. "Are benefits from oil - stocks diversification gone? New evidence from a dynamic copula and high frequency data," Papers 1307.5981, arXiv.org, revised Feb 2015.
    34. Valentina Corradi & Norman R. Swanson, 2003. "Evaluation of Dynamic Stochastic General Equilibrium Models Based on Distributional Comparison of Simulated and Historical Data," Departmental Working Papers 200320, Rutgers University, Department of Economics.
    35. Gonçalves, Sílvia & White, Halbert, 2002. "The Bootstrap Of The Mean For Dependent Heterogeneous Arrays," Econometric Theory, Cambridge University Press, vol. 18(6), pages 1367-1384, December.
    36. Paulo M.D.C. Parente & Richard J. Smith, 2018. "Generalised Empirical Likelihood Kernel Block Bootstrapping," Working Papers REM 2018/55, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    37. Seojeong Lee, 2013. "Asymptotic Refinements of a Misspecification-Robust Bootstrap for Generalized Method of Moments Estimators," Discussion Papers 2013-09, School of Economics, The University of New South Wales.
    38. Jin, Sainan & Corradi, Valentina & Swanson, Norman R., 2017. "Robust Forecast Comparison," Econometric Theory, Cambridge University Press, vol. 33(6), pages 1306-1351, December.
    39. Hong, H. & Scaillet, O., 2006. "A fast subsampling method for nonlinear dynamic models," Journal of Econometrics, Elsevier, vol. 133(2), pages 557-578, August.
    40. Bhardwaj, Geetesh & Corradi, Valentina & Swanson, Norman R., 2008. "A Simulation-Based Specification Test for Diffusion Processes," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 176-193, April.
    41. Corradi, Valentina & Swanson, Norman R., 2006. "Bootstrap conditional distribution tests in the presence of dynamic misspecification," Journal of Econometrics, Elsevier, vol. 133(2), pages 779-806, August.
    42. Valentina Corradi & Norman Swanson, 2003. "The Block Bootstrap for Parameter Estimation Error In Recursive Estimation Schemes, With Applications to Predictive Evaluation," Departmental Working Papers 200313, Rutgers University, Department of Economics.
    43. Prosper Dovonon & Silvia Gonçalves, 2014. "Bootstrapping the GMM overidentification test Under first-order underidentification," CIRANO Working Papers 2014s-25, CIRANO.
    44. Jack Fosten & Daniel Gutknecht & Marc-Oliver Pohle, 2023. "Testing Quantile Forecast Optimality," Papers 2302.02747, arXiv.org, revised Oct 2023.
    45. Scaillet, Olivier & Trojani, Fabio & Camponovo, Lorenzo, 2016. "Comments on : Nonparametric Tail Risk, Stock Returns and the Macroeconomy," Working Papers unige:84999, University of Geneva, Geneva School of Economics and Management.
    46. Lavergne, Pascal & Bertail, Patrice, 2020. "Bootstrapping Quasi Likelihood Ratio Tests under Misspecification," TSE Working Papers 20-1102, Toulouse School of Economics (TSE).
    47. Philipp Kruse, 2020. "Can there only be one? – an empirical comparison of four models on social entrepreneurial intention formation," International Entrepreneurship and Management Journal, Springer, vol. 16(2), pages 641-665, June.
    48. Norman R. Swanson & Weiqi Xiong, 2018. "Big data analytics in economics: What have we learned so far, and where should we go from here?," Canadian Journal of Economics, Canadian Economics Association, vol. 51(3), pages 695-746, August.
    49. Andrea Fracasso & Giuseppe Vittucci Marzetti, 2012. "International R&D spillovers, absorptive capacity and relative backwardness: a panel smooth transition regression model," Department of Economics Working Papers 1203, Department of Economics, University of Trento, Italia.
    50. Jack Fosten & Daniel Gutknecht, 2021. "Horizon confidence sets," Empirical Economics, Springer, vol. 61(2), pages 667-692, August.
    51. James G. MacKinnon, 2007. "Bootstrap Hypothesis Testing," Working Paper 1127, Economics Department, Queen's University.
    52. Lorenzo Camponovo & Olivier Scaillet & Fabio Trojani, 2018. "Predictability Hidden by Anomalous Observations," School of Economics Discussion Papers 0418, School of Economics, University of Surrey.
    53. Gutknecht, Daniel, 2011. "Nonclassical Measurement Error in a Nonlinear (Duration) Model," Economic Research Papers 270763, University of Warwick - Department of Economics.
    54. Adrian Pagan & Hashem Pesaran, 2007. "Econometric Analysis of Structural Systems with Permanent and Transitory Shocks. Working paper #7," NCER Working Paper Series 7, National Centre for Econometric Research.
    55. Norman Swanson & Valentina Corradi, 2006. "Nonparametric Bootstrap Procedures for Predictive Inference Based on Recursive Estimation Schemes," Departmental Working Papers 200618, Rutgers University, Department of Economics.
    56. Elena Ivona Dumitrescu & Georgiana-Denisa Banulescu, 2019. "Do High-frequency-based Measures Improve Conditional Covariance Forecasts?," Post-Print hal-03331122, HAL.
    57. Christian M. Dahl & Emma M. Iglesias, 2008. "The limiting properties of the QMLE in a general class of asymmetric volatility models," CREATES Research Papers 2008-38, Department of Economics and Business Economics, Aarhus University.
    58. Jansen, Dennis W. & Li, Qi & Wang, Zijun & Yang, Jian, 2008. "Fiscal policy and asset markets: A semiparametric analysis," Journal of Econometrics, Elsevier, vol. 147(1), pages 141-150, November.
    59. Xiaohong Chen & Yanqin Fan, 2004. "A Model Selection Test for Bivariate Failure-Time Data," Vanderbilt University Department of Economics Working Papers 0421, Vanderbilt University Department of Economics, revised Oct 2004.
    60. Peter Malec, 2016. "A Semiparametric Intraday GARCH Model," Cambridge Working Papers in Economics 1633, Faculty of Economics, University of Cambridge.
    61. Valentina Corradi & Norman Swanson, 2013. "Testing for Structural Stability of Factor Augmented Forecasting Models," Departmental Working Papers 201314, Rutgers University, Department of Economics.
    62. Patton, Andrew J., 2012. "A review of copula models for economic time series," Journal of Multivariate Analysis, Elsevier, vol. 110(C), pages 4-18.
    63. Gatfaoui, Hayette, 2013. "Translating financial integration into correlation risk: A weekly reporting's viewpoint for the volatility behavior of stock markets," Economic Modelling, Elsevier, vol. 30(C), pages 776-791.
    64. Patton, Andrew, 2013. "Copula Methods for Forecasting Multivariate Time Series," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 899-960, Elsevier.

  18. Silvia Gonçalves & Halbert White, 2001. "The Bootstrap of the Mean for Dependent Heterogeneous Arrays," CIRANO Working Papers 2001s-19, CIRANO.

    Cited by:

    1. Ulrich Hounyo & Sílvia Goncalves & Nour Meddahi, 2013. "Bootstrapping pre-averaged realized volatility under market microstructure noise," CREATES Research Papers 2013-28, Department of Economics and Business Economics, Aarhus University.
    2. Barnett, Alina & Mumtaz, Haroon & Theodoridis, Konstantinos, 2014. "Forecasting UK GDP growth and inflation under structural change. A comparison of models with time-varying parameters," International Journal of Forecasting, Elsevier, vol. 30(1), pages 129-143.
    3. Paulo Parente & Richard J. Smith, 2019. "Quasi-maximum likelihood and the kernel block bootstrap for nonlinear dynamic models," CeMMAP working papers CWP60/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Corradi, Valentina & Fernandez, Andres & Swanson, Norman R., 2009. "Information in the Revision Process of Real-Time Datasets," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 455-467.
    5. Stan Hurn & Ralf Becker, 2006. "Testing for nonlinearity in mean in the presence of heteroskedasticity," Stan Hurn Discussion Papers 2006-02, School of Economics and Finance, Queensland University of Technology.
    6. Hounyo, Ulrich, 2017. "Bootstrapping integrated covariance matrix estimators in noisy jump–diffusion models with non-synchronous trading," Journal of Econometrics, Elsevier, vol. 197(1), pages 130-152.
    7. Goncalves, Silvia & de Jong, Robert, 2003. "Consistency of the stationary bootstrap under weak moment conditions," Economics Letters, Elsevier, vol. 81(2), pages 273-278, November.
    8. Goncalves, Silvia & White, Halbert, 2004. "Maximum likelihood and the bootstrap for nonlinear dynamic models," Journal of Econometrics, Elsevier, vol. 119(1), pages 199-219, March.
    9. Jason Allen & Allan Gregory & Katsumi Shimotsu, 2008. "Empirical Likelihood Block Bootstrapping," Staff Working Papers 08-18, Bank of Canada.
    10. Sílvia GONÇALVES & Benoit PERRON, 2018. "Bootstrapping Factor Models With Cross Sectional Dependence," Cahiers de recherche 10-2018, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    11. Choi, Ji-Eun & Shin, Dong Wan, 2019. "Moving block bootstrapping for a CUSUM test for correlation change," Computational Statistics & Data Analysis, Elsevier, vol. 135(C), pages 95-106.
    12. Romano, Joseph P. & Wolf, Michael, 2001. "Improved nonparametric confidence intervals in time series regressions," DES - Working Papers. Statistics and Econometrics. WS ws010201, Universidad Carlos III de Madrid. Departamento de Estadística.
    13. Calhoun, Gray, 2014. "Block Bootstrap Consistency Under Weak Assumptions," Staff General Research Papers Archive 34313, Iowa State University, Department of Economics.
    14. Hwang, Eunju & Shin, Dong Wan, 2012. "Strong consistency of the stationary bootstrap under ψ-weak dependence," Statistics & Probability Letters, Elsevier, vol. 82(3), pages 488-495.
    15. Hill, Jonathan B. & Aguilar, Mike, 2013. "Moment condition tests for heavy tailed time series," Journal of Econometrics, Elsevier, vol. 172(2), pages 255-274.
    16. Valentina Corradi & Norman R. Swanson, 2003. "A Test for Comparing Multiple Misspecified Conditional Distributions," Departmental Working Papers 200314, Rutgers University, Department of Economics.
    17. A. Amendola & V. Candila, 2016. "Evaluation of volatility predictions in a VaR framework," Quantitative Finance, Taylor & Francis Journals, vol. 16(5), pages 695-709, May.
    18. Hounyo, Ulrich & Lahiri, Kajal, 2023. "Estimating the variance of a combined forecast: Bootstrap-based approach," Journal of Econometrics, Elsevier, vol. 232(2), pages 445-468.
    19. Diep Duong & Norman Swanson, 2013. "Density and Conditional Distribution Based Specification Analysis," Departmental Working Papers 201312, Rutgers University, Department of Economics.
    20. Corradi, Valentina & Swanson, Norman R., 2006. "Bootstrap conditional distribution tests in the presence of dynamic misspecification," Journal of Econometrics, Elsevier, vol. 133(2), pages 779-806, August.
    21. Valentina Corradi & Norman Swanson, 2003. "The Block Bootstrap for Parameter Estimation Error In Recursive Estimation Schemes, With Applications to Predictive Evaluation," Departmental Working Papers 200313, Rutgers University, Department of Economics.
    22. Łukasz Lenart, 2016. "Generalized Resampling Scheme With Application to Spectral Density Matrix in Almost Periodically Correlated Class of Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(3), pages 369-404, May.
    23. Wied, Dominik & Weiß, Gregor N.F. & Ziggel, Daniel, 2016. "Evaluating Value-at-Risk forecasts: A new set of multivariate backtests," Journal of Banking & Finance, Elsevier, vol. 72(C), pages 121-132.
    24. Jack Fosten & Daniel Gutknecht & Marc-Oliver Pohle, 2023. "Testing Quantile Forecast Optimality," Papers 2302.02747, arXiv.org, revised Oct 2023.
    25. Jacek Leśkow & Rafał Synowiecki, 2010. "On bootstrapping periodic random arrays with increasing period," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 71(3), pages 253-279, May.
    26. Denis Kojevnikov, 2021. "The Bootstrap for Network Dependent Processes," Papers 2101.12312, arXiv.org.
    27. Ulrich Hounyo, 2014. "Bootstrapping integrated covariance matrix estimators in noisy jump-diffusion models with non-synchronous trading," CREATES Research Papers 2014-35, Department of Economics and Business Economics, Aarhus University.
    28. Li, Jia & Patton, Andrew J., 2018. "Asymptotic inference about predictive accuracy using high frequency data," Journal of Econometrics, Elsevier, vol. 203(2), pages 223-240.
    29. Adrian Pagan & Hashem Pesaran, 2007. "Econometric Analysis of Structural Systems with Permanent and Transitory Shocks. Working paper #7," NCER Working Paper Series 7, National Centre for Econometric Research.
    30. Zacharias Psaradakis & Márian Vávra, 2018. "Bootstrap-Assisted Tests of Symmetry for Dependent Data," Birkbeck Working Papers in Economics and Finance 1806, Birkbeck, Department of Economics, Mathematics & Statistics.
    31. Hwang, Eunju & Shin, Dong Wan, 2013. "Stationary bootstrapping realized volatility," Statistics & Probability Letters, Elsevier, vol. 83(9), pages 2045-2051.
    32. Hwang, Eunju & Shin, Dong Wan, 2018. "Two-stage stationary bootstrapping for bivariate average realized volatility matrix under market microstructure noise and asynchronicity," Journal of Econometrics, Elsevier, vol. 202(2), pages 178-195.
    33. Bakshi, Gurdip & Panayotov, George, 2013. "Predictability of currency carry trades and asset pricing implications," Journal of Financial Economics, Elsevier, vol. 110(1), pages 139-163.
    34. Ulrich Hounyo, 2014. "The wild tapered block bootstrap," CREATES Research Papers 2014-32, Department of Economics and Business Economics, Aarhus University.

Articles

  1. Gonçalves, Sílvia & Herrera, Ana María & Kilian, Lutz & Pesavento, Elena, 2021. "Impulse response analysis for structural dynamic models with nonlinear regressors," Journal of Econometrics, Elsevier, vol. 225(1), pages 107-130.
    See citations under working paper version above.
  2. Gonçalves, Sílvia & Perron, Benoit, 2020. "Bootstrapping factor models with cross sectional dependence," Journal of Econometrics, Elsevier, vol. 218(2), pages 476-495.
    See citations under working paper version above.
  3. Prosper Dovonon & Sílvia Gonçalves & Ulrich Hounyo & Nour Meddahi, 2019. "Bootstrapping High-Frequency Jump Tests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 793-803, April.
    See citations under working paper version above.
  4. Timothy Conley & Silvia Gonçalves & Christian Hansen, 2018. "Inference with Dependent Data in Accounting and Finance Applications," Journal of Accounting Research, Wiley Blackwell, vol. 56(4), pages 1139-1203, September.

    Cited by:

    1. James G. MacKinnon & Morten {O}rregaard Nielsen & Matthew D. Webb, 2023. "Testing for the appropriate level of clustering in linear regression models," Papers 2301.04522, arXiv.org, revised Mar 2023.
    2. Jinhwan Kim & Rodrigo S. Verdi & Benjamin P. Yost, 2020. "Do Firms Strategically Internalize Disclosure Spillovers? Evidence from Cash‐Financed M&As," Journal of Accounting Research, Wiley Blackwell, vol. 58(5), pages 1249-1297, December.
    3. James MacKinnon & Morten Ørregaard Nielsen, 2022. "Cluster-Robust Inference: A Guide to Empirical Practice," CREATES Research Papers 2022-08, Department of Economics and Business Economics, Aarhus University.
    4. Darendeli, Alper & Fiechter, Peter & Hitz, Jörg-Markus & Lehmann, Nico, 2022. "The role of corporate social responsibility (CSR) information in supply-chain contracting: Evidence from the expansion of CSR rating coverage," Journal of Accounting and Economics, Elsevier, vol. 74(2).
    5. Peter Fiechter & Jörg‐Markus Hitz & Nico Lehmann, 2022. "Real Effects of a Widespread CSR Reporting Mandate: Evidence from the European Union's CSR Directive," Journal of Accounting Research, Wiley Blackwell, vol. 60(4), pages 1499-1549, September.
    6. Michael P. Leung, 2023. "Network Cluster‐Robust Inference," Econometrica, Econometric Society, vol. 91(2), pages 641-667, March.
    7. James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2023. "Leverage, influence, and the jackknife in clustered regression models: Reliable inference using summclust," Stata Journal, StataCorp LP, vol. 23(4), pages 942-982, December.
    8. Kris Hardies & Sarowar Hossain & Larelle (Ellie) Chapple, 2021. "Archival research on audit partners: assessing the research field and recommendations for future research," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(3), pages 4209-4256, September.
    9. Miao Liu, 2022. "Assessing Human Information Processing in Lending Decisions: A Machine Learning Approach," Journal of Accounting Research, Wiley Blackwell, vol. 60(2), pages 607-651, May.
    10. James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2022. "Fast and Reliable Jackknife and Bootstrap Methods for Cluster-Robust Inference," Working Paper 1485, Economics Department, Queen's University.
    11. Harakeh, Mostafa & El-Gammal, Walid & Matar, Ghida, 2019. "Female directors, earnings management, and CEO incentive compensation: UK evidence," Research in International Business and Finance, Elsevier, vol. 50(C), pages 153-170.
    12. Raphael Duguay, 2022. "The Economic Consequences of Financial Audit Regulation in the Charitable Sector," Journal of Accounting Research, Wiley Blackwell, vol. 60(4), pages 1463-1498, September.
    13. Cao, Zhangfan & Chen, Steven Xianglong & Harakeh, Mostafa & Lee, Edward, 2022. "Do non-financial factors influence corporate dividend policies? Evidence from business strategy," International Review of Financial Analysis, Elsevier, vol. 82(C).
    14. Nicholas M. Guest, 2021. "The Information Role of the Media in Earnings News," Journal of Accounting Research, Wiley Blackwell, vol. 59(3), pages 1021-1076, June.
    15. Balakrishnan, Karthik & De George, Emmanuel T. & Ertan, Aytekin & Scobie, Hannah, 2021. "Economic consequences of mandatory auditor reporting to bank regulators," Journal of Accounting and Economics, Elsevier, vol. 72(2).
    16. Dane M. Christensen & Hengda Jin & Suhas A. Sridharan & Laura A. Wellman, 2022. "Hedging on the Hill: Does Political Hedging Reduce Firm Risk?," Management Science, INFORMS, vol. 68(6), pages 4356-4379, June.
    17. Cai Yong & Canay Ivan A. & Kim Deborah & Shaikh Azeem M., 2023. "On the Implementation of Approximate Randomization Tests in Linear Models with a Small Number of Clusters," Journal of Econometric Methods, De Gruyter, vol. 12(1), pages 85-103, January.
    18. Frank S. Zhou & Yuqing Zhou, 2020. "The Dog that Did Not Bark: Limited Price Efficiency and Strategic Nondisclosure," Journal of Accounting Research, Wiley Blackwell, vol. 58(1), pages 155-197, March.
    19. Kevin C. W. Chen & Tai‐Yuan Chen & Weifang Han & Hongqi Yuan, 2022. "Auditors Under Fire: The Association Between Audit Errors and the Career Setbacks of Individual Auditors," Journal of Accounting Research, Wiley Blackwell, vol. 60(3), pages 853-900, June.
    20. Harakeh, Mostafa, 2020. "Dividend policy and corporate investment under information shocks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 65(C).
    21. Wessel M Badenhorst & Rieka von Well, 2023. "The Value‐relevance of Fair Value Measurement for Inventories," Australian Accounting Review, CPA Australia, vol. 33(2), pages 135-159, June.

