IDEAS home Printed from https://ideas.repec.org/e/c/pan48.html
   My authors  Follow this author

Stanislav Anatolyev

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. Stanislav Anatolyev & Mikkel S{o}lvsten, 2020. "Testing Many Restrictions Under Heteroskedasticity," Papers 2003.07320, arXiv.org, revised Jan 2023.

    Cited by:

    1. Hyunseok Jung & Xiaodong Liu, 2023. "Testing for Peer Effects without Specifying the Network Structure," Papers 2306.09806, arXiv.org, revised Jul 2024.
    2. Michal Koles'ar & Ulrich K. Muller & Sebastian T. Roelsgaard, 2023. "The Fragility of Sparsity," Papers 2311.02299, arXiv.org, revised Jan 2024.
    3. Patrick Kline & Raffaele Saggio & Mikkel S{o}lvsten, 2018. "Leave-out estimation of variance components," Papers 1806.01494, arXiv.org, revised Aug 2019.
    4. Anna Mikusheva & Liyang Sun, 2024. "Weak identification with many instruments," The Econometrics Journal, Royal Economic Society, vol. 27(2), pages -28.

  2. Stanislav Anatolyev & Anna Mikusheva, 2018. "Limit Theorems for Factor Models," Papers 1807.06338, arXiv.org, revised Sep 2020.

    Cited by:

    1. Stanislav Anatolyev & Anna Mikusheva, 2018. "Factor models with many assets: strong factors, weak factors, and the two-pass procedure," Papers 1807.04094, arXiv.org, revised Apr 2019.
    2. Anatolyev, Stanislav & Sølvsten, Mikkel, 2023. "Testing many restrictions under heteroskedasticity," Journal of Econometrics, Elsevier, vol. 236(1).
    3. Christis Katsouris, 2023. "Optimal Estimation Methodologies for Panel Data Regression Models," Papers 2311.03471, arXiv.org, revised Nov 2023.

  3. Stanislav Anatolyev & Anna Mikusheva, 2018. "Factor models with many assets: strong factors, weak factors, and the two-pass procedure," Papers 1807.04094, arXiv.org, revised Apr 2019.

    Cited by:

    1. Dennis Lim & Wenjie Wang & Yichong Zhang, 2022. "A Conditional Linear Combination Test with Many Weak Instruments," Papers 2207.11137, arXiv.org, revised Apr 2023.
    2. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2021. "Measurement of factor strength: Theory and practice," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 587-613, August.
    3. Anatolyev, Stanislav & Mikusheva, Anna, 2021. "Limit Theorems For Factor Models," Econometric Theory, Cambridge University Press, vol. 37(5), pages 1034-1074, October.
    4. Alena Skolkova, 2023. "Instrumental Variable Estimation with Many Instruments Using Elastic-Net IV," CERGE-EI Working Papers wp759, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    5. Yinchu Zhu, 2019. "How well can we learn large factor models without assuming strong factors?," Papers 1910.10382, arXiv.org, revised Nov 2019.
    6. Gregory, Richard P., 2024. "Risk premiums from temperature trends," International Review of Economics & Finance, Elsevier, vol. 91(C), pages 505-525.
    7. Jungjun Choi & Ming Yuan, 2024. "High Dimensional Factor Analysis with Weak Factors," Papers 2402.05789, arXiv.org.
    8. Allen, David, 2022. "Asset Pricing Tests, Endogeneity issues and Fama-French factors," MPRA Paper 113610, University Library of Munich, Germany.
    9. Richard Paul Gregory, 2024. "The Role of Political Uncertainty in Climate-Related Disaster Impacts on Financial Markets," JRFM, MDPI, vol. 17(7), pages 1-27, June.
    10. Lim, Dennis & Wang, Wenjie & Zhang, Yichong, 2024. "A conditional linear combination test with many weak instruments," Journal of Econometrics, Elsevier, vol. 238(2).

