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Stephan Smeekes

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. Robert Adamek & Stephan Smeekes & Ines Wilms, 2020. "Lasso Inference for High-Dimensional Time Series," Papers 2007.10952, arXiv.org, revised Sep 2022.

    Cited by:

    1. Christis Katsouris, 2023. "High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods," Papers 2308.16192, arXiv.org.
    2. Efstathios Polyzos & Costas Siriopoulos, 2024. "Autoregressive Random Forests: Machine Learning and Lag Selection for Financial Research," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 225-262, July.
    3. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2020. "Machine Learning Advances for Time Series Forecasting," Papers 2012.12802, arXiv.org, revised Apr 2021.
    4. ALAMI CHENTOUFI, Reda, 2024. "Penalized Convex Estimation in Dynamic Location-Scale models," MPRA Paper 123283, University Library of Munich, Germany.
    5. Eugene Dettaa & Endong Wang, 2024. "Inference in High-Dimensional Linear Projections: Multi-Horizon Granger Causality and Network Connectedness," Papers 2410.04330, arXiv.org.
    6. Robert Adamek & Stephan Smeekes & Ines Wilms, 2023. "Sparse High-Dimensional Vector Autoregressive Bootstrap," Papers 2302.01233, arXiv.org.
    7. Alain Hecq & Luca Margaritella & Stephan Smeekes, 2023. "Inference in Non-stationary High-Dimensional VARs," Papers 2302.01434, arXiv.org, revised Sep 2023.
    8. Sander Barendse, 2023. "Expected Shortfall LASSO," Papers 2307.01033, arXiv.org, revised Jan 2024.
    9. Zhan Gao & Ji Hyung Lee & Ziwei Mei & Zhentao Shi, 2024. "Econometric Inference for High Dimensional Predictive Regressions," Papers 2409.10030, arXiv.org, revised Nov 2024.
    10. Endong Wang, 2024. "Structural counterfactual analysis in macroeconomics: theory and inference," Papers 2409.09577, arXiv.org.

  2. Stephan Smeekes & Ines Wilms, 2020. "bootUR: An R Package for Bootstrap Unit Root Tests," Papers 2007.12249, arXiv.org, revised Jul 2022.

    Cited by:

    1. Kingstone Nyakurukwa & Yudhvir Seetharam, 2025. "Investor sentiment networks: mapping connectedness in DJIA stocks," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-19, December.

  3. Alain Hecq & Luca Margaritella & Stephan Smeekes, 2019. "Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure," Papers 1902.10991, arXiv.org, revised Dec 2020.

    Cited by:

    1. Miao, Ke & Phillips, Peter C.B. & Su, Liangjun, 2023. "High-dimensional VARs with common factors," Journal of Econometrics, Elsevier, vol. 233(1), pages 155-183.
    2. Gianluca Cubadda & Alain Hecq, 2022. "Dimension Reduction for High Dimensional Vector Autoregressive Models," CEIS Research Paper 534, Tor Vergata University, CEIS, revised 24 Mar 2022.
    3. Lenard Lieb & Adam Jassem & Rui Jorge Almeida & Nalan Bac{s}turk & Stephan Smeekes, 2021. "Min(d)ing the President: A text analytic approach to measuring tax news," Papers 2104.03261, arXiv.org, revised Dec 2024.
    4. Mansour-Ichrakieh, Layal, 2020. "The impact of Israeli Geopolitical Risks on the Lebanese Financial Market: A Destabilizer Multiplier," MPRA Paper 99376, University Library of Munich, Germany.
    5. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2020. "Machine Learning Advances for Time Series Forecasting," Papers 2012.12802, arXiv.org, revised Apr 2021.
    6. Robert Adamek & Stephan Smeekes & Ines Wilms, 2020. "Lasso Inference for High-Dimensional Time Series," Papers 2007.10952, arXiv.org, revised Sep 2022.
    7. Eugene Dettaa & Endong Wang, 2024. "Inference in High-Dimensional Linear Projections: Multi-Horizon Granger Causality and Network Connectedness," Papers 2410.04330, arXiv.org.
    8. Fan, Yanqin & Han, Fang & Park, Hyeonseok, 2023. "Estimation and inference in a high-dimensional semiparametric Gaussian copula vector autoregressive model," Journal of Econometrics, Elsevier, vol. 237(1).
    9. Gianluca Cubadda, 2024. "VAR models with an index structure: A survey with new results," Papers 2412.11278, arXiv.org.
    10. Jonas Krampe & Luca Margaritella, 2021. "Factor Models with Sparse VAR Idiosyncratic Components," Papers 2112.07149, arXiv.org, revised May 2022.
    11. Alain Hecq & Marie Ternes & Ines Wilms, 2021. "Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions," Papers 2102.11780, arXiv.org, revised Mar 2022.

  4. Marina Friedrich & Eric Beutner & Hanno Reuvers & Stephan Smeekes & Jean-Pierre Urbain & Whitney Bader & Bruno Franco & Bernard Lejeune & Emmanuel Mahieu, 2019. "A statistical analysis of time trends in atmospheric ethane," Papers 1903.05403, arXiv.org, revised Jun 2020.

    Cited by:

    1. C. Vladimir Rodr'iguez-Caballero & Esther Ruiz, 2024. "Temperature in the Iberian Peninsula: Trend, seasonality, and heterogeneity," Papers 2406.14145, arXiv.org.
    2. Wenxin Zhang & Xuan Zhang & Bryce W. Edwards & Lei Zhong & Huajian Gao & Michael J. Malaska & Robert Hodyss & Julia R. Greer, 2022. "Deformation characteristics of solid-state benzene as a step towards understanding planetary geology," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    3. González-Rivera, Gloria & Rodríguez Caballero, Carlos Vladimir, 2023. "Modelling intervals of minimum/maximum temperatures in the Iberian Peninsula," DES - Working Papers. Statistics and Econometrics. WS 37968, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Yicong Lin & Mingxuan Song, 2023. "Robust bootstrap inference for linear time-varying coefficient models: Some Monte Carlo evidence," Tinbergen Institute Discussion Papers 23-049/III, Tinbergen Institute.

  5. Stephan Smeekes & Etienne Wijler, 2019. "High-Dimensional Forecasting in the Presence of Unit Roots and Cointegration," Papers 1911.10552, arXiv.org.

    Cited by:

    1. 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.
    2. 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.

  6. Caterina Schiavoni & Franz Palm & Stephan Smeekes & Jan van den Brakel, 2019. "A dynamic factor model approach to incorporate Big Data in state space models for official statistics," Papers 1901.11355, arXiv.org, revised Feb 2020.

    Cited by:

    1. Ovielt Baltodano Lopez & Federico Bassetti & Giulia Carallo & Roberto Casarin, 2022. "First-order integer-valued autoregressive processes with Generalized Katz innovations," Papers 2202.02029, arXiv.org, revised Dec 2024.
    2. Bantis, Evripidis & Clements, Michael P. & Urquhart, Andrew, 2023. "Forecasting GDP growth rates in the United States and Brazil using Google Trends," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1909-1924.
    3. Caterina Schiavoni & Siem Jan Koopman & Franz Palm & Stephan Smeekes & Jan van den Brakel, 2021. "Time-varying state correlations in state space models and their estimation via indirect inference," Tinbergen Institute Discussion Papers 21-020/III, Tinbergen Institute.
    4. Danny Pfeffermann, 2022. "Time series modelling of repeated survey data for estimation of finite population parameters," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1757-1777, October.

  7. Stephan Smeekes & Etienne Wijler, 2018. "An Automated Approach Towards Sparse Single-Equation Cointegration Modelling," Papers 1809.08889, arXiv.org, revised Jul 2020.

    Cited by:

    1. Pitarakis, Jean-Yves, 2025. "Detecting sparse cointegration," UC3M Working papers. Economics 45708, Universidad Carlos III de Madrid. Departamento de Economía.
    2. Ziwei Mei & Zhentao Shi, 2022. "On LASSO for High Dimensional Predictive Regression," Papers 2212.07052, arXiv.org, revised Jan 2024.
    3. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2020. "Machine Learning Advances for Time Series Forecasting," Papers 2012.12802, arXiv.org, revised Apr 2021.
    4. Etienne Wijler, 2022. "A restricted eigenvalue condition for unit-root non-stationary data," Papers 2208.12990, arXiv.org.
    5. Marie Levakova & Susanne Ditlevsen, 2024. "Penalisation Methods in Fitting High‐Dimensional Cointegrated Vector Autoregressive Models: A Review," International Statistical Review, International Statistical Institute, vol. 92(2), pages 160-193, August.
    6. Alain Hecq & Luca Margaritella & Stephan Smeekes, 2023. "Inference in Non-stationary High-Dimensional VARs," Papers 2302.01434, arXiv.org, revised Sep 2023.
    7. Alina Stundziene & Vaida Pilinkiene & Jurgita Bruneckiene & Andrius Grybauskas & Mantas Lukauskas & Irena Pekarskiene, 2024. "Future directions in nowcasting economic activity: A systematic literature review," Journal of Economic Surveys, Wiley Blackwell, vol. 38(4), pages 1199-1233, September.
    8. Simionescu, Mihaela & Cifuentes-Faura, Javier, 2022. "Can unemployment forecasts based on Google Trends help government design better policies? An investigation based on Spain and Portugal," Journal of Policy Modeling, Elsevier, vol. 44(1), pages 1-21.
    9. Yao Li & Yugang He, 2024. "Unraveling Korea’s Energy Challenge: The Consequences of Carbon Dioxide Emissions and Energy Use on Economic Sustainability," Sustainability, MDPI, vol. 16(5), pages 1-29, March.
    10. Mei, Ziwei & Shi, Zhentao, 2024. "On LASSO for high dimensional predictive regression," Journal of Econometrics, Elsevier, vol. 242(2).
    11. Zhan Gao & Ji Hyung Lee & Ziwei Mei & Zhentao Shi, 2024. "Econometric Inference for High Dimensional Predictive Regressions," Papers 2409.10030, arXiv.org, revised Nov 2024.
    12. Zhang, Anan & Zheng, Yadi & Huang, Huang & Ding, Ning & Zhang, Chengqian, 2022. "Co-integration theory-based cluster time-varying load optimization control model of regional integrated energy system," Energy, Elsevier, vol. 260(C).