  5. Gonçalves, Sílvia & McCracken, Michael W. & Perron, Benoit, 2017. "Tests of equal accuracy for nested models with estimated factors," Journal of Econometrics, Elsevier, vol. 198(2), pages 231-252.
    See citations under working paper version above.
  6. Hounyo, Ulrich & Gonçalves, Sílvia & Meddahi, Nour, 2017. "Bootstrapping Pre-Averaged Realized Volatility Under Market Microstructure Noise," Econometric Theory, Cambridge University Press, vol. 33(4), pages 791-838, August.
    See citations under working paper version above.
  7. Dovonon, Prosper & Gonçalves, Sílvia, 2017. "Bootstrapping the GMM overidentification test under first-order underidentification," Journal of Econometrics, Elsevier, vol. 201(1), pages 43-71.
    See citations under working paper version above.
  8. Sílvia Gonçalves & Benoit Perron & Antoine Djogbenou, 2017. "Bootstrap Prediction Intervals for Factor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 53-69, January.
    See citations under working paper version above.
  9. Gonçalves, Sílvia & Kaffo, Maximilien, 2015. "Bootstrap inference for linear dynamic panel data models with individual fixed effects," Journal of Econometrics, Elsevier, vol. 186(2), pages 407-426.

    Cited by:

    1. Xiao, Jiaqi & Juodis, Arturas & Karavias, Yiannis & Sarafidis, Vasilis & Ditzen, Jan, 2022. "Improved Tests for Granger Non-Causality in Panel Data," MPRA Paper 114231, University Library of Munich, Germany.
    2. Elizabeth Schroeder, 2016. "Dynamic labor supply adjustment with bias correction," Empirical Economics, Springer, vol. 51(4), pages 1623-1640, December.
    3. Li, Yun & Nie, Dan & Zhao, Xingang & Li, Yanbin, 2017. "Market structure and performance: An empirical study of the Chinese solar cell industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 78-82.
    4. Barreto, Leonardo & Finkelstein Shapiro, Alan & Nuguer, Victoria, 2023. "Domestic barriers to entry and external vulnerability in emerging economies," Journal of Economic Dynamics and Control, Elsevier, vol. 154(C).
    5. Mike G. Tsionas & Subal C. Kumbhakar, 2023. "Proxy variable estimation of productivity and efficiency," Southern Economic Journal, John Wiley & Sons, vol. 89(3), pages 885-923, January.
    6. Ayden Higgins & Koen Jochmans, 2022. "Bootstrap inference for fixed-effect models," Papers 2201.11156, arXiv.org.
    7. Juodis, Artūras & Karabiyik, Hande & Westerlund, Joakim, 2021. "On the robustness of the pooled CCE estimator," Journal of Econometrics, Elsevier, vol. 220(2), pages 325-348.
    8. Chen, Weihao & Cizek, Pavel, 2023. "Bias-Corrected Instrumental Variable Estimation in Linear Dynamic Panel Data Models," Discussion Paper 2023-028, Tilburg University, Center for Economic Research.
    9. Okui, Ryo & Yanagi, Takahide, 2019. "Panel data analysis with heterogeneous dynamics," Journal of Econometrics, Elsevier, vol. 212(2), pages 451-475.
    10. Giuseppe Cavaliere & S'ilvia Gonc{c}alves & Morten {O}rregaard Nielsen & Edoardo Zanelli, 2022. "Bootstrap inference in the presence of bias," Papers 2208.02028, arXiv.org, revised Nov 2023.
    11. Huang, Dashan & Li, Jiangyuan & Wang, Liyao & Zhou, Guofu, 2020. "Time series momentum: Is it there?," Journal of Financial Economics, Elsevier, vol. 135(3), pages 774-794.
    12. Samaresh Bardhan & Rajesh Sharma & Vivekananda Mukherjee, 2019. "Threshold Effect of Bank-specific Determinants of Non-performing Assets: An Application in Indian Banking," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 18(1_suppl), pages 1-34, April.
    13. Khalaf, Lynda & Saunders, Charles J., 2020. "Monte Carlo two-stage indirect inference (2SIF) for autoregressive panels," Journal of Econometrics, Elsevier, vol. 218(2), pages 419-434.
    14. Ryo Okui & Takahide Yanagi, 2018. "Kernel Estimation for Panel Data with Heterogeneous Dynamics," Papers 1802.08825, arXiv.org, revised May 2019.
    15. Lu, Xun & Su, Liangjun, 2023. "Uniform inference in linear panel data models with two-dimensional heterogeneity," Journal of Econometrics, Elsevier, vol. 235(2), pages 694-719.
    16. Chen, Weihao & Cizek, Pavel, 2023. "Bias-Corrected Instrumental Variable Estimation in Linear Dynamic Panel Data Models," Other publications TiSEM 9bf2c16c-522f-4223-8037-c, Tilburg University, School of Economics and Management.
    17. Norkutė, Milda & Westerlund, Joakim, 2019. "The factor analytical method for interactive effects dynamic panel models with moving average errors," Econometrics and Statistics, Elsevier, vol. 11(C), pages 83-104.
    18. Paulo M.M. Rodrigues & Pedro Portugal & Anabela Carneiro, 2021. "The persistence of wages," Working Papers w202112, Banco de Portugal, Economics and Research Department.
    19. De Vos, Ignace & Stauskas, Ovidijus, 2021. "Bootstrap Improved Inference for Factor-Augmented Regressions with CCE," Working Papers 2021:16, Lund University, Department of Economics.
    20. Chihwa Kao & Long Liu & Rui Sun, 2021. "A bias-corrected fixed effects estimator in the dynamic panel data model," Empirical Economics, Springer, vol. 60(1), pages 205-225, January.
    21. Christis Katsouris, 2023. "Optimal Estimation Methodologies for Panel Data Regression Models," Papers 2311.03471, arXiv.org, revised Nov 2023.
    22. Ivan Fernandez-Val & Wayne Yuan Gao & Yuan Liao & Francis Vella, 2022. "Dynamic Heterogeneous Distribution Regression Panel Models, with an Application to Labor Income Processes," Papers 2202.04154, arXiv.org, revised Jan 2023.
    23. Christis Katsouris, 2023. "Bootstrapping Nonstationary Autoregressive Processes with Predictive Regression Models," Papers 2307.14463, arXiv.org.
    24. Shuowen Chen, 2022. "Indirect Inference for Nonlinear Panel Models with Fixed Effects," Papers 2203.10683, arXiv.org, revised Apr 2022.
    25. Arturas Juodis & Yiannis Karavias, 2019. "Partially heterogeneous tests for Granger non-causality in panel data," Bank of Lithuania Working Paper Series 59, Bank of Lithuania.
    26. Antonio F. Galvao & Thomas Parker & Zhijie Xiao, 2021. "Bootstrap inference for panel data quantile regression," Papers 2111.03626, arXiv.org.
    27. Thomas Gemert & Lenard Lieb & Tania Treibich, 2022. "Local fiscal multipliers of different government spending categories," Empirical Economics, Springer, vol. 63(5), pages 2551-2575, November.

  10. Sílvia Gonçalves & Ulrich Hounyo & Nour Meddahi, 2014. "Bootstrap Inference for Pre-averaged Realized Volatility based on Nonoverlapping Returns," Journal of Financial Econometrics, Oxford University Press, vol. 12(4), pages 679-707.
    See citations under working paper version above.
  11. Gonçalves, Sílvia & Perron, Benoit, 2014. "Bootstrapping factor-augmented regression models," Journal of Econometrics, Elsevier, vol. 182(1), pages 156-173.
    See citations under working paper version above.
  12. Dovonon, Prosper & Gonçalves, Sílvia & Meddahi, Nour, 2013. "Bootstrapping realized multivariate volatility measures," Journal of Econometrics, Elsevier, vol. 172(1), pages 49-65.
    See citations under working paper version above.
  13. Gonçalves, Sílvia & Vogelsang, Timothy J., 2011. "Block Bootstrap Hac Robust Tests: The Sophistication Of The Naive Bootstrap," Econometric Theory, Cambridge University Press, vol. 27(4), pages 745-791, August.