  4. Stanislav Anatolyev & Sergei Seleznev & Veronika Selezneva, 2018. "Formation of Market Beliefs in the Oil Market," CERGE-EI Working Papers wp619, The Center for Economic Research and Graduate Education - Economics Institute, Prague.

    Cited by:

    1. Sultan Alturki & Alexander Kurov, 2022. "Market inefficiencies surrounding energy announcements," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(1), pages 172-188, January.

  5. Stanislav Anatolyev & Jozef Barunik, 2017. "Forecasting dynamic return distributions based on ordered binary choice," Papers 1711.05681, arXiv.org, revised Jan 2019.

    Cited by:

    1. Stanislav Anatolyev, 2021. "Directional news impact curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 94-107, January.
    2. Jozef Barunik & Lubos Hanus, 2023. "Learning Probability Distributions of Day-Ahead Electricity Prices," Papers 2310.02867, arXiv.org, revised Oct 2023.
    3. Jozef Barunik & Lubos Hanus, 2022. "Learning Probability Distributions in Macroeconomics and Finance," Papers 2204.06848, arXiv.org.
    4. Baruník, Jozef & Hanus, Luboš, 2024. "Fan charts in era of big data and learning," Finance Research Letters, Elsevier, vol. 61(C).
    5. Lei, Heng & Xue, Minggao & Liu, Huiling, 2022. "Probability distribution forecasting of carbon allowance prices: A hybrid model considering multiple influencing factors," Energy Economics, Elsevier, vol. 113(C).

  6. Stanislav Anatolyev & Nikita Kobotaev, 2015. "Modeling and Forecasting Realized Covariance Matrices with Accounting for Leverage," Working Papers w0213, New Economic School (NES).

    Cited by:

    1. Cem Cakmakli & Verda Ozturk, 2021. "Economic Value of Modeling the Joint Distribution of Returns and Volatility: Leverage Timing," Koç University-TUSIAD Economic Research Forum Working Papers 2110, Koc University-TUSIAD Economic Research Forum.
    2. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2020. "Multivariate leverage effects and realized semicovariance GARCH models," Journal of Econometrics, Elsevier, vol. 217(2), pages 411-430.
    3. Luo, Jiawen & Chen, Langnan, 2020. "Realized volatility forecast with the Bayesian random compressed multivariate HAR model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 781-799.
    4. Qu, Hui & Zhang, Yi, 2022. "Asymmetric multivariate HAR models for realized covariance matrix: A study based on volatility timing strategies," Economic Modelling, Elsevier, vol. 106(C).
    5. 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.
    6. Yaojie Zhang & Yu Wei & Li Liu, 2019. "Improving forecasting performance of realized covariance with extensions of HAR-RCOV model: statistical significance and economic value," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1425-1438, September.
    7. Jiawen Luo & Langnan Chen, 2019. "Multivariate realized volatility forecasts of agricultural commodity futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(12), pages 1565-1586, December.
    8. Alfelt, Gustav & Bodnar, Taras & Javed, Farrukh & Tyrcha, Joanna, 2020. "Singular conditional autoregressive Wishart model for realized covariance matrices," Working Papers 2021:1, Örebro University, School of Business.

  7. Stanislav Anatolyev & Nikolay Gospodinov, 2015. "Multivariate return decomposition: theory and implications," FRB Atlanta Working Paper 2015-7, Federal Reserve Bank of Atlanta.

    Cited by:

    1. Stanislav Anatolyev, 2021. "Directional news impact curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 94-107, January.
    2. Nikolay Gospodinov, 2017. "Asset Co-movements: Features and Challenges," FRB Atlanta Working Paper 2017-11, Federal Reserve Bank of Atlanta.
    3. Chen, Nan-Kuang & Chen, Shiu-Sheng & Chou, Yu-Hsi, 2013. "Further evidence on bear market predictability: The role of the external finance premium," MPRA Paper 49093, University Library of Munich, Germany.