  8. Eric Beutner & Alexander Heinemann & Stephan Smeekes, 2018. "A Residual Bootstrap for Conditional Value-at-Risk," Papers 1808.09125, arXiv.org, revised Aug 2023.

    Cited by:

    1. Beutner, Eric & Heinemann, Alexander & Smeekes, Stephan, 2024. "A residual bootstrap for conditional Value-at-Risk," Journal of Econometrics, Elsevier, vol. 238(2).
    2. Christian Francq & Jean-Michel Zakoian, 2019. "Virtual Historical Simulation for estimating the conditional VaR of large portfolios," Papers 1909.04661, arXiv.org.
    3. Francq, Christian & Zakoïan, Jean-Michel, 2022. "Testing the existence of moments for GARCH processes," Journal of Econometrics, Elsevier, vol. 227(1), pages 47-64.
    4. Eric Beutner & Julia Schaumburg & Barend Spanjers, 2024. "Bootstrapping GARCH Models Under Dependent Innovations," Tinbergen Institute Discussion Papers 24-008/III, Tinbergen Institute.
    5. Cavaliere, Giuseppe & Nielsen, Heino Bohn & Pedersen, Rasmus Søndergaard & Rahbek, Anders, 2022. "Bootstrap inference on the boundary of the parameter space, with application to conditional volatility models," Journal of Econometrics, Elsevier, vol. 227(1), pages 241-263.
    6. Alexander Heinemann & Sean Telg, 2018. "A Residual Bootstrap for Conditional Expected Shortfall," Papers 1811.11557, arXiv.org.
    7. Royer, Julien, 2021. "Conditional asymmetry in Power ARCH($\infty$) models," MPRA Paper 109118, University Library of Munich, Germany.
    8. Alexander Heinemann, 2019. "A Bootstrap Test for the Existence of Moments for GARCH Processes," Papers 1902.01808, arXiv.org, revised Jul 2019.
    9. Heil, Thomas L.A. & Peter, Franziska J. & Prange, Philipp, 2022. "Measuring 25 years of global equity market co-movement using a time-varying spatial model," Journal of International Money and Finance, Elsevier, vol. 128(C).

  9. Marina Friedrich & Stephan Smeekes & Jean-Pierre Urbain, 2018. "Autoregressive Wild Bootstrap Inference for Nonparametric Trends," Papers 1807.02357, arXiv.org, revised Nov 2019.

    Cited by:

    1. C. Vladimir Rodr'iguez-Caballero & Esther Ruiz, 2024. "Temperature in the Iberian Peninsula: Trend, seasonality, and heterogeneity," Papers 2406.14145, arXiv.org.
    2. Beutner, Eric & Heinemann, Alexander & Smeekes, Stephan, 2024. "A residual bootstrap for conditional Value-at-Risk," Journal of Econometrics, Elsevier, vol. 238(2).
    3. Marina Friedrich & Eric Beutner & Hanno Reuvers & Stephan Smeekes & Jean-Pierre Urbain & Whitney Bader & Bruno Franco & Bernard Lejeune & Emmanuel Mahieu, 2020. "A statistical analysis of time trends in atmospheric ethane," Climatic Change, Springer, vol. 162(1), pages 105-125, September.
    4. Jonathan Benchimol & Luigi Palumbo, 2024. "Sanctions and Russian online prices," Temi di discussione (Economic working papers) 1468, Bank of Italy, Economic Research and International Relations Area.
    5. Friedrich, Marina & Lin, Yicong, 2024. "Sieve bootstrap inference for linear time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 239(1).
    6. Marina Friedrich & Luca Margaritella & Stephan Smeekes, 2023. "High-Dimensional Granger Causality for Climatic Attribution," Papers 2302.03996, arXiv.org, revised Jun 2024.
    7. Yayi Yan & Jiti Gao & Bin peng, 2020. "A Class of Time-Varying Vector Moving Average (infinity) Models," Monash Econometrics and Business Statistics Working Papers 39/20, Monash University, Department of Econometrics and Business Statistics.
    8. González-Rivera, Gloria & Rodríguez Caballero, Carlos Vladimir, 2023. "Modelling intervals of minimum/maximum temperatures in the Iberian Peninsula," DES - Working Papers. Statistics and Econometrics. WS 37968, Universidad Carlos III de Madrid. Departamento de Estadística.
    9. Marina Friedrich & S'ebastien Fries & Michael Pahle & Ottmar Edenhofer, 2019. "Understanding the explosive trend in EU ETS prices -- fundamentals or speculation?," Papers 1906.10572, arXiv.org, revised Mar 2020.
    10. Yayi Yan & Jiti Gao & Bin Peng, 2020. "A Class of Time-Varying Vector Moving Average Models: Nonparametric Kernel Estimation and Application," Papers 2010.01492, arXiv.org.
    11. Marina Friedrich & Sébastien Fries & Michael Pahle & Ottmar Edenhofer, 2020. "Rules vs. Discretion in Cap-and-Trade Programs: Evidence from the EU Emission Trading System," CESifo Working Paper Series 8637, CESifo.
    12. Yicong Lin & Mingxuan Song, 2023. "Robust bootstrap inference for linear time-varying coefficient models: Some Monte Carlo evidence," Tinbergen Institute Discussion Papers 23-049/III, Tinbergen Institute.
    13. Giannerini, Simone & Goracci, Greta & Rahbek, Anders, 2024. "The validity of bootstrap testing for threshold autoregression," Journal of Econometrics, Elsevier, vol. 239(1).
    14. Yayi Yan & Jiti Gao & Bin Peng, 2021. "Asymptotics for Time-Varying Vector MA(∞) Processes," Monash Econometrics and Business Statistics Working Papers 22/21, Monash University, Department of Econometrics and Business Statistics.

  10. Christophe Hurlin & Sébastien Laurent & Rogier Quaedvlieg & Stephan Smeekes, 2017. "Risk Measure Inference," Post-Print hal-01457393, HAL.

    Cited by:

    1. Francq, Christian & Zakoian, Jean-Michel, 2015. "Looking for efficient qml estimation of conditional value-at-risk at multiple risk levels," MPRA Paper 67195, University Library of Munich, Germany.
    2. Christian Francq & Jean-Michel Zakoian, 2019. "Virtual Historical Simulation for estimating the conditional VaR of large portfolios," Papers 1909.04661, arXiv.org.
    3. Hassanniakalager, Arman & Baker, Paul L. & Platanakis, Emmanouil, 2024. "A False Discovery Rate approach to optimal volatility forecasting model selection," International Journal of Forecasting, Elsevier, vol. 40(3), pages 881-902.
    4. Sarlin, Peter & Holopainen, Markus, 2016. "Toward robust early-warning models: a horse race, ensembles and model uncertainty," Working Paper Series 1900, European Central Bank.
    5. Ophélie Couperier & Jérémy Leymarie, 2020. "Backtesting Expected Shortfall via Multi-Quantile Regression," Working Papers halshs-01909375, HAL.
    6. Hurlin, Christophe & Leymarie, Jérémy & Patin, Antoine, 2018. "Loss functions for Loss Given Default model comparison," European Journal of Operational Research, Elsevier, vol. 268(1), pages 348-360.
    7. Michael B. Gordy & Alexander J. McNeil, 2017. "Spectral backtests of forecast distributions with application to risk management," Papers 1708.01489, arXiv.org, revised Jul 2019.
    8. Raphaëlle BELLANDO & Oana TOADER, 2017. "An analysis of banks’ weaknesses in the light of stress tests," LEO Working Papers / DR LEO 2479, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    9. Markus Holopainen & Peter Sarlin, 2015. "Toward robust early-warning models: A horse race, ensembles and model uncertainty," Papers 1501.04682, arXiv.org, revised Apr 2016.
    10. Wided Khiari & Salim Ben Sassi, 2019. "On Identifying the Systemically Important Tunisian Banks: An Empirical Approach Based on the ?CoVaR Measures," Risks, MDPI, vol. 7(4), pages 1-15, December.
    11. Saidane, Dhafer & Sène, Babacar & Désiré Kanga, Kouamé, 2021. "Pan-African banks, banking interconnectivity: A new systemic risk measure in the WAEMU," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).

  11. Eric Beutner & Alexander Heinemann & Stephan Smeekes, 2017. "A Justification of Conditional Confidence Intervals," Papers 1710.00643, arXiv.org, revised Jan 2019.

    Cited by:

    1. Beutner, Eric & Heinemann, Alexander & Smeekes, Stephan, 2024. "A residual bootstrap for conditional Value-at-Risk," Journal of Econometrics, Elsevier, vol. 238(2).
    2. Eric Beutner & Alexander Heinemann & Stephan Smeekes, 2019. "A General Framework for Prediction in Time Series Models," Papers 1902.01622, arXiv.org.
    3. Christian Francq & Jean-Michel Zakoian, 2019. "Virtual Historical Simulation for estimating the conditional VaR of large portfolios," Papers 1909.04661, arXiv.org.
    4. Alexander Heinemann & Sean Telg, 2018. "A Residual Bootstrap for Conditional Expected Shortfall," Papers 1811.11557, arXiv.org.
    5. Beutner, Eric & Heinemann, Alexander & Smeekes, Stephan, 2017. "A Justification of Conditional Confidence Intervals," Research Memorandum 023, Maastricht University, Graduate School of Business and Economics (GSBE).
    6. Loïc Cantin & Christian Francq & Jean-Michel Zakoïan, 2022. "Estimating dynamic systemic risk measures," Working Papers 2022-11, Center for Research in Economics and Statistics.