    Cited by:

    1. Marian Vavra, 2015. "On a Bootstrap Test for Forecast Evaluations," Working and Discussion Papers WP 5/2015, Research Department, National Bank of Slovakia.
    2. Robin Greenwood & Samuel G. Hanson, 2013. "Issuer Quality and Corporate Bond Returns," The Review of Financial Studies, Society for Financial Studies, vol. 26(6), pages 1483-1525.
    3. Zhang, Xianyang, 2016. "Fixed-smoothing asymptotics in the generalized empirical likelihood estimation framework," Journal of Econometrics, Elsevier, vol. 193(1), pages 123-146.
    4. Timothy Conley & Silvia Gonçalves & Christian Hansen, 2018. "Inference with Dependent Data in Accounting and Finance Applications," Journal of Accounting Research, Wiley Blackwell, vol. 56(4), pages 1139-1203, September.
    5. Hwang, Jungbin & Sun, Yixiao, 2017. "Asymptotic F and t tests in an efficient GMM setting," Journal of Econometrics, Elsevier, vol. 198(2), pages 277-295.
    6. Stephan Smeekes & Joakim Westerlund, 2019. "Robust block bootstrap panel predictability tests," Econometric Reviews, Taylor & Francis Journals, vol. 38(9), pages 1089-1107, October.
    7. Ulrich K. Müller & Mark W. Watson, 2015. "Low-Frequency Econometrics," NBER Working Papers 21564, National Bureau of Economic Research, Inc.
    8. Robert Adamek & Stephan Smeekes & Ines Wilms, 2020. "Lasso Inference for High-Dimensional Time Series," Papers 2007.10952, arXiv.org, revised Sep 2022.
    9. Coroneo, Laura & Iacone, Fabrizio & Paccagnini, Alessia & Santos Monteiro, Paulo, 2023. "Testing the predictive accuracy of COVID-19 forecasts," International Journal of Forecasting, Elsevier, vol. 39(2), pages 606-622.
    10. Ross McKitrick & Timothy Vogelsang, 2011. "Multivariate trend comparisons between autocorrelated climate series with general trend regressors," Working Papers 1109, University of Guelph, Department of Economics and Finance.
    11. Karavias, Yiannis & Symeonides, Spyridon D. & Tzavalis, Elias, 2018. "Higher order expansions for error variance matrix estimates in the Gaussian AR(1) linear regression model," Statistics & Probability Letters, Elsevier, vol. 135(C), pages 54-59.
    12. Laura Coroneo & Fabrizio Iacone, 2020. "Comparing predictive accuracy in small samples using fixed‐smoothing asymptotics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(4), pages 391-409, June.
    13. Xiaoqing Ye & Yixiao Sun, 2018. "Heteroskedasticity- and autocorrelation-robust F and t tests in Stata," Stata Journal, StataCorp LP, vol. 18(4), pages 951-980, December.
    14. Cheol-Keun Cho & Timothy J. Vogelsang, 2016. "Fixed- b Inference for Testing Structural Change in a Time Series Regression," Econometrics, MDPI, vol. 5(1), pages 1-26, December.
    15. Surajit Ray & N. E. Savin, 2008. "The performance of heteroskedasticity and autocorrelation robust tests: a Monte Carlo study with an application to the three-factor Fama-French asset-pricing model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(1), pages 91-109.
    16. James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2020. "Wild Bootstrap and Asymptotic Inference with Multiway Clustering," CREATES Research Papers 2020-06, Department of Economics and Business Economics, Aarhus University.
    17. Kim, Min Seong & Sun, Yixiao, 2011. "Spatial heteroskedasticity and autocorrelation consistent estimation of covariance matrix," Journal of Econometrics, Elsevier, vol. 160(2), pages 349-371, February.
    18. Andrea Fracasso & Giuseppe Vittucci Marzetti, 2012. "International R&D spillovers, absorptive capacity and relative backwardness: a panel smooth transition regression model," Department of Economics Working Papers 1203, Department of Economics, University of Trento, Italia.
    19. Federico Belotti & Alessandro Casini & Leopoldo Catania & Stefano Grassi & Pierre Perron, 2023. "Simultaneous bandwidths determination for DK-HAC estimators and long-run variance estimation in nonparametric settings," Econometric Reviews, Taylor & Francis Journals, vol. 42(3), pages 281-306, February.
    20. Matei Demetrescu & Christoph Hanck & Robinson Kruse, 2016. "Fixed-b Inference in the Presence of Time-Varying Volatility," CREATES Research Papers 2016-01, Department of Economics and Business Economics, Aarhus University.
    21. Rho, Seunghwa & Vogelsang, Timothy J., 2021. "Inference in time series models using smoothed-clustered standard errors," Journal of Econometrics, Elsevier, vol. 224(1), pages 113-133.
    22. Sun, Yixiao, 2013. "Fixed-smoothing Asymptotics in a Two-step GMM Framework," University of California at San Diego, Economics Working Paper Series qt64x4z265, Department of Economics, UC San Diego.
    23. Hwang, Jungbin & Sun, Yixiao, 2018. "Should we go one step further? An accurate comparison of one-step and two-step procedures in a generalized method of moments framework," Journal of Econometrics, Elsevier, vol. 207(2), pages 381-405.
    24. Xianyang Zhang & Xiaofeng Shao, 2016. "On the coverage bound problem of empirical likelihood methods for time series," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(2), pages 395-421, March.

  14. Gonçalves, Sílvia, 2011. "The Moving Blocks Bootstrap For Panel Linear Regression Models With Individual Fixed Effects," Econometric Theory, Cambridge University Press, vol. 27(5), pages 1048-1082, October.

    Cited by:

    1. Eguren-Martin, Fernando & O’Neill, Cian & Sokol, Andrej & Berge, Lukas von dem, 2021. "Capital flows-at-risk: push, pull and the role of policy," Working Paper Series 2538, European Central Bank.
    2. Timothy Conley & Silvia Gonçalves & Christian Hansen, 2018. "Inference with Dependent Data in Accounting and Finance Applications," Journal of Accounting Research, Wiley Blackwell, vol. 56(4), pages 1139-1203, September.
    3. Galvao, Antonio F. & Montes-Rojas, Gabriel & Sosa-Escudero, Walter & Wang, Liang, 2013. "Tests for skewness and kurtosis in the one-way error component model," Journal of Multivariate Analysis, Elsevier, vol. 122(C), pages 35-52.
    4. Andrea Fracasso & Giuseppe Vittucci Marzetti, 2012. "International R&D spillovers, absorptive capacity and relative backwardness: a panel smooth transition regression model," Department of Economics Working Papers 1203, Department of Economics, University of Trento, Italia.
    5. Ying Liao & Cuixia Li & Lei Jiang & Liang Peng, 2021. "Quantifying Diseconomies Of Scale For Mutual Funds," Annals of Economics and Finance, Society for AEF, vol. 22(1), pages 1-24, May.
    6. Hwang, Jungbin & Sun, Yixiao, 2018. "Should we go one step further? An accurate comparison of one-step and two-step procedures in a generalized method of moments framework," Journal of Econometrics, Elsevier, vol. 207(2), pages 381-405.
    7. Chen, Bin & Huang, Liquan, 2018. "Nonparametric testing for smooth structural changes in panel data models," Journal of Econometrics, Elsevier, vol. 202(2), pages 245-267.
    8. Trapani, Lorenzo, 2013. "On bootstrapping panel factor series," Journal of Econometrics, Elsevier, vol. 172(1), pages 127-141.

  15. Gonçalves, Sílvia & Meddahi, Nour, 2011. "Box-Cox transforms for realized volatility," Journal of Econometrics, Elsevier, vol. 160(1), pages 129-144, January.

    Cited by:

    1. Yu-Min Yen, 2013. "Testing Jumps via False Discovery Rate Control," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-15, April.
    2. Anders Eriksson & Daniel P. A. Preve & Jun Yu, 2019. "Forecasting Realized Volatility Using a Nonnegative Semiparametric Model," JRFM, MDPI, vol. 12(3), pages 1-23, August.
    3. Proietti, Tommaso & Lütkepohl, Helmut, 2013. "Does the Box–Cox transformation help in forecasting macroeconomic time series?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 88-99.
    4. Taylor, Nick, 2017. "Realised variance forecasting under Box-Cox transformations," International Journal of Forecasting, Elsevier, vol. 33(4), pages 770-785.
    5. Chevallier, Julien & Benoit, Sevi, 2009. "On the Realized Volatility of the ECX CO2 Emissions 2008 Futures Contract: Distribution, Dynamics and Forecasting," Sustainable Development Papers 55834, Fondazione Eni Enrico Mattei (FEEM).
    6. Lu, Xinjie & Ma, Feng & Wang, Jiqian & Wang, Jianqiong, 2020. "Examining the predictive information of CBOE OVX on China’s oil futures volatility: Evidence from MS-MIDAS models," Energy, Elsevier, vol. 212(C).
    7. Weigand, Roland, 2014. "Matrix Box-Cox Models for Multivariate Realized Volatility," University of Regensburg Working Papers in Business, Economics and Management Information Systems 478, University of Regensburg, Department of Economics.
    8. Chen, Wang & Lu, Xinjie & Wang, Jiqian, 2022. "Modeling and managing stock market volatility using MRS-MIDAS model," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 625-635.
    9. Camponovo, Lorenzo & Matsushita, Yukitoshi & Otsu, Taisuke, 2019. "Empirical likelihood for high frequency data," LSE Research Online Documents on Economics 100320, London School of Economics and Political Science, LSE Library.
    10. Audrino, Francesco & Sigrist, Fabio & Ballinari, Daniele, 2020. "The impact of sentiment and attention measures on stock market volatility," International Journal of Forecasting, Elsevier, vol. 36(2), pages 334-357.
    11. Peter Reinhard Hansen & Guillaume Horel, 2009. "Quadratic Variation by Markov Chains," CREATES Research Papers 2009-13, Department of Economics and Business Economics, Aarhus University.

  16. Sílvia Gonçalves & Nour Meddahi, 2009. "Bootstrapping Realized Volatility," Econometrica, Econometric Society, vol. 77(1), pages 283-306, January.