  8. Stanislav Anatolyev, 2012. "Instrumental variables estimation and inference in the presence of many exogenous regressors," Working Papers w0162, New Economic School (NES).

    Cited by:

    1. Alyssa G. Anderson & Wenxin Du & Bernd Schlusche, 2021. "Arbitrage Capital of Global Banks," Finance and Economics Discussion Series 2021-032, Board of Governors of the Federal Reserve System (U.S.).
    2. Mattia Filomena & Matteo Picchio & Alessia Lo Turco, 2024. "Trade exposure, immigrants and workplace injuries," Working Papers 488, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    3. Eric Gautier & Christiern Rose, 2022. "Fast, Robust Inference for Linear Instrumental Variables Models using Self-Normalized Moments," Papers 2211.02249, arXiv.org, revised Nov 2022.
    4. Eleonora Patacchini & Tiziano Arduini & Edoardo Rainone, 2014. "Identification and Estimation of Outcome Response with Heterogeneous Treatment Externalities," Center for Policy Research Working Papers 167, Center for Policy Research, Maxwell School, Syracuse University.
    5. Daniel A. Broxterman & William D. Larson, 2020. "An empirical examination of shift‐share instruments," Journal of Regional Science, Wiley Blackwell, vol. 60(4), pages 677-711, September.
    6. Anatolyev, Stanislav & Sølvsten, Mikkel, 2023. "Testing many restrictions under heteroskedasticity," Journal of Econometrics, Elsevier, vol. 236(1).
    7. Michal Kolesár & Raj Chetty & John Friedman & Edward Glaeser & Guido W. Imbens, 2015. "Identification and Inference With Many Invalid Instruments," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(4), pages 474-484, October.
    8. Zhenhong Huang & Chen Wang & Jianfeng Yao, 2023. "A specification test for the strength of instrumental variables," Papers 2302.14396, arXiv.org.
    9. Kirill S. Evdokimov & Michal Kolesár, 2018. "Inference in Instrumental Variable Regression Analysis with Heterogeneous Treatment Effects," Working Papers 2018-16, Princeton University. Economics Department..
    10. Muhammad Qasim, 2024. "A weighted average limited information maximum likelihood estimator," Statistical Papers, Springer, vol. 65(5), pages 2641-2666, July.
    11. Kolesár, Michal, 2018. "Minimum distance approach to inference with many instruments," Journal of Econometrics, Elsevier, vol. 204(1), pages 86-100.
    12. Paul Goldsmith-Pinkham & Isaac Sorkin & Henry Swift, 2018. "Bartik Instruments: What, When, Why, and How," NBER Working Papers 24408, National Bureau of Economic Research, Inc.
    13. Eugenio Levi & Isabelle Sin & Steven Stillman, 2024. "The lasting impact of external shocks on political opinions and populist voting," Economic Inquiry, Western Economic Association International, vol. 62(1), pages 349-374, January.
    14. Helmut Farbmacher & Rebecca Groh & Michael Muhlegger & Gabriel Vollert, 2024. "Revisiting the Many Instruments Problem using Random Matrix Theory," Papers 2408.08580, arXiv.org.

  9. Stanislav Anatolyev & Grigory Kosenok, 2011. "Sequential Testing with Uniformly Distributed Size," Working Papers w0123, New Economic School (NES).

    Cited by:

    1. Sven Otto & Jorg Breitung, 2020. "Backward CUSUM for Testing and Monitoring Structural Change with an Application to COVID-19 Pandemic Data," Papers 2003.02682, arXiv.org, revised Mar 2022.
    2. Josua Gösmann & Tobias Kley & Holger Dette, 2021. "A new approach for open‐end sequential change point monitoring," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(1), pages 63-84, January.
    3. Otto, Sven & Breitung, Jörg, 2020. "Backward CUSUM for Testing and Monitoring Structural Change," VfS Annual Conference 2020 (Virtual Conference): Gender Economics 224533, Verein für Socialpolitik / German Economic Association.