  12. Smeekes, Stephan & Wijler, Etiënne, 2016. "Macroeconomic Forecasting Using Penalized Regression Methods," Research Memorandum 039, Maastricht University, Graduate School of Business and Economics (GSBE).

    Cited by:

    1. Oxana Babecka Kucharcukova & Jan Bruha, 2016. "Nowcasting the Czech Trade Balance," Working Papers 2016/11, Czech National Bank.
    2. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
    3. Gianluca Cubadda & Alain Hecq, 2022. "Dimension Reduction for High Dimensional Vector Autoregressive Models," CEIS Research Paper 534, Tor Vergata University, CEIS, revised 24 Mar 2022.
    4. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Working Papers 20-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Aug 2020.
    5. Amélie Charles & Olivier Darné, 2022. "Backcasting world trade growth using data reduction methods," The World Economy, Wiley Blackwell, vol. 45(10), pages 3169-3191, October.
    6. Camehl, Annika, 2023. "Penalized estimation of panel vector autoregressive models: A panel LASSO approach," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1185-1204.
    7. Thomas Despois & Catherine Doz, 2021. "Identifying and interpreting the factors in factor models via sparsity: Different approaches," PSE Working Papers halshs-02235543, HAL.
    8. Адилханова Зарина // Adilkhanova Zarina & Ержан Ислам // Yerzhan Islam, 2024. "Система селективно - комбинированного прогноза инфляции (SSCIF)// Selective-Combined Inflation Forecasting System," Working Papers #2024-13, National Bank of Kazakhstan.
    9. Thomas Despois & Catherine Doz, 2022. "Identifying and interpreting the factors in factor models via sparsity : Different approaches," Working Papers halshs-03626503, HAL.
    10. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2020. "Machine Learning Advances for Time Series Forecasting," Papers 2012.12802, arXiv.org, revised Apr 2021.
    11. Thomas Despois & Catherine Doz, 2023. "Identifying and interpreting the factors in factor models via sparsity: Different approaches," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 533-555, June.
    12. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
    13. Smeekes, Stephan & Wijler, Etienne, 2021. "An automated approach towards sparse single-equation cointegration modelling," Journal of Econometrics, Elsevier, vol. 221(1), pages 247-276.
    14. 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.
    15. Wilms, Ines & Rombouts, Jeroen & Croux, Christophe, 2021. "Multivariate volatility forecasts for stock market indices," International Journal of Forecasting, Elsevier, vol. 37(2), pages 484-499.
    16. Li, W. & Fok, D. & Franses, Ph.H.B.F., 2019. "Forecasting own brand sales: Does incorporating competition help?," Econometric Institute Research Papers EI2019-35, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    17. Marie Levakova & Susanne Ditlevsen, 2024. "Penalisation Methods in Fitting High‐Dimensional Cointegrated Vector Autoregressive Models: A Review," International Statistical Review, International Statistical Institute, vol. 92(2), pages 160-193, August.
    18. Jonas Krampe & Luca Margaritella, 2021. "Factor Models with Sparse VAR Idiosyncratic Components," Papers 2112.07149, arXiv.org, revised May 2022.
    19. Lee, Ji Hyung & Shi, Zhentao & Gao, Zhan, 2022. "On LASSO for predictive regression," Journal of Econometrics, Elsevier, vol. 229(2), pages 322-349.
    20. Bantis, Evripidis & Clements, Michael P. & Urquhart, Andrew, 2023. "Forecasting GDP growth rates in the United States and Brazil using Google Trends," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1909-1924.
    21. Alain Hecq & Marie Ternes & Ines Wilms, 2021. "Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions," Papers 2102.11780, arXiv.org, revised Mar 2022.
    22. Jin-Kyu Jung & Manasa Patnam & Anna Ter-Martirosyan, 2018. "An Algorithmic Crystal Ball: Forecasts-based on Machine Learning," IMF Working Papers 2018/230, International Monetary Fund.
    23. Jean Armand Gnagne & Kevin Moran, 2018. "Monitoring Bank Failures in a Data-Rich Environment," Cahiers de recherche 1815, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    24. Renjie Lu & Philip L.H. Yu & Xiaohang Wang, 2020. "Sparse vector error correction models with application to cointegration‐based trading," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 62(3), pages 297-321, September.
    25. Krampe, J. & Paparoditis, E. & Trenkler, C., 2023. "Structural inference in sparse high-dimensional vector autoregressions," Journal of Econometrics, Elsevier, vol. 234(1), pages 276-300.
    26. Ardia, David & Bluteau, Keven & Boudt, Kris, 2019. "Questioning the news about economic growth: Sparse forecasting using thousands of news-based sentiment values," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1370-1386.
    27. Wang, Shixuan & Syntetos, Aris A. & Liu, Ying & Di Cairano-Gilfedder, Carla & Naim, Mohamed M., 2023. "Improving automotive garage operations by categorical forecasts using a large number of variables," European Journal of Operational Research, Elsevier, vol. 306(2), pages 893-908.
    28. Thomas Despois & Catherine Doz, 2022. "Identifying and interpreting the factors in factor models via sparsity : Different approaches," PSE Working Papers halshs-03626503, HAL.
    29. Khan, Faridoon & Muhammadullah, Sara & Sharif, Arshian & Lee, Chien-Chiang, 2024. "The role of green energy stock market in forecasting China's crude oil market: An application of IIS approach and sparse regression models," Energy Economics, Elsevier, vol. 130(C).
    30. Jeronymo Marcondes Pinto & Jennifer L. Castle, 2022. "Machine Learning Dynamic Switching Approach to Forecasting in the Presence of Structural Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 18(2), pages 129-157, July.
    31. Thomas Despois & Catherine Doz, 2021. "Identifying and interpreting the factors in factor models via sparsity: Different approaches," Working Papers halshs-02235543, HAL.
    32. Marijn A. Bolhuis & Brett Rayner, 2020. "Deus ex Machina? A Framework for Macro Forecasting with Machine Learning," IMF Working Papers 2020/045, International Monetary Fund.
    33. Mei, Ziwei & Shi, Zhentao, 2024. "On LASSO for high dimensional predictive regression," Journal of Econometrics, Elsevier, vol. 242(2).
    34. Zhan Gao & Ji Hyung Lee & Ziwei Mei & Zhentao Shi, 2024. "Econometric Inference for High Dimensional Predictive Regressions," Papers 2409.10030, arXiv.org, revised Nov 2024.
    35. Anesti, Nikoleta & Kalamara, Eleni & Kapetanios, George, 2021. "Forecasting UK GDP growth with large survey panels," Bank of England working papers 923, Bank of England.
    36. Yoshiki Nakajima & Naoya Sueishi, 2022. "Forecasting the Japanese macroeconomy using high-dimensional data," The Japanese Economic Review, Springer, vol. 73(2), pages 299-324, April.
    37. Davood Pirayesh Neghab & Mucahit Cevik & M. I. M. Wahab, 2023. "Explaining Exchange Rate Forecasts with Macroeconomic Fundamentals Using Interpretive Machine Learning," Papers 2303.16149, arXiv.org.
    38. Tarassow, Artur, 2019. "Forecasting U.S. money growth using economic uncertainty measures and regularisation techniques," International Journal of Forecasting, Elsevier, vol. 35(2), pages 443-457.
    39. Simone Tonini & Francesca Chiaromonte & Alessandro Giovannelli, 2022. "On the impact of serial dependence on penalized regression methods," LEM Papers Series 2022/21, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.

  13. Götz, T.B. & Hecq, A.W. & Smeekes, S., 2015. "Testing for Granger Causality in Large Mixed-Frequency VARs," Research Memorandum 036, Maastricht University, Graduate School of Business and Economics (GSBE).

    Cited by:

    1. Gianluca Cubadda & Alain Hecq, 2021. "Reduced Rank Regression Models in Economics and Finance," CEIS Research Paper 525, Tor Vergata University, CEIS, revised 08 Nov 2021.
    2. Gianluca Cubadda & Alain Hecq, 2022. "Dimension Reduction for High Dimensional Vector Autoregressive Models," CEIS Research Paper 534, Tor Vergata University, CEIS, revised 24 Mar 2022.
    3. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2020. "Testing a large set of zero restrictions in regression models, with an application to mixed frequency Granger causality," Journal of Econometrics, Elsevier, vol. 218(2), pages 633-654.
    4. Martin Enilov, 2024. "The predictive power of commodity prices for future economic growth: Evaluating the role of economic development," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 3040-3062, July.
    5. Tomás del Barrio Castro & Alain Hecq, 2016. "Testing for Deterministic Seasonality in Mixed-Frequency VARs," DEA Working Papers 76, Universitat de les Illes Balears, Departament d'Economía Aplicada.
    6. Feifei Huang & Mingxia Lin & Shoukat Iqbal Khattak, 2024. "Form Uncertainty to Sustainable Decision-Making: A Novel MIDAS–AM–DeepAR-Based Prediction Model for E-Commerce Industry Development," Sustainability, MDPI, vol. 16(14), pages 1-24, July.
    7. Thomas B. Götz & Alain W. Hecq, 2019. "Granger Causality Testing in Mixed‐Frequency VARs with Possibly (Co)Integrated Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(6), pages 914-935, November.
    8. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
    9. Lynda Khalaf & Maral Kichian & Charles Saunders & Marcel Voia, 2021. "Dynamic panels with MIDAS covariates: Nonlinearity, estimation and fit," Post-Print hal-03528880, HAL.
    10. Bacchiocchi, Emanuele & Bastianin, Andrea & Missale, Alessandro & Rossi, Eduardo, 2020. "Structural analysis with mixed-frequency data: A model of US capital flows," Economic Modelling, Elsevier, vol. 89(C), pages 427-443.
    11. Dudda, Tom L. & Klein, Tony & Nguyen, Duc Khuong & Walther, Thomas, 2022. "Common Drivers of Commodity Futures?," QBS Working Paper Series 2022/05, Queen's University Belfast, Queen's Business School.
    12. Ankargren, Sebastian & Jonéus, Paulina, 2021. "Simulation smoothing for nowcasting with large mixed-frequency VARs," Econometrics and Statistics, Elsevier, vol. 19(C), pages 97-113.
    13. Xinzhou Qi & Wenjing Zhang & Ling Sun & Zijiang Hu & Zhong Ning, 2024. "Navigating the tides of uncertainty: exploring the complex relationship between global economic policy and crude oil transportation," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 26(4), pages 612-629, December.
    14. Alain Hecq & Marie Ternes & Ines Wilms, 2021. "Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions," Papers 2102.11780, arXiv.org, revised Mar 2022.
    15. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2013. "Testing for Granger Causality with Mixed Frequency Data," CEPR Discussion Papers 9655, C.E.P.R. Discussion Papers.
    16. Olatunji Abdul Shobande & Joseph Onuche Enemona, 2021. "A Multivariate VAR Model for Evaluating Sustainable Finance and Natural Resource Curse in West Africa: Evidence from Nigeria and Ghana," Sustainability, MDPI, vol. 13(5), pages 1-15, March.
    17. Andrea Cipollini & Ieva Mikaliunaite, 2021. "Financial distress and real economic activity in Lithuania: a Granger causality test based on mixed-frequency VAR," Empirical Economics, Springer, vol. 61(2), pages 855-881, August.
    18. Alain Hecq & Marie Ternes & Ines Wilms, 2023. "Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions," Papers 2301.10592, arXiv.org, revised Nov 2024.
    19. Lu, Wanbo & Liu, Qibo & Wang, Jie, 2024. "Effect of electricity policy uncertainty and carbon emission prices on electricity demand in China based on mixed-frequency data models," Utilities Policy, Elsevier, vol. 91(C).
    20. Motegi, Kaiji & Sadahiro, Akira, 2018. "Sluggish private investment in Japan’s Lost Decade: Mixed frequency vector autoregression approach," The North American Journal of Economics and Finance, Elsevier, vol. 43(C), pages 118-128.
    21. Giusto Andrea & İşcan Talan B., 2018. "The Rescaled VAR Model with an Application to Mixed-Frequency Macroeconomic Forecasting," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(4), pages 1-16, September.
    22. Chi-Wei Su & Yuru Song & Hsu-Ling Chang & Weike Zhang & Meng Qin, 2023. "Could Cryptocurrency Policy Uncertainty Facilitate U.S. Carbon Neutrality?," Sustainability, MDPI, vol. 15(9), pages 1-15, May.

  14. 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).

    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. 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.
    3. Matei Demetrescu & Christoph Hanck & Robinson Kruse‐Becher, 2022. "Robust inference under time‐varying volatility: A real‐time evaluation of professional forecasters," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 1010-1030, August.
    4. Friedrich, Marina & Smeekes, Stephan & Urbain, Jean-Pierre, 2020. "Autoregressive wild bootstrap inference for nonparametric trends," Journal of Econometrics, Elsevier, vol. 214(1), pages 81-109.
    5. Smeekes, Stephan & Wijler, Etiënne, 2016. "Macroeconomic Forecasting Using Penalized Regression Methods," Research Memorandum 039, Maastricht University, Graduate School of Business and Economics (GSBE).
    6. Helmut Herwartz & Yabibal M. Walle, 2018. "A powerful wild bootstrap diagnosis of panel unit roots under linear trends and time-varying volatility," Computational Statistics, Springer, vol. 33(1), pages 379-411, March.
    7. Li, Muyi & Zhang, Yanfen, 2022. "Bootstrapping multivariate portmanteau tests for vector autoregressive models with weak assumptions on errors," Computational Statistics & Data Analysis, Elsevier, vol. 165(C).
    8. Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Michael Wolf & Dan Wunderli, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 352-376, May.
    9. 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.
    10. Yicong Lin & Mingxuan Song, 2023. "Robust bootstrap inference for linear time-varying coefficient models: Some Monte Carlo evidence," Tinbergen Institute Discussion Papers 23-049/III, Tinbergen Institute.

  15. Westerlund, J. & Smeekes, S., 2013. "Robust block bootstrap panel predictability tests," Research Memorandum 060, Maastricht University, Graduate School of Business and Economics (GSBE).

    Cited by:

    1. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2021. "Interactive R&D Spillovers: an estimation strategy based on forecasting-driven model selection," Working Papers hal-03224910, HAL.
    2. Demetrescu, Matei & Georgiev, Iliyan & Rodrigues, Paulo MM & Taylor, AM Robert, 2022. "Extensions to IVX Methods of Inference for Return Predictability," Essex Finance Centre Working Papers 29779, University of Essex, Essex Business School.
    3. Gioldasis, Georgios & Musolesi, Antonio & Simioni, Michel, 2023. "Interactive R&D spillovers: An estimation strategy based on forecasting-driven model selection," International Journal of Forecasting, Elsevier, vol. 39(1), pages 144-169.
    4. Westerlund, Joakim & Thuraisamy, Kannan, 2016. "Panel multi-predictor test procedures with an application to emerging market sovereign risk," Emerging Markets Review, Elsevier, vol. 28(C), pages 44-60.
    5. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2021. "Interactive R&D Spillovers: An estimation strategy based on forecasting-driven model selection," SEEDS Working Papers 0621, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Jun 2021.

  16. Giuseppe Cavaliere & Peter C.B. Phillips & Stephan Smeekes & A.M. Robert Taylor, 2012. "Lag Length Selection for Unit Root Tests in the Presence of Nonstationary Volatility," Cowles Foundation Discussion Papers 1844, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. GwanSeon Kim & Tyler Mark, 2017. "Impacts of corn price and imported beef price on domestic beef price in South Korea," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 5(1), pages 1-13, December.
    2. Mohitosh Kejriwal & Xuewen Yu & Pierre Perron, 2020. "Bootstrap Procedures for Detecting Multiple Persistence Shifts in Heteroskedastic Time Series," Boston University - Department of Economics - Working Papers Series WP2020-009, Boston University - Department of Economics.
    3. 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).
    4. Giuseppe Cavaliere & Peter C. B. Phillips & Stephan Smeekes & A. M. Robert Taylor, 2015. "Lag Length Selection for Unit Root Tests in the Presence of Nonstationary Volatility," Econometric Reviews, Taylor & Francis Journals, vol. 34(4), pages 512-536, April.
    5. Skrobotov Anton & Cavaliere Giuseppe & Taylor Robert, 2016. "Wild Bootstrap Seasonal Unit Root Tests for Time Series with Periodic Non-Stationary Volatility," Working Papers wpaper-2016-269, Gaidar Institute for Economic Policy, revised 2016.
    6. Jin Guo & Tetsuji Tanaka, 2020. "Examining the determinants of global and local price passthrough in cereal markets: evidence from DCC-GJR-GARCH and panel analyses," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 8(1), pages 1-22, December.
    7. Nguyen, Xuan & Nguyen, Cuong, 2016. "The Impact of Petroleum Retail Price Shocks on Inflation in Vietnam," MPRA Paper 93136, University Library of Munich, Germany.
    8. Martin C. Arnold & Thilo Reinschlussel, 2024. "Bootstrap Adaptive Lasso Solution Path Unit Root Tests," Papers 2409.07859, arXiv.org.
    9. Kung-Sik Chan & Simone Giannerini & Greta Goracci & Howell Tong, 2020. "Testing for threshold regulation in presence of measurement error with an application to the PPP hypothesis," Papers 2002.09968, arXiv.org, revised Nov 2021.
    10. Skrobotov, Anton, 2020. "Survey on structural breaks and unit root tests," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 58, pages 96-141.
    11. Oanea, Dumitru-Cristian, 2015. "Financial markets integration: A vector error-correction approach," The Journal of Economic Asymmetries, Elsevier, vol. 12(2), pages 153-161.
    12. Xu, Bin & Lin, Boqiang, 2016. "Reducing CO2 emissions in China's manufacturing industry: Evidence from nonparametric additive regression models," Energy, Elsevier, vol. 101(C), pages 161-173.

  17. Smeekes, S. & Urbain, J.R.Y.J., 2011. "On the applicability of the sieve bootstrap in time series panels," Research Memorandum 055, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).

    Cited by:

    1. Meyer, Marco & Jentsch, Carsten & Kreiss, Jens-Peter, 2015. "Baxter`s inequality and sieve bootstrap for random fields," Working Papers 15-06, University of Mannheim, Department of Economics.
    2. 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).
    3. Matsuki, Takashi & Sugimoto, Kimiko, 2013. "Stationarity of Asian real exchange rates: An empirical application of multiple testing to nonstationary panels with a structural break," Economic Modelling, Elsevier, vol. 34(C), pages 52-58.
    4. Takashi Matsuki, 2016. "Linear and nonlinear comovement in Southeast Asian local currency bond markets: a stepwise multiple testing approach," Empirical Economics, Springer, vol. 51(2), pages 591-619, September.
    5. Xie, Zixiong & Chen, Shyh-Wei, 2019. "Exchange rates and fundamentals: A bootstrap panel data analysis," Economic Modelling, Elsevier, vol. 78(C), pages 209-224.
    6. Antonia Arsova, 2021. "Exchange rate pass-through to import prices in Europe: a panel cointegration approach," Empirical Economics, Springer, vol. 61(1), pages 61-100, July.
    7. Valeria D’Amato & Steven Haberman & Gabriella Piscopo & Maria Russolillo, 2014. "Computational framework for longevity risk management," Computational Management Science, Springer, vol. 11(1), pages 111-137, January.
    8. Matteo Barigozzi & Giuseppe Cavaliere & Lorenzo Trapani, 2020. "Determining the rank of cointegration with infinite variance," Discussion Papers 20/01, University of Nottingham, Granger Centre for Time Series Econometrics.