    Cited by:

    1. Yu-Min Yen, 2013. "Testing Jumps via False Discovery Rate Control," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-15, April.
    2. Hounyo, Ulrich & Varneskov, Rasmus T., 2020. "Inference for local distributions at high sampling frequencies: A bootstrap approach," Journal of Econometrics, Elsevier, vol. 215(1), pages 1-34.
    3. Ulrich Hounyo & Sílvia Goncalves & Nour Meddahi, 2013. "Bootstrapping pre-averaged realized volatility under market microstructure noise," CREATES Research Papers 2013-28, Department of Economics and Business Economics, Aarhus University.
    4. Tim Bollerslev & Jia Li & Leonardo Salim Saker Chaves, 2021. "Generalized Jump Regressions for Local Moments," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(4), pages 1015-1025, October.
    5. Ole E. Barndorff-Nielsen & Neil Shephard, 2005. "Variation, jumps, market frictions and high frequency data in financial econometrics," OFRC Working Papers Series 2005fe08, Oxford Financial Research Centre.
    6. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2008. "Designing Realized Kernels to Measure the ex post Variation of Equity Prices in the Presence of Noise," Econometrica, Econometric Society, vol. 76(6), pages 1481-1536, November.
    7. Anisha Ghosh & Oliver Linton, 2019. "Estimation with Mixed Data Frequencies: A Bias-Correction Approach," CeMMAP working papers CWP65/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Neil Shephard & Ole E. Barndorff-Nielsen & Asger Lunde, 2006. "Subsampling realised kernels," Economics Series Working Papers 278, University of Oxford, Department of Economics.
    9. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2011. "Multivariate realised kernels: Consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading," Post-Print hal-00815564, HAL.
    10. BAUWENS, Luc & STORTI, Giuseppe, 2013. "Computationally efficient inference procedures for vast dimensional realized covariance models," LIDAM Reprints CORE 2469, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    11. Zhang, Lan & Mykland, Per A. & Aït-Sahalia, Yacine, 2011. "Edgeworth expansions for realized volatility and related estimators," Journal of Econometrics, Elsevier, vol. 160(1), pages 190-203, January.
    12. Holger Dette & Vasyl Golosnoy & Janosch Kellermann, 2023. "The effect of intraday periodicity on realized volatility measures," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(3), pages 315-342, April.
    13. Jacod, Jean & Li, Yingying & Mykland, Per A. & Podolskij, Mark & Vetter, Mathias, 2007. "Microstructure noise in the continuous case: the pre-averaging approach," Technical Reports 2007,41, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    14. Dimpfl, Thomas & Schweikert, Karsten, 2023. "Information shares for markets with partially overlapping trading hours," Journal of Banking & Finance, Elsevier, vol. 154(C).
    15. Hounyo, Ulrich, 2017. "Bootstrapping integrated covariance matrix estimators in noisy jump–diffusion models with non-synchronous trading," Journal of Econometrics, Elsevier, vol. 197(1), pages 130-152.
    16. Ulrich Hounyo & Bezirgen Veliyev, 2015. "Validity of Edgeworth expansions for realized volatility estimators," CREATES Research Papers 2015-21, Department of Economics and Business Economics, Aarhus University.
    17. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    18. Tim Bollerslev & Andrew J. Patton & Rogier Quaedvlieg, 2016. "Modeling and Forecasting (Un)Reliable Realized Covariances for More Reliable Financial Decisions," CREATES Research Papers 2016-10, Department of Economics and Business Economics, Aarhus University.
    19. Per A. Mykland & Neil Shephard & Kevin Sheppard, 2012. "Efficient and feasible inference for the components of financial variation using blocked multipower variation," Economics Papers 2012-W02, Economics Group, Nuffield College, University of Oxford.
    20. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2016. "Exploiting the errors: A simple approach for improved volatility forecasting," Journal of Econometrics, Elsevier, vol. 192(1), pages 1-18.
    21. Amir Safari & Detlef Seese, 2010. "Behavior of realized volatility and correlation in exchange markets," International Econometric Review (IER), Econometric Research Association, vol. 2(2), pages 73-96, September.
    22. Ole E. Barndorff-Nielsen & Sven Erik Graversen & Jean Jacod & Neil Shephard, 2005. "Limit theorems for bipower variation in financial econometrics," OFRC Working Papers Series 2005fe09, Oxford Financial Research Centre.
    23. Tim Bollerslev & Jia Li & Andrew J. Patton & Rogier Quaedvlieg, 2020. "Realized Semicovariances," Econometrica, Econometric Society, vol. 88(4), pages 1515-1551, July.
    24. Yuma Uehara, 2023. "Bootstrap method for misspecified ergodic Lévy driven stochastic differential equation models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(4), pages 533-565, August.
    25. Yacine Ait-Sahalia & Jialin Yu, 2009. "High frequency market microstructure noise estimates and liquidity measures," Papers 0906.1444, arXiv.org.
    26. Chevallier, Julien & Benoit, Sevi, 2009. "On the Realized Volatility of the ECX CO2 Emissions 2008 Futures Contract: Distribution, Dynamics and Forecasting," Sustainable Development Papers 55834, Fondazione Eni Enrico Mattei (FEEM).
    27. Amaya, Diego & Christoffersen, Peter & Jacobs, Kris & Vasquez, Aurelio, 2015. "Does realized skewness predict the cross-section of equity returns?," Journal of Financial Economics, Elsevier, vol. 118(1), pages 135-167.
    28. Hounyo, Ulrich & Varneskov, Rasmus T., 2017. "A local stable bootstrap for power variations of pure-jump semimartingales and activity index estimation," Journal of Econometrics, Elsevier, vol. 198(1), pages 10-28.
    29. Shin Kanaya & Taisuke Otsu, 2011. "Large Deviations of Realized Volatility," Cowles Foundation Discussion Papers 1798, Cowles Foundation for Research in Economics, Yale University.
    30. Vortelinos, Dimitrios I., 2010. "The properties of realized correlation: Evidence from the French, German and Greek equity markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 50(3), pages 273-290, August.
    31. Gustavo Fruet Dias & Karsten Schweiker, 2024. "Integrated Variance Estimation for Assets Traded in Multiple Venues," University of East Anglia School of Economics Working Paper Series 2024-04, School of Economics, University of East Anglia, Norwich, UK..
    32. Tim Bollerslev & Jia Li & Yuan Xue, 2018. "Volume, Volatility, and Public News Announcements," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 85(4), pages 2005-2041.
    33. Patrick M. Kline & Andres Santos, 2011. "Higher Order Properties of the Wild Bootstrap Under Misspecification," NBER Working Papers 16793, National Bureau of Economic Research, Inc.
    34. Nath, H. (Mindi) B. & Kim, Jae H. & Brooks, Robert D., 2012. "Realized dual-betas for leading Australian stocks: An evaluation of the estimation methods and the effect of the sampling interval," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 83(C), pages 10-22.
    35. Ait-Sahalia, Yacine & Mykland, Per A. & Zhang, Lan, 2005. "Ultra high frequency volatility estimation with dependent microstructure noise," Discussion Paper Series 1: Economic Studies 2005,30, Deutsche Bundesbank.
    36. Sílvia Gonçalves & Ulrich Hounyo & Nour Meddahi, 2013. "Bootstrap inference for pre-averaged realized volatility based on non-overlapping returns," CREATES Research Papers 2013-07, Department of Economics and Business Economics, Aarhus University.
    37. Mikkel Bennedsen & Ulrich Hounyo & Asger Lunde & Mikko S. Pakkanen, 2016. "The Local Fractional Bootstrap," CREATES Research Papers 2016-15, Department of Economics and Business Economics, Aarhus University.
    38. Mikkel Bennedsen & Ulrich Hounyo & Asger Lunde & Mikko S. Pakkanen, 2016. "The Local Fractional Bootstrap," Papers 1605.00868, arXiv.org, revised Oct 2017.
    39. Lorenzo Camponovo & Yukitoshi Matsushita & Taisuke Otsu, 2018. "Nonparametric Likelihood for Volatility Under High Frequency Data," School of Economics Discussion Papers 0318, School of Economics, University of Surrey.
    40. Dovonon, Prosper & Goncalves, Silvia & Hounyo, Ulrich & Meddahi, Nour, 2017. "Bootstrapping high-frequency jump tests," TSE Working Papers 17-810, Toulouse School of Economics (TSE).
    41. Tim Bollerslev & Jia Li & Zhipeng Liao, 2021. "Fixed‐k inference for volatility," Quantitative Economics, Econometric Society, vol. 12(4), pages 1053-1084, November.
    42. Dovonon, Prosper & Goncalves, Silvia & Meddahi, Nour, 2010. "Bootstrapping realized multivariate volatility measures," MPRA Paper 40123, University Library of Munich, Germany.
    43. Chaker, Selma, 2017. "On high frequency estimation of the frictionless price: The use of observed liquidity variables," Journal of Econometrics, Elsevier, vol. 201(1), pages 127-143.
    44. Ulrich Hounyo, 2014. "Bootstrapping integrated covariance matrix estimators in noisy jump-diffusion models with non-synchronous trading," CREATES Research Papers 2014-35, Department of Economics and Business Economics, Aarhus University.
    45. Djellout, Hacène & Guillin, Arnaud & Samoura, Yacouba, 2017. "Estimation of the realized (co-)volatility vector: Large deviations approach," Stochastic Processes and their Applications, Elsevier, vol. 127(9), pages 2926-2960.
    46. Linton, Oliver & Whang, Yoon-Jae & Yen, Yu-Min, 2016. "A nonparametric test of a strong leverage hypothesis," Journal of Econometrics, Elsevier, vol. 194(1), pages 153-186.
    47. He, Lidan & Liu, Qiang & Liu, Zhi, 2020. "Edgeworth corrections for spot volatility estimator," Statistics & Probability Letters, Elsevier, vol. 164(C).
    48. Jim Griffin & Jia Liu & John M. Maheu, 2021. "Bayesian Nonparametric Estimation of Ex Post Variance [Out of Sample Forecasts of Quadratic Variation]," Journal of Financial Econometrics, Oxford University Press, vol. 19(5), pages 823-859.
    49. Shin, Dong Wan & Hwang, Eunju, 2015. "A Lagrangian multiplier test for market microstructure noise with applications to sampling interval determination for realized volatilities," Economics Letters, Elsevier, vol. 129(C), pages 95-99.
    50. Torben G. Andersen & Viktor Todorov, 2009. "Realized Volatility and Multipower Variation," CREATES Research Papers 2009-49, Department of Economics and Business Economics, Aarhus University.
    51. Jean Jacod & Yingying Li & Per A. Mykland & Mark Podolskij & Mathias Vetter, 2007. "Microstructure Noise in the Continuous Case: The Pre-Averaging Approach - JLMPV-9," CREATES Research Papers 2007-43, Department of Economics and Business Economics, Aarhus University.
    52. Mykland, Per A. & Zhang, Lan, 2016. "Between data cleaning and inference: Pre-averaging and robust estimators of the efficient price," Journal of Econometrics, Elsevier, vol. 194(2), pages 242-262.
    53. Gonçalves, Sílvia & Meddahi, Nour, 2011. "Box-Cox transforms for realized volatility," Journal of Econometrics, Elsevier, vol. 160(1), pages 129-144, January.
    54. Camponovo, Lorenzo & Matsushita, Yukitoshi & Otsu, Taisuke, 2019. "Empirical likelihood for high frequency data," LSE Research Online Documents on Economics 100320, London School of Economics and Political Science, LSE Library.
    55. Julien Chevallier & Benoît Sévi, 2009. "On the realized volatility of the ECX CO2 emissions 2008 futures contract: distribution, dynamics and forecasting," Working Papers hal-04140871, HAL.
    56. Li, Jia & Todorov, Viktor & Tauchen, George & Chen, Rui, 2017. "Mixed-scale jump regressions with bootstrap inference," Journal of Econometrics, Elsevier, vol. 201(2), pages 417-432.
    57. Patton, Andrew J., 2011. "Data-based ranking of realised volatility estimators," Journal of Econometrics, Elsevier, vol. 161(2), pages 284-303, April.
    58. Hwang, Eunju & Shin, Dong Wan, 2013. "Stationary bootstrapping realized volatility," Statistics & Probability Letters, Elsevier, vol. 83(9), pages 2045-2051.
    59. Peter Reinhard Hansen & Guillaume Horel, 2009. "Quadratic Variation by Markov Chains," CREATES Research Papers 2009-13, Department of Economics and Business Economics, Aarhus University.
    60. Ulrich Hounyo, 2013. "Bootstrapping realized volatility and realized beta under a local Gaussianity assumption," CREATES Research Papers 2013-30, Department of Economics and Business Economics, Aarhus University.
    61. Hwang, Eunju & Shin, Dong Wan, 2018. "Two-stage stationary bootstrapping for bivariate average realized volatility matrix under market microstructure noise and asynchronicity," Journal of Econometrics, Elsevier, vol. 202(2), pages 178-195.
    62. Hwang, Eunju & Shin, Dong Wan, 2014. "A bootstrap test for jumps in financial economics," Economics Letters, Elsevier, vol. 125(1), pages 74-78.
    63. Kalnina, Ilze, 2011. "Subsampling high frequency data," Journal of Econometrics, Elsevier, vol. 161(2), pages 262-283, April.
    64. Selma Chaker, 2013. "Volatility and Liquidity Costs," Staff Working Papers 13-29, Bank of Canada.