  10. Stanislav Anatolyev & Natalia Kryzhanovskaya, 2009. "Directional Prediction of Returns under Asymmetric Loss: Direct and Indirect Approaches," Working Papers w0136, New Economic School (NES).

    Cited by:

    1. Roch, Oriol, 2013. "Histogram-based prediction of directional price relatives," Finance Research Letters, Elsevier, vol. 10(3), pages 110-115.

  11. Stanislav Anatolyev, 2009. "Inference in Regression Models with Many Regressors," Working Papers w0125, New Economic School (NES).

    Cited by:

    1. Patrick Richard, 2014. "Bootstrap tests in linear models with many regressors," Cahiers de recherche 14-06, Departement d'économique de l'École de gestion à l'Université de Sherbrooke.
    2. Abhimanyu Gupta & Myung Hwan Seo, 2019. "Robust Inference on Infinite and Growing Dimensional Time Series Regression," Papers 1911.08637, arXiv.org, revised Apr 2023.
    3. Mayer, Alexander, 2022. "On the local power of some tests of strict exogeneity in linear fixed effects models," Econometrics and Statistics, Elsevier, vol. 24(C), pages 49-74.
    4. Stanislav Anatolyev & Anna Mikusheva, 2018. "Factor models with many assets: strong factors, weak factors, and the two-pass procedure," Papers 1807.04094, arXiv.org, revised Apr 2019.
    5. Ian Martin & Stefan Nagel, 2019. "Market Efficiency in the Age of Big Data," CESifo Working Paper Series 8015, CESifo.
    6. Olivier Ledoit & Michael Wolf, 2013. "Spectrum estimation: a unified framework for covariance matrix estimation and PCA in large dimensions," ECON - Working Papers 105, Department of Economics - University of Zurich, revised Jul 2013.
    7. Anatolyev, Stanislav & Sølvsten, Mikkel, 2023. "Testing many restrictions under heteroskedasticity," Journal of Econometrics, Elsevier, vol. 236(1).
    8. Westerlund, Joakim, 2019. "Testing additive versus interactive effects in fixed-T panels," Economics Letters, Elsevier, vol. 174(C), pages 5-8.
    9. Hongchang Hu & Weifu Hu & Xinxin Yu, 2021. "Pseudo-maximum likelihood estimators in linear regression models with fractional time series," Statistical Papers, Springer, vol. 62(2), pages 639-659, April.
    10. Patrick Kline & Raffaele Saggio & Mikkel S{o}lvsten, 2018. "Leave-out estimation of variance components," Papers 1806.01494, arXiv.org, revised Aug 2019.
    11. Riccardo D'Adamo, 2018. "Cluster-Robust Standard Errors for Linear Regression Models with Many Controls," Papers 1806.07314, arXiv.org, revised Apr 2019.
    12. Calhoun, Gray, 2010. "Hypothesis Testing in Linear Regression when K/N is Large," Staff General Research Papers Archive 32216, Iowa State University, Department of Economics.
    13. Laurini, Márcio Poletti & Sanvicente, Antônio Zoratto & Monteiro, Rogério da Costa, 2011. "Generalized Tests of Investment Fund Performance," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 31(2), December.
    14. Wang, Wenjie & Kaffo, Maximilien, 2016. "Bootstrap inference for instrumental variable models with many weak instruments," Journal of Econometrics, Elsevier, vol. 192(1), pages 231-268.
    15. Stanislav Anatolyev, 2012. "Instrumental variables estimation and inference in the presence of many exogenous regressors," Working Papers w0162, Center for Economic and Financial Research (CEFIR).
    16. Zhenhong Huang & Chen Wang & Jianfeng Yao, 2023. "The First-stage F Test with Many Weak Instruments," Papers 2302.14423, arXiv.org, revised Sep 2024.
    17. Dante Amengual & Luca Repetto, 2014. "Testing a Large Number of Hypotheses in Approximate Factor Models," Working Papers wp2014_1410, CEMFI.
    18. Abhimanyu Gupta & Xi Qu, 2021. "Consistent specification testing under spatial dependence," Papers 2101.10255, arXiv.org, revised Aug 2022.
    19. Richard, Patrick, 2019. "Residual bootstrap tests in linear models with many regressors," Journal of Econometrics, Elsevier, vol. 208(2), pages 367-394.