  18. Smeekes, S., 2011. "Bootstrap sequential tests to determine the stationary units in a panel," Research Memorandum 003, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).

    Cited by:

    1. Francesca Iorio & Stefano Fachin, 2014. "Savings and investments in the OECD: a panel cointegration study with a new bootstrap test," Empirical Economics, Springer, vol. 46(4), pages 1271-1300, June.
    2. Johan Blomquist & Joakim Westerlund, 2016. "Panel bootstrap tests of slope homogeneity," Empirical Economics, Springer, vol. 50(4), pages 1359-1381, June.
    3. 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).
    4. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
    5. Stephan Smeekes & Joakim Westerlund, 2019. "Robust block bootstrap panel predictability tests," Econometric Reviews, Taylor & Francis Journals, vol. 38(9), pages 1089-1107, October.
    6. Saeid Mahdavi & Joakim Westerlund, 2017. "Are state–local government expenditures converging? New evidence based on sequential unit root tests," Empirical Economics, Springer, vol. 53(2), pages 373-403, September.
    7. Matsuki, Takashi & Sugimoto, Kimiko, 2013. "Stationarity of Asian real exchange rates: An empirical application of multiple testing to nonstationary panels with a structural break," Economic Modelling, Elsevier, vol. 34(C), pages 52-58.
    8. Di Iorio, Francesca & Fachin, Stefano, 2010. "A Panel Cointegration study of the long-run relationship between Savings and Investments in the OECD economies, 1970-2007," MPRA Paper 25873, University Library of Munich, Germany.
    9. Joakim Westerlund & Johan Blomquist, 2013. "A modified LLC panel unit root test of the PPP hypothesis," Empirical Economics, Springer, vol. 44(2), pages 833-860, April.
    10. Milda Norkute, 2015. "Can the sectoral New Keynesian Phillips curve explain inflation dynamics in the Euro Area?," Empirical Economics, Springer, vol. 49(4), pages 1191-1216, December.
    11. Takashi Matsuki, 2016. "Linear and nonlinear comovement in Southeast Asian local currency bond markets: a stepwise multiple testing approach," Empirical Economics, Springer, vol. 51(2), pages 591-619, September.
    12. Czudaj, Robert & Hanck, Christoph, 2013. "Nonstationary-Volatility Robust Panel Unit Root Tests and the Great Moderation," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79734, Verein für Socialpolitik / German Economic Association.
    13. Perevyshin, Yu. & Skrobotov, A., 2017. "The Price Convergence of Individual Goods in the Russian Regions," Journal of the New Economic Association, New Economic Association, vol. 35(3), pages 71-102.
    14. Westerlund, Joakim & Thuraisamy, Kannan & Sharma, Susan, 2015. "On the use of panel cointegration tests in energy economics," Energy Economics, Elsevier, vol. 50(C), pages 359-363.
    15. Adiguzel, Ugur & Sahbaz, Ahmet & Ozcan, Ceyhun Can & Nazlioglu, Saban, 2014. "The behavior of Turkish exchange rates: A panel data perspective," Economic Modelling, Elsevier, vol. 42(C), pages 177-185.
    16. Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Stephan Smeekes, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 398-415, May.

  19. Stephan Smeekes & A. M. Robert Taylor, 2010. "Bootstrap union tests for unit roots in the presence of nonstationary volatility," Discussion Papers 10/03, University of Nottingham, Granger Centre for Time Series Econometrics.

    Cited by:

    1. Skrobotov Anton, 2018. "On Bootstrap Implementation of Likelihood Ratio Test for a Unit Root," Working Papers wpaper-2018-302, Gaidar Institute for Economic Policy, revised 2018.
    2. Ghoshray, Atanu, 2021. "Are coffee farmers worse off in the long run?," 95th Annual Conference, March 29-30, 2021, Warwick, UK (Hybrid) 311084, Agricultural Economics Society - AES.
    3. Smeekes, S., 2011. "Bootstrap sequential tests to determine the stationary units in a panel," Research Memorandum 003, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    4. Tony Addison & Atanu Ghoshray, 2020. "Discerning trends in international metal prices in the presence of non-stationary volatility," WIDER Working Paper Series wp-2020-104, World Institute for Development Economic Research (UNU-WIDER).
    5. 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).
    6. Giuseppe Cavaliere & Peter C. B. Phillips & Stephan Smeekes & A. M. Robert Taylor, 2015. "Lag Length Selection for Unit Root Tests in the Presence of Nonstationary Volatility," Econometric Reviews, Taylor & Francis Journals, vol. 34(4), pages 512-536, April.
    7. Andre M. Marques & Gilberto Tadeu Lima, 2021. "Testing for Granger Causality in Quantiles Between the Wage Share and Capacity Utilization," Working Papers, Department of Economics 2021_03, University of São Paulo (FEA-USP).
    8. Friedrich, Marina & Lin, Yicong, 2024. "Sieve bootstrap inference for linear time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 239(1).
    9. Ghoshray, Atanu, 2022. "Trends and persistence of farm-gate coffee prices around the world," 96th Annual Conference, April 4-6, 2022, K U Leuven, Belgium 321166, Agricultural Economics Society - AES.
    10. Marina Friedrich & Luca Margaritella & Stephan Smeekes, 2023. "High-Dimensional Granger Causality for Climatic Attribution," Papers 2302.03996, arXiv.org, revised Jun 2024.
    11. Maican, Florin G. & Sweeney, Richard J., 2013. "Rejection Probabilities for a Battery of Unit-Root Tests," Working Papers in Economics 568, University of Gothenburg, Department of Economics.
    12. Marques, André M. & Lima, Gilberto Tadeu, 2022. "Testing for Granger causality in quantiles between the wage share in income and productive capacity utilization," Structural Change and Economic Dynamics, Elsevier, vol. 62(C), pages 290-312.
    13. Martin C. Arnold & Thilo Reinschlussel, 2024. "Bootstrap Adaptive Lasso Solution Path Unit Root Tests," Papers 2409.07859, arXiv.org.
    14. Skrobotov, Anton, 2020. "Survey on structural breaks and unit root tests," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 58, pages 96-141.
    15. Mahdavi, Saeid & Westerlund, Joakim, 2018. "Subnational government tax revenue capacity and effort convergence: New evidence from sequential unit root tests," Economic Modelling, Elsevier, vol. 73(C), pages 174-183.
    16. Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Stephan Smeekes, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 398-415, May.
    17. Yicong Lin & Mingxuan Song, 2023. "Robust bootstrap inference for linear time-varying coefficient models: Some Monte Carlo evidence," Tinbergen Institute Discussion Papers 23-049/III, Tinbergen Institute.

  20. Smeekes, S., 2009. "Detrending bootstrap unit root tests," Research Memorandum 056, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).

    Cited by:

    1. Skrobotov Anton, 2018. "On Bootstrap Implementation of Likelihood Ratio Test for a Unit Root," Working Papers wpaper-2018-302, Gaidar Institute for Economic Policy, revised 2018.
    2. Palm, F.C. & Smeekes, S. & Urbain, J.R.Y.J., 2008. "Cross-sectional dependence robust block bootstrap panel unit root tests," Research Memorandum 048, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    3. Kruel, Maximiliano & Ceretta, Paulo Sergio, 2022. "Asymmetric influences on Latin American stock markets: A quantile approach," The Journal of Economic Asymmetries, Elsevier, vol. 26(C).
    4. Stephan Smeekes & A. M. Robert Taylor, 2010. "Bootstrap union tests for unit roots in the presence of nonstationary volatility," Discussion Papers 10/03, University of Nottingham, Granger Centre for Time Series Econometrics.
    5. 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).
    6. Sergei Kharin & Zuzana Kapustova & Ivan Lichner, 2024. "Unveiling an asymmetric relationship between global crude oil and local food prices in an oil-importing economy," Letters in Spatial and Resource Sciences, Springer, vol. 17(1), pages 1-24, December.
    7. Yanglin Li, 2024. "New Unit Root Tests in the Nonlinear ESTAR Framework: The Movement and Volatility Characteristics of Crude oil and Copper Prices," Computational Economics, Springer;Society for Computational Economics, vol. 63(5), pages 1757-1776, May.
    8. Zhenxin Wang & Shaoping Wang & Yayi Yan, 2024. "Sieve Bootstrap for Fixed-b Phillips–Perron Unit Root Test," Computational Economics, Springer;Society for Computational Economics, vol. 64(6), pages 3181-3205, December.
    9. Sergei Kharin & Zuzana Kapustova & Ivan Lichner, 2023. "Price transmission between maize and poultry product markets in the Visegrád Group countries: What is more nonlinear, egg or chicken?," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 69(12), pages 510-522.
    10. Takashi Matsuki, 2016. "Linear and nonlinear comovement in Southeast Asian local currency bond markets: a stepwise multiple testing approach," Empirical Economics, Springer, vol. 51(2), pages 591-619, September.
    11. Skrobotov, Anton, 2020. "Survey on structural breaks and unit root tests," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 58, pages 96-141.
    12. Li, Haiqi & Zheng, Chaowen, 2018. "Unit root quantile autoregression testing with smooth structural changes," Finance Research Letters, Elsevier, vol. 25(C), pages 83-89.