  17. Silvia Goncalves & Nour Meddahi, 2008. "Edgeworth Corrections for Realized Volatility," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 139-162.

    Cited by:

    1. Zhang, Lan & Mykland, Per A. & Aït-Sahalia, Yacine, 2011. "Edgeworth expansions for realized volatility and related estimators," Journal of Econometrics, Elsevier, vol. 160(1), pages 190-203, January.
    2. Ulrich Hounyo & Bezirgen Veliyev, 2015. "Validity of Edgeworth expansions for realized volatility estimators," CREATES Research Papers 2015-21, Department of Economics and Business Economics, Aarhus University.
    3. Dovonon, Prosper & Goncalves, Silvia & Meddahi, Nour, 2010. "Bootstrapping realized multivariate volatility measures," MPRA Paper 40123, University Library of Munich, Germany.
    4. He, Lidan & Liu, Qiang & Liu, Zhi, 2020. "Edgeworth corrections for spot volatility estimator," Statistics & Probability Letters, Elsevier, vol. 164(C).
    5. Camponovo, Lorenzo & Matsushita, Yukitoshi & Otsu, Taisuke, 2019. "Empirical likelihood for high frequency data," LSE Research Online Documents on Economics 100320, London School of Economics and Political Science, LSE Library.

  18. Silvia Goncalves & Lutz Kilian, 2007. "Asymptotic and Bootstrap Inference for AR(∞) Processes with Conditional Heteroskedasticity," Econometric Reviews, Taylor & Francis Journals, vol. 26(6), pages 609-641.

    Cited by:

    1. Giuseppe Cavaliere & A. M. Robert Taylor, 2009. "Bootstrap M Unit Root Tests," Econometric Reviews, Taylor & Francis Journals, vol. 28(5), pages 393-421.
    2. Philip Preuss & Ruprecht Puchstein & Holger Dette, 2015. "Detection of Multiple Structural Breaks in Multivariate Time Series," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 654-668, June.
    3. Andrews, Donald W.K. & Guggenberger, Patrik, 2012. "Asymptotics for LS, GLS, and feasible GLS statistics in an AR(1) model with conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 169(2), pages 196-210.
    4. Demetrescu Matei, 2009. "Panel Unit Root Testing with Nonlinear Instruments for Infinite-Order Autoregressive Processes," Journal of Time Series Econometrics, De Gruyter, vol. 1(2), pages 1-30, December.
    5. Richard T. Baillie & George Kapetanios & Fotis Papailias, 2017. "Inference for impulse response coefficients from multivariate fractionally integrated processes," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 60-84, March.
    6. Hsiu-Hsin Ko, 2016. "Exchange Rate Predictability in Finite Samples," The Japanese Economic Review, Japanese Economic Association, vol. 67(3), pages 361-378, September.
    7. Tommaso Proietti & Alessandro Giovannelli, 2017. "A Durbin-Levinson Regularized Estimator of High Dimensional Autocovariance Matrices," CEIS Research Paper 410, Tor Vergata University, CEIS, revised 19 Jul 2017.
    8. Marian Vavra, 2015. "On a Bootstrap Test for Forecast Evaluations," Working and Discussion Papers WP 5/2015, Research Department, National Bank of Slovakia.
    9. Cavaliere, Giuseppe & Harvey, David I. & Leybourne, Stephen J. & Taylor, A.M. Robert, 2011. "Testing For Unit Roots In The Presence Of A Possible Break In Trend And Nonstationary Volatility," Econometric Theory, Cambridge University Press, vol. 27(5), pages 957-991, October.
    10. Atsushi Inoue & Òscar Jordà & Guido M. Kuersteiner, 2023. "Significance Bands for Local Projections," Working Paper Series 2023-15, Federal Reserve Bank of San Francisco.
    11. Oscar Jorda & Massimiliano Marcellino, 2008. "Path Forecast Evaluation," Working Papers 131, University of California, Davis, Department of Economics.
    12. Giuseppe Cavaliere & Morten Ørregaard Nielsen & A.M. Robert Taylor, 2014. "Bootstrap Score Tests for Fractional Integration in Heteroskedastic ARFIMA Models, with an Application to Price Dynamics in Commodity Spot and Futures Markets," CREATES Research Papers 2014-22, Department of Economics and Business Economics, Aarhus University.
    13. Clark, Todd E. & McCracken, Michael W., 2015. "Nested forecast model comparisons: A new approach to testing equal accuracy," Journal of Econometrics, Elsevier, vol. 186(1), pages 160-177.
    14. Giuseppe Cavaliere & Morten Ørregaard Nielsen & Robert Taylor, 2017. "Quasi-Maximum Likelihood Estimation and Bootstrap Inference in Fractional Time Series Models with Heteroskedasticity of Unknown Form," CREATES Research Papers 2017-02, Department of Economics and Business Economics, Aarhus University.
    15. Xu, Ke-Li, 2012. "Robustifying multivariate trend tests to nonstationary volatility," Journal of Econometrics, Elsevier, vol. 169(2), pages 147-154.
    16. Smeekes, S. & Urbain, J.R.Y.J., 2014. "A multivariate invariance principle for modified wild bootstrap methods with an application to unit root testing," Research Memorandum 008, Maastricht University, Graduate School of Business and Economics (GSBE).
    17. Lütkepohl, Helmut & Schlaak, Thore, 2019. "Bootstrapping impulse responses of structural vector autoregressive models identified through GARCH," Journal of Economic Dynamics and Control, Elsevier, vol. 101(C), pages 41-61.
    18. Quentin Giai Gianetto & Hamdi Raïssi, 2015. "Testing Instantaneous Causality in Presence of Nonconstant Unconditional Covariance," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 46-53, January.
    19. Todd E. Clark & Michael W. McCracken, 2009. "In-sample tests of predictive ability: a new approach," Research Working Paper RWP 09-10, Federal Reserve Bank of Kansas City.
    20. Corradi, Valentina & Iglesias, Emma M., 2008. "Bootstrap refinements for QML estimators of the GARCH(1,1) parameters," Journal of Econometrics, Elsevier, vol. 144(2), pages 500-510, June.
    21. Xu, Ke-Li & Phillips, Peter C.B., 2008. "Adaptive estimation of autoregressive models with time-varying variances," Journal of Econometrics, Elsevier, vol. 142(1), pages 265-280, January.
    22. H. Peter Boswijk & Giuseppe Cavaliere & Anders Rahbek & Iliyan Georgiev, 2021. "Bootstrapping Non-Stationary Stochastic Volatility," Papers 2101.03562, arXiv.org.
    23. Hsiu-Hsin Ko, 2016. "Exchange Rate Predictability in Finite Samples," The Japanese Economic Review, Springer, vol. 67(3), pages 361-378, September.
    24. Shimizu Kenichi, 2013. "The bootstrap does not alwayswork for heteroscedasticmodels," Statistics & Risk Modeling, De Gruyter, vol. 30(3), pages 189-204, August.
    25. Psaradakis, Zacharias & Vávra, Marián, 2017. "A distance test of normality for a wide class of stationary processes," Econometrics and Statistics, Elsevier, vol. 2(C), pages 50-60.
    26. Hounyo, Ulrich & Lahiri, Kajal, 2023. "Estimating the variance of a combined forecast: Bootstrap-based approach," Journal of Econometrics, Elsevier, vol. 232(2), pages 445-468.
    27. Giuseppe Cavaliere & Rasmus Søndergaard Pedersen & Anders Rahbek, 2018. "The Fixed Volatility Bootstrap for a Class of Arch(q) Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(6), pages 920-941, November.
    28. Oscar Jorda & Alan Taylor & Sanjay Singh, 2019. "The Long-Run Effects of Monetary Policy," 2019 Meeting Papers 1307, Society for Economic Dynamics.
    29. Brüggemann, Ralf & Jentsch, Carsten & Trenkler, Carsten, 2014. "Inference in VARs with Conditional Heteroskedasticity of Unknown Form," Working Papers 14-21, University of Mannheim, Department of Economics.
    30. Salish, Nazarii & Gleim, Alexander, 2019. "A moment-based notion of time dependence for functional time series," Journal of Econometrics, Elsevier, vol. 212(2), pages 377-392.
    31. Donald W. K. Andrews & Patrik Guggenberger, 2014. "A Conditional-Heteroskedasticity-Robust Confidence Interval for the Autoregressive Parameter," The Review of Economics and Statistics, MIT Press, vol. 96(2), pages 376-381, May.
    32. Nikolay Gospodinov & Ye Tao, 2011. "Bootstrap Unit Root Tests in Models with GARCH(1,1) Errors," Econometric Reviews, Taylor & Francis Journals, vol. 30(4), pages 379-405, August.
    33. José Luis Montiel Olea & Mikkel Plagborg‐Møller, 2021. "Local Projection Inference Is Simpler and More Robust Than You Think," Econometrica, Econometric Society, vol. 89(4), pages 1789-1823, July.
    34. Ron Alquist & Gregory Bauer & Antonio Diez de los Rios, 2014. "What Does the Convenience Yield Curve Tell Us about the Crude Oil Market?," Staff Working Papers 14-42, Bank of Canada.
    35. Lee, Yoon-Jin & Okui, Ryo & Shintani, Mototsugu, 2018. "Asymptotic inference for dynamic panel estimators of infinite order autoregressive processes," Journal of Econometrics, Elsevier, vol. 204(2), pages 147-158.
    36. Guodong Li & Chenlei Leng & Chih-Ling Tsai, 2014. "A Hybrid Bootstrap Approach To Unit Root Tests," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(4), pages 299-321, July.
    37. Zhang, Erhua & Wu, Jilin, 2020. "Adaptive estimation of AR∞ models with time-varying variances," Economics Letters, Elsevier, vol. 197(C).
    38. Daniel Dzikowski & Carsten Jentsch, 2024. "Structural Periodic Vector Autoregressions," Papers 2401.14545, arXiv.org.
    39. Giuseppe Cavaliere & Anders Rahbek & A.M.Robert Taylor, 2009. "Co-integration Rank Testing under Conditional Heteroskedasticity," CREATES Research Papers 2009-22, Department of Economics and Business Economics, Aarhus University.
    40. Bob Nobay & Ivan Paya & David A. Peel, 2010. "Inflation Dynamics in the U.S.: Global but Not Local Mean Reversion," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(1), pages 135-150, February.
    41. Wanbo Lu & Rui Ke, 2019. "A generalized least squares estimation method for the autoregressive conditional duration model," Statistical Papers, Springer, vol. 60(1), pages 123-146, February.
    42. Giuseppe Cavaliere & Anders Rahbek & A. M. Robert Taylor, 2009. "Co-integration rank tests under conditional heteroskedasticity," Discussion Papers 09/02, University of Nottingham, Granger Centre for Time Series Econometrics.
    43. Niklas Ahlgren & Paul Catani, 2017. "Wild bootstrap tests for autocorrelation in vector autoregressive models," Statistical Papers, Springer, vol. 58(4), pages 1189-1216, December.
    44. Kilian, Lutz & Kim, Yun Jung, 2009. "Do Local Projections Solve the Bias Problem in Impulse Response Inference?," CEPR Discussion Papers 7266, C.E.P.R. Discussion Papers.

  19. Sílvia Gonçalves & Massimo Guidolin, 2006. "Predictable Dynamics in the S&P 500 Index Options Implied Volatility Surface," The Journal of Business, University of Chicago Press, vol. 79(3), pages 1591-1636, May.
    See citations under working paper version above.
  20. Goncalves, Silvia & White, Halbert, 2005. "Bootstrap Standard Error Estimates for Linear Regression," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 970-979, September.

    Cited by:

    1. Sandoval, Luis A. & Carpio, Carlos E. & Sanchez, Marcos & Borja, Ivan & Cabrera, Tania, 2017. "The effect of 'traffic-light' nutritional labelling on carbonated soft drink purchases in Ecuador," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 259181, Agricultural and Applied Economics Association.
    2. Sarno, Lucio & Della Corte, Pasquale & Tsiakas, Ilias, 2010. "Spot and Forward Volatility in Foreign Exchange," CEPR Discussion Papers 7893, C.E.P.R. Discussion Papers.
    3. Kul Luintel & Mosahid Khan & Konstantinos Theodoridis, 2014. "On the robustness of R&D," Journal of Productivity Analysis, Springer, vol. 42(2), pages 137-155, October.
    4. Andreas Hagemann, 2017. "Cluster-Robust Bootstrap Inference in Quantile Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 446-456, January.
    5. Matias D. Cattaneo & Michael Jansson & Whitney K. Newey, 2018. "Inference in Linear Regression Models with Many Covariates and Heteroscedasticity," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1350-1361, July.
    6. Della Corte, P. & Sarno, L. & Sestieri, G., 2011. "The Predictive Information Content of External Imbalances for Exchange Rate Returns: How Much Is It Worth?," Working papers 313, Banque de France.
    7. Rangvid, Jesper & Schmeling, Maik & Schrimpf, Andreas, 2014. "Dividend Predictability Around the World," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(5-6), pages 1255-1277, December.
    8. Arulampalam, Wiji & Corradi, Valentina & Gutknecht, Daniel, 2014. "Modelling Heaped Duration Data: An Application to Neonatal Mortality," IZA Discussion Papers 8493, Institute of Labor Economics (IZA).
    9. Augustus J. Panton, 2020. "Climate hysteresis and monetary policy," CAMA Working Papers 2020-76, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    10. Kanittha Tambunlertchai & Sittidaj Pongkijvorasin, 2020. "The impacts of collective threshold requirements for rewards in a CPR experiment," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 22(4), pages 537-554, October.
    11. Lindahl, Therese & Bodin, Örjan & Tengö, Maria, 2015. "Governing complex commons — The role of communication for experimental learning and coordinated management," Ecological Economics, Elsevier, vol. 111(C), pages 111-120.
    12. Bianchi, Mattia & Murtinu, Samuele & Scalera, Vittoria G., 2019. "R&D Subsidies as Dual Signals in Technological Collaborations," Research Policy, Elsevier, vol. 48(9), pages 1-1.
    13. Wang, Wenzhao & Su, Chen & Duxbury, Darren, 2021. "Investor sentiment and stock returns: Global evidence," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 365-391.
    14. Noble, Stephanie M. & Lee, Kang Bok & Zaretzki, Russell & Autry, Chad, 2017. "Coupon clipping by impoverished consumers: Linking demographics, basket size, and coupon redemption rates," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 553-571.
    15. Vieira, Fausto & Fernandes, Marcelo & Chague, Fernando, 2017. "Forecasting the Brazilian yield curve using forward-looking variables," International Journal of Forecasting, Elsevier, vol. 33(1), pages 121-131.
    16. Perez-Laborda, Alejandro & Perez-Sebastian, Fidel, 2020. "Capital-skill complementarity and biased technical change across US sectors," Journal of Macroeconomics, Elsevier, vol. 66(C).
    17. Xiaohong Chen & Jinyong Hahn & Zhipeng Liao, 2012. "Asymptotic Efficiency of Semiparametric Two-step GMM," Cowles Foundation Discussion Papers 1880, Cowles Foundation for Research in Economics, Yale University.
    18. Corredor, Pilar & Ferrer, Elena & Santamaria, Rafael, 2013. "Investor sentiment effect in stock markets: Stock characteristics or country-specific factors?," International Review of Economics & Finance, Elsevier, vol. 27(C), pages 572-591.
    19. Gonçalves, Sílvia & Kaffo, Maximilien, 2015. "Bootstrap inference for linear dynamic panel data models with individual fixed effects," Journal of Econometrics, Elsevier, vol. 186(2), pages 407-426.
    20. Mosahid Khan & Kul B. Luintel & Konstantinos Theodoris, 2010. "How Robust is the R&D – Productivity relationship? Evidence from OECD Countries," WIPO Economic Research Working Papers 01, World Intellectual Property Organization - Economics and Statistics Division, revised Dec 2010.
    21. Qingwei Wang, 2010. "Sentiment, Convergence of Opinion, and Market Crash," Working Papers 10012, Bangor Business School, Prifysgol Bangor University (Cymru / Wales).
    22. James G. MacKinnon, 2006. "Bootstrap Methods In Econometrics," Working Paper 1028, Economics Department, Queen's University.
    23. Buse, Rebekka & Schienle, Melanie & Urban, Jörg, 2022. "Assessing the impact of policy and regulation interventions in European sovereign credit risk networks: What worked best?," Journal of International Economics, Elsevier, vol. 139(C).
    24. Mototsugu Shintani & Zi-yi Guo, 2015. "Improving the Finite Sample Performance of Autoregression Estimators in Dynamic Factor Models: A Bootstrap Approach," Vanderbilt University Department of Economics Working Papers 15-00013, Vanderbilt University Department of Economics.
    25. Hounyo, Ulrich & Lahiri, Kajal, 2023. "Estimating the variance of a combined forecast: Bootstrap-based approach," Journal of Econometrics, Elsevier, vol. 232(2), pages 445-468.
    26. Møller, Stig V., 2014. "GDP growth and the yield curvature," Finance Research Letters, Elsevier, vol. 11(1), pages 1-7.
    27. White, Halbert, 2006. "Time-series estimation of the effects of natural experiments," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 527-566.
    28. Ji-Eun Choi & Dong Wan Shin, 2021. "A self-normalization break test for correlation matrix," Statistical Papers, Springer, vol. 62(5), pages 2333-2353, October.
    29. Schrimpf, Andreas & Wang, Qingwei, 2010. "A reappraisal of the leading indicator properties of the yield curve under structural instability," International Journal of Forecasting, Elsevier, vol. 26(4), pages 836-857, October.
    30. Kato Kengo, 2011. "A note on moment convergence of bootstrap M-estimators," Statistics & Risk Modeling, De Gruyter, vol. 28(1), pages 51-61, March.
    31. Schmeling, Maik, 2009. "Investor sentiment and stock returns: Some international evidence," Journal of Empirical Finance, Elsevier, vol. 16(3), pages 394-408, June.
    32. Grilli, Luca & Murtinu, Samuele, 2018. "Selective subsidies, entrepreneurial founders' human capital, and access to R&D alliances," Research Policy, Elsevier, vol. 47(10), pages 1945-1963.
    33. Johan Blomquist & Joakim Westerlund, 2016. "Panel bootstrap tests of slope homogeneity," Empirical Economics, Springer, vol. 50(4), pages 1359-1381, June.
    34. Jean-Jacques Forneron, 2022. "Estimation and Inference by Stochastic Optimization," Papers 2205.03254, arXiv.org.
    35. Mohammad Mojtahedi & Sidney Newton & Jason Meding, 2017. "Predicting the resilience of transport infrastructure to a natural disaster using Cox’s proportional hazards regression model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 85(2), pages 1119-1133, January.
    36. Li, Jing, 2018. "Essays on model uncertainty in financial models," Other publications TiSEM 202cd910-7ef1-4db4-94ae-d, Tilburg University, School of Economics and Management.
    37. Tambunlertchai, Kanittha & Pongkijvorasin, Sittidaj, 2021. "Regulatory stringency and behavior in a common pool resource game: Lab and field experiments," Journal of Asian Economics, Elsevier, vol. 74(C).
    38. Jinyong Hahn & Zhipeng Liao, 2021. "Bootstrap Standard Error Estimates and Inference," Econometrica, Econometric Society, vol. 89(4), pages 1963-1977, July.
    39. Kobelsky, Kevin W. & Robinson, Michael A., 2010. "The impact of outsourcing on information technology spending," International Journal of Accounting Information Systems, Elsevier, vol. 11(2), pages 105-119.
    40. Ranjani Atukorala & Maxwell L. King & Sivagowry Sriananthakumar, 2014. "Applications of Information Measures to Assess Convergence in the Central Limit Theorem," Monash Econometrics and Business Statistics Working Papers 29/14, Monash University, Department of Econometrics and Business Statistics.
    41. Therese Lindahl & Anne-Sophie Crépin & Caroline Schill, 2016. "Potential Disasters can Turn the Tragedy into Success," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 65(3), pages 657-676, November.
    42. Kristian Skrede Gleditsch & Sara M. T. Polo, 2016. "Ethnic inclusion, democracy, and terrorism," Public Choice, Springer, vol. 169(3), pages 207-229, December.
    43. Baetje, Fabian & Menkhoff, Lukas, 2013. "Macro determinants of U.S. stock market risk premia in bull and bear markets," Hannover Economic Papers (HEP) dp-520, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    44. Gimenez-Nadal, José Ignacio & Lafuente, Miguel & Molina, José Alberto & Velilla, Jorge, 2016. "Resampling and Bootstrap to Assess the Relevance of Variables: A New Algorithmic Approach with Applications to Entrepreneurship Data," IZA Discussion Papers 9938, Institute of Labor Economics (IZA).
    45. Fernandes, Marcelo & Vieira, Fausto, 2019. "A dynamic Nelson–Siegel model with forward-looking macroeconomic factors for the yield curve in the US," Journal of Economic Dynamics and Control, Elsevier, vol. 106(C), pages 1-1.

  21. Goncalves, Silvia & White, Halbert, 2004. "Maximum likelihood and the bootstrap for nonlinear dynamic models," Journal of Econometrics, Elsevier, vol. 119(1), pages 199-219, March.
    See citations under working paper version above.
  22. Goncalves, Silvia & Kilian, Lutz, 2004. "Bootstrapping autoregressions with conditional heteroskedasticity of unknown form," Journal of Econometrics, Elsevier, vol. 123(1), pages 89-120, November.
    See citations under working paper version above.
  23. Goncalves, Silvia & de Jong, Robert, 2003. "Consistency of the stationary bootstrap under weak moment conditions," Economics Letters, Elsevier, vol. 81(2), pages 273-278, November.

    Cited by:

    1. Dehling, Herold & Sharipov, Olimjon Sh. & Wendler, Martin, 2015. "Bootstrap for dependent Hilbert space-valued random variables with application to von Mises statistics," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 200-215.
    2. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    3. Calhoun, Gray, 2014. "Block Bootstrap Consistency Under Weak Assumptions," Staff General Research Papers Archive 34313, Iowa State University, Department of Economics.
    4. Hwang, Eunju & Shin, Dong Wan, 2012. "Strong consistency of the stationary bootstrap under ψ-weak dependence," Statistics & Probability Letters, Elsevier, vol. 82(3), pages 488-495.
    5. Fleming, Jeff & Kirby, Chris & Ostdiek, Barbara, 2006. "Bootstrap tests of multiple inequality restrictions on variance ratios," Economics Letters, Elsevier, vol. 91(3), pages 343-348, June.
    6. A. Amendola & V. Candila, 2016. "Evaluation of volatility predictions in a VaR framework," Quantitative Finance, Taylor & Francis Journals, vol. 16(5), pages 695-709, May.
    7. Prayut Jain & Shashi Jain, 2019. "Can Machine Learning-Based Portfolios Outperform Traditional Risk-Based Portfolios? The Need to Account for Covariance Misspecification," Risks, MDPI, vol. 7(3), pages 1-27, July.
    8. Vikranth Lokeshwar Dhandapani & Shashi Jain, 2023. "Data-driven Approach for Static Hedging of Exchange Traded Options," Papers 2302.00728, arXiv.org, revised Jan 2024.
    9. Li, Jia & Patton, Andrew J., 2018. "Asymptotic inference about predictive accuracy using high frequency data," Journal of Econometrics, Elsevier, vol. 203(2), pages 223-240.
    10. Zacharias Psaradakis & Márian Vávra, 2018. "Bootstrap-Assisted Tests of Symmetry for Dependent Data," Birkbeck Working Papers in Economics and Finance 1806, Birkbeck, Department of Economics, Mathematics & Statistics.
    11. Hwang, Eunju & Shin, Dong Wan, 2013. "Stationary bootstrapping realized volatility," Statistics & Probability Letters, Elsevier, vol. 83(9), pages 2045-2051.
    12. M. Chudý & S. Karmakar & W. B. Wu, 2020. "Long-term prediction intervals of economic time series," Empirical Economics, Springer, vol. 58(1), pages 191-222, January.
    13. Dominik Wied, 2017. "A nonparametric test for a constant correlation matrix," Econometric Reviews, Taylor & Francis Journals, vol. 36(10), pages 1157-1172, November.

  24. Gonçalves, Sílvia & White, Halbert, 2002. "The Bootstrap Of The Mean For Dependent Heterogeneous Arrays," Econometric Theory, Cambridge University Press, vol. 18(6), pages 1367-1384, December.
    See citations under working paper version above.
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