  12. Stanislav Anatolyev & Nikolay Gospodinov, 2008. "Specification Testing in Models with Many Instruments," Working Papers w0124, New Economic School (NES).

    Cited by:

    1. Pierre Chaussé, 2011. "Generalized empirical likelihood for a continuum of moment conditions," Working Papers 1104, University of Waterloo, Department of Economics, revised Oct 2011.
    2. Wenjie Wang, 2012. "Bootstrapping Anderson-Rubin Statistic and J Statistic in Linear IV Models with Many Instruments," KIER Working Papers 810, Kyoto University, Institute of Economic Research.
    3. Kaffo, Maximilien & Wang, Wenjie, 2017. "On bootstrap validity for specification testing with many weak instruments," Economics Letters, Elsevier, vol. 157(C), pages 107-111.
    4. Dennis Lim & Wenjie Wang & Yichong Zhang, 2022. "A Conditional Linear Combination Test with Many Weak Instruments," Papers 2207.11137, arXiv.org, revised Apr 2023.
    5. Dick, Christian D. & Schmeling, Maik & Schrimpf, Andreas, 2010. "Macro expectations, aggregate uncertainty, and expected term premia," ZEW Discussion Papers 10-064, ZEW - Leibniz Centre for European Economic Research.
    6. Norman R. Swanson & John C. Chao & Jerry A. Hausman & Whitney K. Newey & Tiemen Woutersen, 2011. "Testing Overidentifying Restrictions with Many Instruments and Heteroskedasticity," Departmental Working Papers 201118, Rutgers University, Department of Economics.
    7. Eric Gautier & Christiern Rose, 2022. "Fast, Robust Inference for Linear Instrumental Variables Models using Self-Normalized Moments," Papers 2211.02249, arXiv.org, revised Nov 2022.
    8. Hyunseok Jung & Xiaodong Liu, 2023. "Testing for Peer Effects without Specifying the Network Structure," Papers 2306.09806, arXiv.org, revised Jul 2024.
    9. Kohtaro Hitomi & Masamune Iwasawa & Yoshihiko Nishiyama, 2022. "Optimal minimax rates against nonsmooth alternatives [Optimal testing for additivity in multiple nonparametric regression]," The Econometrics Journal, Royal Economic Society, vol. 25(2), pages 322-339.
    10. Federico Crudu & Giovanni Mellace & Zsolt Sandor, 2017. "Inference in instrumental variables models with heteroskedasticity and many instruments," Department of Economics University of Siena 761, Department of Economics, University of Siena.
    11. Anatolyev, Stanislav & Sølvsten, Mikkel, 2023. "Testing many restrictions under heteroskedasticity," Journal of Econometrics, Elsevier, vol. 236(1).
    12. Michal Kolesár & Raj Chetty & John Friedman & Edward Glaeser & Guido W. Imbens, 2015. "Identification and Inference With Many Invalid Instruments," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(4), pages 474-484, October.
    13. Díaz Antonia & Puch Luis A., 2019. "Investment, technological progress and energy efficiency," The B.E. Journal of Macroeconomics, De Gruyter, vol. 19(2), pages 1-28, June.
    14. Marine Carrasco & Mohamed Doukali, 2022. "Testing overidentifying restrictions with many instruments and heteroscedasticity using regularised jackknife IV," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 71-97.
    15. Wang, Wenjie, 2022. "Wild bootstrap test of overidentification with many instruments and heteroskedasticity," MPRA Paper 115168, University Library of Munich, Germany.
    16. Tom Boot & Johannes W. Ligtenberg, 2023. "Identification- and many instrument-robust inference via invariant moment conditions," Papers 2303.07822, arXiv.org, revised Sep 2023.