  21. Palm, F.C. & Smeekes, S. & Urbain, J.R.Y.J., 2008. "Cross-sectional dependence robust block bootstrap panel unit root tests," Research Memorandum 048, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).

    Cited by:

    1. Vincent Bodart & Bertrand Candelon & Jean - François Carpantier, 2011. "Real exchanges rates in commodity producing countries : A reappraisal," Working Papers hal-01821152, HAL.
    2. Guohua Feng & Jiti Gao & Fei Liu & Bin Peng, 2024. "Estimation and Inference for Three-Dimensional Panel Data Models," Papers 2404.08365, arXiv.org, revised Sep 2024.
    3. Francesca Iorio & Stefano Fachin, 2014. "Savings and investments in the OECD: a panel cointegration study with a new bootstrap test," Empirical Economics, Springer, vol. 46(4), pages 1271-1300, June.
    4. Vincent Bodart & Bertrand Candelon & Jean-François Carpantier, 2015. "Real exchanges rates, commodity prices and structural factors in developing countries," Post-Print hal-01821129, HAL.
    5. Borsi, Mihály Tamás & Metiu, Norbert, 2013. "The evolution of economic convergence in the European Union," Discussion Papers 28/2013, Deutsche Bundesbank.
    6. Muhammad Omer & Jakob de Haan & Bert Scholtens, 2019. "Does Uncovered Interest Rate Parity Hold After All?," Lahore Journal of Economics, Department of Economics, The Lahore School of Economics, vol. 24(2), pages 49-72, July-Dec.
    7. Hu, Yang & Valera, Harold Glenn A. & Oxley, Les, 2019. "Market efficiency of the top market-cap cryptocurrencies: Further evidence from a panel framework," Finance Research Letters, Elsevier, vol. 31(C), pages 138-145.
    8. Eijffinger, S.C.W. & Qian, Z., 2010. "Globalization and the Output-Inflation Tradeoff : New Time Series Evidence," Other publications TiSEM f4bfa96e-e080-4bb4-9714-c, Tilburg University, School of Economics and Management.
    9. M. Hashem Pesaran & L. Vanessa Smith & Takashi Yamagata, 2008. "Panel Unit Root Tests in the Presence of a Multifactor Error Structure," CESifo Working Paper Series 2193, CESifo.
    10. Asiye Tutuncu & Yasar Bayraktar, 2024. "The effect of democracy and corruption paradox on economic growth: MINT countries," Economic Change and Restructuring, Springer, vol. 57(4), pages 1-25, August.
    11. Nikolaos Giannellis & Minoas Koukouritakis, 2018. "Gold Price and Exchange Rates: A Panel Smooth Transition Regression Model for the G7 Countries," Working Papers 1806, University of Crete, Department of Economics.
    12. Stephan Smeekes & Jean-Pierre Urbain, 2014. "On the Applicability of the Sieve Bootstrap in Time Series Panels," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 139-151, February.
    13. Smeekes, S., 2011. "Bootstrap sequential tests to determine the stationary units in a panel," Research Memorandum 003, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    14. Chaohua Dong & Jiti Gao & Bin Peng & Yayi Yan, 2023. "Estimation and Inference for a Class of Generalized Hierarchical Models," Papers 2311.02789, arXiv.org, revised Apr 2024.
    15. Francesca Di Iorio & Umberto Triacca, 2014. "Testing for A Set of Linear Restrictions in VARMA Models Using Autoregressive Metric: An Application to Granger Causality Test," Econometrics, MDPI, vol. 2(4), pages 1-14, December.
    16. Gabriel Bruneau & Kevin Moran, 2012. "Exchange Rate Fluctuations and Labour Market Adjustments in Canadian Manufacturing Industries," Cahiers de recherche 1227, CIRPEE.
    17. MOON, Hyungsik Roger & PERRON, Benoit, 2010. "Beyond Panel Unit Root Tests: Using Multiple Testing to Determine the Non Stationarity Properties of Individual Series in a Panel," Cahiers de recherche 2010-04, Universite de Montreal, Departement de sciences economiques.
    18. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2021. "Interactive R&D Spillovers: an estimation strategy based on forecasting-driven model selection," Working Papers hal-03224910, HAL.
    19. Peter Sephton & Tolga Omay, 2023. "A note on CO2 emissions using two new tests," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 50(4), pages 933-941, November.
    20. Muhammad Omer & Jakob de Haan & Bert Scholtens, 2014. "Testing uncovered interest rate parity using LIBOR," Applied Economics, Taylor & Francis Journals, vol. 46(30), pages 3708-3723, October.
    21. Gao, J. & Linton, O. & Peng, B., 2022. "A Nonparametric Panel Model for Climate Data with Seasonal and Spatial Variation," Janeway Institute Working Papers 2215, Faculty of Economics, University of Cambridge.
    22. Gioldasis, Georgios & Musolesi, Antonio & Simioni, Michel, 2023. "Interactive R&D spillovers: An estimation strategy based on forecasting-driven model selection," International Journal of Forecasting, Elsevier, vol. 39(1), pages 144-169.
    23. 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).
    24. Stephan Smeekes & Joakim Westerlund, 2019. "Robust block bootstrap panel predictability tests," Econometric Reviews, Taylor & Francis Journals, vol. 38(9), pages 1089-1107, October.
    25. Friedrich, Marina & Smeekes, Stephan & Urbain, Jean-Pierre, 2020. "Autoregressive wild bootstrap inference for nonparametric trends," Journal of Econometrics, Elsevier, vol. 214(1), pages 81-109.
    26. Saeid Mahdavi & Joakim Westerlund, 2017. "Are state–local government expenditures converging? New evidence based on sequential unit root tests," Empirical Economics, Springer, vol. 53(2), pages 373-403, September.
    27. Smeekes, Stephan & Wijler, Etienne, 2021. "An automated approach towards sparse single-equation cointegration modelling," Journal of Econometrics, Elsevier, vol. 221(1), pages 247-276.
    28. Ghoshray, Atanu & Monfort, Mercedes & Ordóñez, Javier, 2019. "Re-examining inequality persistence," Economics Discussion Papers 2019-70, Kiel Institute for the World Economy (IfW Kiel).
    29. Pedroni, Peter & Vogelsang, Timothy J. & Wagner, Martin & Westerlund, Joakim, 2011. "Nonparametric Rank Tests for Non-stationary Panels," Economics Series 270, Institute for Advanced Studies.
    30. Hanck, Christoph & Demetrescu, Matei & Tarcolea, Adina, 2012. "IV-Based Cointegration Testing in Dependent Panels with Time-Varying Variance," VfS Annual Conference 2012 (Goettingen): New Approaches and Challenges for the Labor Market of the 21st Century 62072, Verein für Socialpolitik / German Economic Association.
    31. Matsuki, Takashi & Sugimoto, Kimiko, 2013. "Stationarity of Asian real exchange rates: An empirical application of multiple testing to nonstationary panels with a structural break," Economic Modelling, Elsevier, vol. 34(C), pages 52-58.
    32. Helmut Herwartz & Yabibal M. Walle, 2018. "A powerful wild bootstrap diagnosis of panel unit roots under linear trends and time-varying volatility," Computational Statistics, Springer, vol. 33(1), pages 379-411, March.
    33. Yiannis Karavias & Elias Tzavalis, 2014. "Testing for unit roots in panels with structural changes, spatial and temporal dependence when the time dimension is finite," Discussion Papers 14/03, University of Nottingham, Granger Centre for Time Series Econometrics.
    34. Di Iorio, Francesca & Triacca, Umberto, 2013. "Testing for Granger non-causality using the autoregressive metric," Economic Modelling, Elsevier, vol. 33(C), pages 120-125.
    35. Peter Pedroni, 2018. "Panel Cointegration Techniques and Open Challenges," Department of Economics Working Papers 2018-09, Department of Economics, Williams College.
    36. Tolga Omay & Perihan Iren, 2023. "Controlling Heterogeneous Structure of Smooth Breaks in Panel Unit Root and Cointegration Testing," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 233-265, January.
    37. Di Iorio, Francesca & Fachin, Stefano, 2010. "A Panel Cointegration study of the long-run relationship between Savings and Investments in the OECD economies, 1970-2007," MPRA Paper 25873, University Library of Munich, Germany.
    38. Jiti Gao & Bin Peng & Yayi Yan, 2022. "Higher-order Expansions and Inference for Panel Data Models," Papers 2205.00577, arXiv.org, revised Jun 2023.
    39. Chaohua Dong & Jiti Gao & Bin Peng & Yayi Yan, 2023. "Estimation of Semiparametric Multi-Index Models Using Deep Neural Networks," Monash Econometrics and Business Statistics Working Papers 21/23, Monash University, Department of Econometrics and Business Statistics.
    40. Esra KADANALI & Omer YALCINKAYA, 2020. "Effects of Climate Change on Economic Growth: Evidence from 20 Biggest Economies of the World," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 93-118, September.
    41. Iorio, Francesca Di & Fachin, Stefano, 2014. "Savings and investments in the OECD, 1970–2007: A test of panel cointegration with regime changes," The North American Journal of Economics and Finance, Elsevier, vol. 28(C), pages 59-76.
    42. Takashi Matsuki, 2016. "Linear and nonlinear comovement in Southeast Asian local currency bond markets: a stepwise multiple testing approach," Empirical Economics, Springer, vol. 51(2), pages 591-619, September.
    43. Tolga Omay & Muhammad Shahbaz & Chris Stewart, 2021. "Is there really hysteresis in the OECD unemployment rates? New evidence using a Fourier panel unit root test," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 48(4), pages 875-901, November.
    44. Obasa, Rotimi Sunday & Gurowa, S. U, 2023. "Effect of Information and Communication Technology on Financial Performance of Deposit Money Banks in Nigeria," Thesis Commons c7x45_v1, Center for Open Science.
    45. Perevyshin, Yu. & Skrobotov, A., 2017. "The Price Convergence of Individual Goods in the Russian Regions," Journal of the New Economic Association, New Economic Association, vol. 35(3), pages 71-102.
    46. Omid Ranjbar & Xiao-Lin Li & Tsangyao Chang & Chien-Chiang Lee, 2015. "Stability of long-run growth in East Asian countries: New evidence from panel stationarity test with structural breaks," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 24(4), pages 570-589, June.
    47. Nikolaos Giannellis & Minoas Koukouritakis, 2018. "Currency Misalignments in the BRIICS Countries: Fixed Vs. Floating Exchange Rates," Open Economies Review, Springer, vol. 29(5), pages 1123-1151, November.
    48. Xie, Zixiong & Chen, Shyh-Wei, 2019. "Exchange rates and fundamentals: A bootstrap panel data analysis," Economic Modelling, Elsevier, vol. 78(C), pages 209-224.
    49. Francesca Di Iorio & Stefano Fachin, 2017. "Evaluating Restricted Common Factor models for non-stationary data," DSS Empirical Economics and Econometrics Working Papers Series 2017/2, Centre for Empirical Economics and Econometrics, Department of Statistics, "Sapienza" University of Rome.
    50. Hodelin, Reynaldo Senra, 2022. "Statistical disclosure and economic growth: What is the nexus?," World Development, Elsevier, vol. 160(C).
    51. Mirza, Faisal Mehmood & Sinha, Avik & Khan, Javeria Rehman & Kalugina, Olga A. & Zafar, Muhammad Wasif, 2022. "Impact of Energy Efficiency on CO2 Emissions: Empirical Evidence from Developing Countries," MPRA Paper 111923, University Library of Munich, Germany, revised 2022.
    52. Obasa, Rotimi Sunday Mr. & Gurowa, S. U, 2023. "Effect of Information and Communication Technology on Financial Performance of Deposit Money Bank in Nigeria," Thesis Commons c7x45, Center for Open Science.
    53. Emirmahmutoglu, Furkan & Omay, Tolga, 2014. "Reexamining the PPP hypothesis: A nonlinear asymmetric heterogeneous panel unit root test," Economic Modelling, Elsevier, vol. 40(C), pages 184-190.
    54. Westerlund, Joakim & Thuraisamy, Kannan, 2016. "Panel multi-predictor test procedures with an application to emerging market sovereign risk," Emerging Markets Review, Elsevier, vol. 28(C), pages 44-60.
    55. Jiti Gao & Bin Peng & Yayi Yan, 2022. "A Simple Bootstrap Method for Panel Data Inferences," Monash Econometrics and Business Statistics Working Papers 7/22, Monash University, Department of Econometrics and Business Statistics.
    56. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2021. "Interactive R&D Spillovers: An estimation strategy based on forecasting-driven model selection," SEEDS Working Papers 0621, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Jun 2021.
    57. Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Stephan Smeekes, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 398-415, May.
    58. Yushu Li & Ghazi Shukur, 2013. "Testing for Unit Roots in Panel Data Using a Wavelet Ratio Method," Computational Economics, Springer;Society for Computational Economics, vol. 41(1), pages 59-69, January.
    59. Mrabet, Zouhair & Alsamara, Mouyad & Mimouni, Karim & Mnasri, Ayman, 2021. "Can human development and political stability improve environmental quality? New evidence from the MENA region," Economic Modelling, Elsevier, vol. 94(C), pages 28-44.
    60. Okelele, Daniel Ochudi & Lokina, Razack & Ruhinduka, Remidius Denis, 2021. "Effect of Trade Openness on Ecological Footprint in Sub-Saharan Africa," African Journal of Economic Review, African Journal of Economic Review, vol. 10(01), December.
    61. Fumitaka Furuoka, 2017. "A new approach to testing unemployment hysteresis," Empirical Economics, Springer, vol. 53(3), pages 1253-1280, November.
    62. Yayi Yan & Jiti Gao & Bin Peng, 2021. "Asymptotics for Time-Varying Vector MA(∞) Processes," Monash Econometrics and Business Statistics Working Papers 22/21, Monash University, Department of Econometrics and Business Statistics.

  22. Palm, F.C. & Smeekes, S. & Urbain, J.R.Y.J., 2007. "A sieve bootstrap test for cointegration in a conditional error correction model," Research Memorandum 054, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).

    Cited by:

    1. Kascha, Christian & Trenkler, Carsten, 2011. "Bootstrapping the likelihood ratio cointegration test in error correction models with unknown lag order," Computational Statistics & Data Analysis, Elsevier, vol. 55(2), pages 1008-1017, February.
    2. Wen-Yi Chen, 2013. "Does Housing Cost Affect Birth Rates in Taiwan? The ADL Test for Threshold Co-integration," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 90-103, October.
    3. Stephan Smeekes & Jean-Pierre Urbain, 2014. "On the Applicability of the Sieve Bootstrap in Time Series Panels," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 139-151, February.
    4. Ghysels, Eric & Miller, J. Isaac, 2013. "Testing for Cointegration with Temporally Aggregated and Mixed-frequency Time Series," CEPR Discussion Papers 9654, C.E.P.R. Discussion Papers.
    5. Marian Vavra, 2015. "On a Bootstrap Test for Forecast Evaluations," Working and Discussion Papers WP 5/2015, Research Department, National Bank of Slovakia.
    6. Stephan Smeekes, 2013. "Detrending Bootstrap Unit Root Tests," Econometric Reviews, Taylor & Francis Journals, vol. 32(8), pages 869-891, November.
    7. Smeekes, Stephan & Wijler, Etienne, 2021. "An automated approach towards sparse single-equation cointegration modelling," Journal of Econometrics, Elsevier, vol. 221(1), pages 247-276.
    8. Sebastian Kripfganz & Daniel C. Schneider, 2019. "Response surface regressions for critical value bounds and approximate p-values in equilibrium correction models," Discussion Papers 1901, University of Exeter, Department of Economics.
    9. Bayer, C & Hanck, C.H., 2009. "Combining non-cointegration tests," Research Memorandum 012, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    10. Melike E. Bildirici & Sema Yılmaz Genç & Rui Alexandre Castanho, 2022. "Environmental Pollution, Terrorism, and Mortality Rate in China, India, Russia, and Türkiye," Sustainability, MDPI, vol. 14(19), pages 1-11, October.
    11. Gengenbach, C. & Urbain, J.R.Y.J. & Westerlund, J., 2008. "Panel error correction testing with global stochastic trends," Research Memorandum 051, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    12. Omay, Tolga & Emirmahmutoglu, Furkan & Denaux, Zulal S., 2017. "Nonlinear error correction based cointegration test in panel data," Economics Letters, Elsevier, vol. 157(C), pages 1-4.
    13. Zacharias Psaradakis & Marián Vávra, 2015. "A Distance Test of Normality for a Wide Class of Stationary Processes," Birkbeck Working Papers in Economics and Finance 1513, Birkbeck, Department of Economics, Mathematics & Statistics.
    14. Ayman Mnasri & Zouhair Mrabet & Mouyad Alsamara, 2023. "A new quadratic asymmetric error correction model: does size matter?," Empirical Economics, Springer, vol. 65(1), pages 33-64, July.
    15. Karsten Reichold & Carsten Jentsch, 2022. "A Bootstrap-Assisted Self-Normalization Approach to Inference in Cointegrating Regressions," Papers 2204.01373, arXiv.org.
    16. Sidorenko, Alexandra, 2001. "Stochastic Model of Demand for Medical Care with Endogenous Labour Supply and Health Insurance," Departmental Working Papers 2001-08, The Australian National University, Arndt-Corden Department of Economics.

  23. Palm, F.C. & Smeekes, S. & Urbain, J.R.Y.J., 2006. "Bootstrap unit root tests: comparison and extensions," Research Memorandum 015, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).

    Cited by:

    1. Hwang, Eunju & Shin, Dong Wan, 2015. "Stationary bootstrapping for semiparametric panel unit root tests," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 14-25.
    2. Skrobotov Anton, 2018. "On Bootstrap Implementation of Likelihood Ratio Test for a Unit Root," Working Papers wpaper-2018-302, Gaidar Institute for Economic Policy, revised 2018.
    3. Jaap W. B. Bos & Bertrand Candelon & Claire Economidou, 2016. "Does knowledge spill over across borders and technology regimes?," Journal of Productivity Analysis, Springer, vol. 46(1), pages 63-82, August.
    4. SILVESTRINI, Andrea, 2010. "Testing fiscal sustainability in Poland: a Bayesian analysis of cointegration," LIDAM Reprints CORE 2220, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Shin, Dong Wan & Hwang, Eunju, 2013. "Stationary bootstrapping for cointegrating regressions," Statistics & Probability Letters, Elsevier, vol. 83(2), pages 474-480.
    6. Kristoffer Pons Bertelsen, 2019. "Comparing Tests for Identification of Bubbles," CREATES Research Papers 2019-16, Department of Economics and Business Economics, Aarhus University.
    7. Astill, Sam & Taylor, A.M. Robert & Kellard, Neil & Korkos, Ioannis, 2023. "Using covariates to improve the efficacy of univariate bubble detection methods," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 342-366.
    8. Stephan Smeekes & Jean-Pierre Urbain, 2014. "On the Applicability of the Sieve Bootstrap in Time Series Panels," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 139-151, February.
    9. Rossi, Francesca & Lieberman, Offer, 2023. "Spatial autoregressions with an extended parameter space and similarity-based weights," Journal of Econometrics, Elsevier, vol. 235(2), pages 1770-1798.
    10. Su, Jen-Je & Cheung, Adrian (Wai-Kong) & Roca, Eduardo, 2014. "Does Purchasing Power Parity hold? New evidence from wild-bootstrapped nonlinear unit root tests in the presence of heteroskedasticity," Economic Modelling, Elsevier, vol. 36(C), pages 161-171.
    11. Stephan Smeekes & A. M. Robert Taylor, 2010. "Bootstrap union tests for unit roots in the presence of nonstationary volatility," Discussion Papers 10/03, University of Nottingham, Granger Centre for Time Series Econometrics.
    12. Richard, Patrick, 2009. "Modified fast double sieve bootstraps for ADF tests," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4490-4499, October.
    13. Bruce E. Hansen & Jeffrey S. Racine, 2024. "Bootstrap Model Averaging Unit Root Inference," Advances in Econometrics, in: Essays in Honor of Subal Kumbhakar, volume 46, pages 81-98, Emerald Group Publishing Limited.
    14. 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).
    15. Stephan Smeekes, 2013. "Detrending Bootstrap Unit Root Tests," Econometric Reviews, Taylor & Francis Journals, vol. 32(8), pages 869-891, November.
    16. Margherita Gerolimetto & Stefano Magrini, 2020. "Testing for boundary conditions in case of fractionally integrated processes," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 357-371, June.
    17. Palm, F.C. & Smeekes, S. & Urbain, J.R.Y.J., 2007. "A sieve bootstrap test for cointegration in a conditional error correction model," Research Memorandum 054, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    18. Wang Liqiong, 2013. "Bootstrap Point Optimal Unit Root Tests," Journal of Time Series Econometrics, De Gruyter, vol. 6(1), pages 1-31, July.
    19. Friedrich, Marina & Lin, Yicong, 2024. "Sieve bootstrap inference for linear time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 239(1).
    20. Davidson, Russell & Trokić, Mirza, 2020. "The fast iterated bootstrap," Journal of Econometrics, Elsevier, vol. 218(2), pages 451-475.
    21. Franco, G.C. & Reisen, V.A. & Alves, F.A., 2013. "Bootstrap tests for fractional integration and cointegration: A comparison study," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 87(C), pages 19-29.
    22. Peter C. B. Phillips, 2009. "Bootstrapping I(1) Data," Cowles Foundation Discussion Papers 1689, Cowles Foundation for Research in Economics, Yale University.
    23. V. A. Reisen & C. Lévy-Leduc & M. Bourguignon & H. Boistard, 2017. "Robust Dickey–Fuller tests based on ranks for time series with additive outliers," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(1), pages 115-131, January.
    24. Sevan Gulesserian & Mohitosh Kejriwal, 2014. "On the power of bootstrap tests for stationarity: a Monte Carlo comparison," Empirical Economics, Springer, vol. 46(3), pages 973-998, May.
    25. Skrobotov, Anton, 2020. "Survey on structural breaks and unit root tests," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 58, pages 96-141.
    26. László Kónya, 2020. "Did the unemployment rates converge in the EU?," Empirical Economics, Springer, vol. 59(2), pages 627-657, August.
    27. Xuguang Sheng & Jingyun Yang, 2013. "Truncated Product Methods for Panel Unit Root Tests," Working Papers 2013-004, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    28. Nunzio Cappuccio & Diego Lubian, 2016. "Unit Root Tests: The Role of the Univariate Models Implied by Multivariate Time Series," Econometrics, MDPI, vol. 4(2), pages 1-11, April.
    29. Peter Sephton, 2017. "Finite Sample Critical Values of the Generalized KPSS Stationarity Test," Computational Economics, Springer;Society for Computational Economics, vol. 50(1), pages 161-172, June.
    30. Gutierrez, Luciano, 2011. "Looking for Rational Bubbles in Agricultural Commodity Markets," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 120377, European Association of Agricultural Economists.
    31. Giuseppe Cavaliere & A. M. Robert Taylor, 2009. "Bootstrap M Unit Root Tests," Econometric Reviews, Taylor & Francis Journals, vol. 28(5), pages 393-421.
    32. Gutierrez, Luciano, 2011. "Bootstrapping asset price bubbles," Economic Modelling, Elsevier, vol. 28(6), pages 2488-2493.
    33. Shelef, Amit, 2016. "A Gini-based unit root test," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 763-772.
    34. 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.
    35. Zou, Nan & Politis, Dimitris N., 2019. "Linear process bootstrap unit root test," Statistics & Probability Letters, Elsevier, vol. 145(C), pages 74-80.

Articles

  1. Beutner, Eric & Heinemann, Alexander & Smeekes, Stephan, 2024. "A residual bootstrap for conditional Value-at-Risk," Journal of Econometrics, Elsevier, vol. 238(2).
    See citations under working paper version above.
  2. Alain Hecq & Luca Margaritella & Stephan Smeekes, 2023. "Granger Causality Testing in High-Dimensional VARs: A Post-Double-Selection Procedure," Journal of Financial Econometrics, Oxford University Press, vol. 21(3), pages 915-958.
    See citations under working paper version above.
  3. Adamek, Robert & Smeekes, Stephan & Wilms, Ines, 2023. "Lasso inference for high-dimensional time series," Journal of Econometrics, Elsevier, vol. 235(2), pages 1114-1143.
    See citations under working paper version above.
  4. Eric Beutner & Yicong Lin & Stephan Smeekes, 2023. "GLS estimation and confidence sets for the date of a single break in models with trends," Econometric Reviews, Taylor & Francis Journals, vol. 42(2), pages 195-219, February.

    Cited by:

    1. Yicong Lin & Mingxuan Song, 2023. "Robust bootstrap inference for linear time-varying coefficient models: Some Monte Carlo evidence," Tinbergen Institute Discussion Papers 23-049/III, Tinbergen Institute.

  5. Smeekes, Stephan & Wijler, Etienne, 2021. "An automated approach towards sparse single-equation cointegration modelling," Journal of Econometrics, Elsevier, vol. 221(1), pages 247-276.
    See citations under working paper version above.
  6. Caterina Schiavoni & Franz Palm & Stephan Smeekes & Jan van den Brakel, 2021. "A dynamic factor model approach to incorporate Big Data in state space models for official statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 324-353, January.
    See citations under working paper version above.
  7. Marina Friedrich & Eric Beutner & Hanno Reuvers & Stephan Smeekes & Jean-Pierre Urbain & Whitney Bader & Bruno Franco & Bernard Lejeune & Emmanuel Mahieu, 2020. "A statistical analysis of time trends in atmospheric ethane," Climatic Change, Springer, vol. 162(1), pages 105-125, September.
    See citations under working paper version above.
  8. Friedrich, Marina & Smeekes, Stephan & Urbain, Jean-Pierre, 2020. "Autoregressive wild bootstrap inference for nonparametric trends," Journal of Econometrics, Elsevier, vol. 214(1), pages 81-109.
    See citations under working paper version above.
  9. Stephan Smeekes & Joakim Westerlund, 2019. "Robust block bootstrap panel predictability tests," Econometric Reviews, Taylor & Francis Journals, vol. 38(9), pages 1089-1107, October.
    See citations under working paper version above.
  10. Smeekes, Stephan & Wijler, Etienne, 2018. "Macroeconomic forecasting using penalized regression methods," International Journal of Forecasting, Elsevier, vol. 34(3), pages 408-430.
    See citations under working paper version above.
  11. Christophe Hurlin & Sébastien Laurent & Rogier Quaedvlieg & Stephan Smeekes, 2017. "Risk Measure Inference," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(4), pages 499-512, October.
    See citations under working paper version above.
  12. Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2016. "Testing for Granger causality in large mixed-frequency VARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 418-432.
    See citations under working paper version above.
  13. Giuseppe Cavaliere & Peter C. B. Phillips & Stephan Smeekes & A. M. Robert Taylor, 2015. "Lag Length Selection for Unit Root Tests in the Presence of Nonstationary Volatility," Econometric Reviews, Taylor & Francis Journals, vol. 34(4), pages 512-536, April.
    See citations under working paper version above.
  14. Stephan Smeekes & Jean-Pierre Urbain, 2014. "On the Applicability of the Sieve Bootstrap in Time Series Panels," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 139-151, February.
    See citations under working paper version above.
  15. Stephan Smeekes, 2013. "Detrending Bootstrap Unit Root Tests," Econometric Reviews, Taylor & Francis Journals, vol. 32(8), pages 869-891, November.
    See citations under working paper version above.
  16. Smeekes, Stephan & Taylor, A.M. Robert, 2012. "Bootstrap Union Tests For Unit Roots In The Presence Of Nonstationary Volatility," Econometric Theory, Cambridge University Press, vol. 28(2), pages 422-456, April.
    See citations under working paper version above.
  17. Palm, Franz C. & Smeekes, Stephan & Urbain, Jean-Pierre, 2011. "Cross-sectional dependence robust block bootstrap panel unit root tests," Journal of Econometrics, Elsevier, vol. 163(1), pages 85-104, July.
    See citations under working paper version above.
  18. Palm, Franz C. & Smeekes, Stephan & Urbain, Jean-Pierre, 2010. "A Sieve Bootstrap Test For Cointegration In A Conditional Error Correction Model," Econometric Theory, Cambridge University Press, vol. 26(3), pages 647-681, June.
    • Arnold Zellner & Franz C. Palm, 2000. "Correction," Econometrica, Econometric Society, vol. 68(5), pages 1293-1294, September.
    See citations under working paper version above.
  19. Franz C. Palm & Stephan Smeekes & Jean‐Pierre Urbain, 2008. "Bootstrap Unit‐Root Tests: Comparison and Extensions," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(2), pages 371-401, March.
    See citations under working paper version above.
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