    17. Zhenhong Huang & Chen Wang & Jianfeng Yao, 2023. "A specification test for the strength of instrumental variables," Papers 2302.14396, arXiv.org.
    18. Yoonseok Lee & Ryo Okui, 2009. "A Specification Test for Instrumental Variables Regression with Many Instruments," Cowles Foundation Discussion Papers 1741, Cowles Foundation for Research in Economics, Yale University.
    19. Abutaliev, Albert & Anatolyev, Stanislav, 2013. "Asymptotic variance under many instruments: Numerical computations," Economics Letters, Elsevier, vol. 118(2), pages 272-274.
    20. Anna Mikusheva & Liyang Sun, 2020. "Inference with Many Weak Instruments," Papers 2004.12445, arXiv.org, revised Oct 2021.
    21. Guy Tchuente, 2021. "A Note on the Topology of the First Stage of 2SLS with Many Instruments," Papers 2106.15003, arXiv.org.
    22. Johannes W. Ligtenberg, 2023. "Inference in IV models with clustered dependence, many instruments and weak identification," Papers 2306.08559, arXiv.org, revised Mar 2024.
    23. Kolesár, Michal, 2018. "Minimum distance approach to inference with many instruments," Journal of Econometrics, Elsevier, vol. 204(1), pages 86-100.
    24. Marine Carrasco & Guy Tchuente, 2016. "Regularization Based Anderson Rubin Tests for Many Instruments," Studies in Economics 1608, School of Economics, University of Kent.
    25. Kohtaro Hitomi & Masamune Iwasawa & Yoshihiko Nishiyama, 2018. "Rate Optimal Specification Test When the Number of Instruments is Large," KIER Working Papers 986, Kyoto University, Institute of Economic Research.
    26. Travaglini, Guido, 2010. "Dynamic Econometric Testing of Climate Change and of its Causes," MPRA Paper 23600, University Library of Munich, Germany.
    27. Lee, Yoonseok & Okui, Ryo, 2012. "Hahn–Hausman test as a specification test," Journal of Econometrics, Elsevier, vol. 167(1), pages 133-139.
    28. Jiawei Fu, 2024. "Extracting Mechanisms from Heterogeneous Effects: An Identification Strategy for Mediation Analysis," Papers 2403.04131, arXiv.org, revised Oct 2024.
    29. Max-Sebastian Dov`i, 2021. "Inference on the New Keynesian Phillips Curve with Very Many Instrumental Variables," Papers 2101.09543, arXiv.org, revised Mar 2021.
    30. Lim, Dennis & Wang, Wenjie & Zhang, Yichong, 2024. "A conditional linear combination test with many weak instruments," Journal of Econometrics, Elsevier, vol. 238(2).
    31. Qingliang Fan & Zijian Guo & Ziwei Mei, 2022. "A Heteroskedasticity-Robust Overidentifying Restriction Test with High-Dimensional Covariates," Papers 2205.00171, arXiv.org, revised May 2024.
    32. Wang, Wenjie & Kaffo, Maximilien, 2016. "Bootstrap inference for instrumental variable models with many weak instruments," Journal of Econometrics, Elsevier, vol. 192(1), pages 231-268.
    33. Stanislav Anatolyev, 2012. "Instrumental variables estimation and inference in the presence of many exogenous regressors," Working Papers w0162, Center for Economic and Financial Research (CEFIR).
    34. Atsushi Inoue & Barbara Rossi, 2015. "Tests for the validity of portfolio or group choice in financial and panel regressions," Economics Working Papers 1523, Department of Economics and Business, Universitat Pompeu Fabra.
    35. Wang, Wenjie, 2020. "On Bootstrap Validity for the Test of Overidentifying Restrictions with Many Instruments and Heteroskedasticity," MPRA Paper 104858, University Library of Munich, Germany.
    36. Zhenhong Huang & Chen Wang & Jianfeng Yao, 2023. "The First-stage F Test with Many Weak Instruments," Papers 2302.14423, arXiv.org, revised Sep 2024.

  13. Stanislav Anatolyev & Nikolay Gospodinov, 2007. "Modeling Financial Return Dynamics by Decomposition," Working Papers w0095, New Economic School (NES).

    Cited by:

    1. Haibin Xie & Yuying Sun & Pengying Fan, 2023. "Return direction forecasting: a conditional autoregressive shape model with beta density," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-16, December.
    2. Stanislav Anatolyev & Natalia Kryzhanovskaya, 2009. "Directional Prediction of Returns under Asymmetric Loss: Direct and Indirect Approaches," Working Papers w0136, Center for Economic and Financial Research (CEFIR).
    3. Thomakos, Dimitrios D. & Wang, Tao, 2010. "'Optimal' probabilistic and directional predictions of financial returns," Journal of Empirical Finance, Elsevier, vol. 17(1), pages 102-119, January.

  14. Stanislav Anatolyev, 2007. "Inference about predictive ability when there are many predictors," Working Papers w0096, New Economic School (NES).

    Cited by:

    1. Clark, Todd & McCracken, Michael, 2013. "Advances in Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1107-1201, Elsevier.
    2. Calhoun, Gray, 2014. "Out-Of-Sample Comparisons of Overfit Models," Staff General Research Papers Archive 32462, Iowa State University, Department of Economics.
    3. Ron Alquist & Lutz Kilian & Robert Vigfusson, 2011. "Forecasting the Price of Oil," Staff Working Papers 11-15, Bank of Canada.

  15. Stanislav Anatolyev & Dmitry Shakin, 2006. "Trade intensity in the Russian stock market:dynamics, distribution and determinants," Working Papers w0070, New Economic School (NES).

    Cited by:

    1. Zhi-Qiang Jiang & Wei Chen & Wei-Xing Zhou, 2008. "Detrended fluctuation analysis of intertrade durations," Papers 0806.2444, arXiv.org.
    2. Alexander Muravyev, 2009. "Dual Class Stock in Russia: Explaining a Pricing Anomaly," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 45(2), pages 21-43, March.
    3. Yong-Ping Ruan & Wei-Xing Zhou, 2010. "Long-term correlations and multifractal nature in the intertrade durations of a liquid Chinese stock and its warrant," Papers 1008.0160, arXiv.org.
    4. Kovačić, Zlatko, 2007. "Forecasting volatility: Evidence from the Macedonian stock exchange," MPRA Paper 5319, University Library of Munich, Germany.
    5. Dionne, Georges & Pacurar, Maria & Zhou, Xiaozhou, 2015. "Liquidity-adjusted Intraday Value at Risk modeling and risk management: An application to data from Deutsche Börse," Journal of Banking & Finance, Elsevier, vol. 59(C), pages 202-219.
    6. Anatolyev, Stanislav, 2008. "A 10-year retrospective on the determinants of Russian stock returns," Research in International Business and Finance, Elsevier, vol. 22(1), pages 56-67, January.
    7. Denisa Georgiana Banulescu & Gilbert Colletaz & Christophe Hurlin & Sessi Tokpavi, 2013. "High-Frequency Risk Measures," Working Papers halshs-00859456, HAL.
    8. Dionne, Georges & Duchesne, Pierre & Pacurar, Maria, 2005. "Intraday Value at Risk (IVaR) using tick-by-tick data with application to the Toronto Stock Exchange," Working Papers 05-9, HEC Montreal, Canada Research Chair in Risk Management.
    9. Stanislav Anatolyev, 2013. "Objects of nonstructural time series modeling (in Russian)," Quantile, Quantile, issue 11, pages 1-12, December.

  16. Stanislav Anatolyev, 2006. "Dynamic modeling under linear-exponential loss," Working Papers w0092, New Economic School (NES).

    Cited by:

    1. Liu, Xiaochun, 2011. "Modeling the time-varying skewness via decomposition for out-of-sample forecast," MPRA Paper 41248, University Library of Munich, Germany.
    2. Araichi, Sawssen & Peretti, Christian de & Belkacem, Lotfi, 2016. "Solvency capital requirement for a temporal dependent losses in insurance," Economic Modelling, Elsevier, vol. 58(C), pages 588-598.
    3. Stanislav Anatolyev & Natalia Kryzhanovskaya, 2009. "Directional Prediction of Returns under Asymmetric Loss: Direct and Indirect Approaches," Working Papers w0136, Center for Economic and Financial Research (CEFIR).

  17. Stanislav Anatolyev & Grigory Kosenok, 2006. "Tests in contingency tables as regression tests," Working Papers w0075, New Economic School (NES).

    Cited by:

    1. Sentana, Juan, 2022. "Tests for independence between categorical variables," Economics Letters, Elsevier, vol. 220(C).

  18. Stanislav Anatolyev, 2006. "Nonparametric retrospection and monitoring of predictability of financial returns," Working Papers w0071, New Economic School (NES).

    Cited by:

    1. Kulikova, Maria V. & Taylor, David R. & Kulikov, Gennady Yu., 2024. "Evolving efficiency of the BRICS markets," Economic Systems, Elsevier, vol. 48(1).
    2. Alenka Kavkler & Mejra Festić, 2011. "Modelling Stock Exchange Index Returns in Different GDP Growth Regimes," Prague Economic Papers, Prague University of Economics and Business, vol. 2011(1), pages 3-22.
    3. Kovačić, Zlatko, 2007. "Forecasting volatility: Evidence from the Macedonian stock exchange," MPRA Paper 5319, University Library of Munich, Germany.
    4. Kian-Ping Lim & Weiwei Luo & Jae H. Kim, 2013. "Are US stock index returns predictable? Evidence from automatic autocorrelation-based tests," Applied Economics, Taylor & Francis Journals, vol. 45(8), pages 953-962, March.
    5. Pierre Perron & Eduardo Zorita & Eiji Kurozumi, 2017. "Monitoring Parameter Constancy with Endogenous Regressors," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(5), pages 791-805, September.

  19. Stanislav Anatolyev, 2005. "Optimal Instruments in Time Series: A Survey," Working Papers w0069, New Economic School (NES).

    Cited by:

    1. Stanislav Anatolyev, 2007. "Optimal instruments (in Russian)," Quantile, Quantile, issue 2, pages 61-69, March.

  20. West,K.D. & Wong,K.-F. & Anatolyev,S., 2001. "Instrumental variables estimation of heteroskedastic linear models using all lags of instruments," Working papers 20, Wisconsin Madison - Social Systems.

    Cited by:

    1. Salem Abo-Zaid, 2021. "Taxation, credit frictions and the cyclical behavior of the labor wedge," Empirical Economics, Springer, vol. 60(4), pages 1777-1816, April.
    2. Jeremy Tobacman & David Laibson & Andrea Repetto, 2007. "Estimating Discount Functions with Consumption Choices over the Lifecycle," Economics Series Working Papers 341, University of Oxford, Department of Economics.
    3. Salem Abo-Zaid & Anastasia Zervou, 2016. "Financing of Firms, Labor Reallocation and the Distributional Role of Monetary Policy," Working Papers 20161020_001, Texas A&M University, Department of Economics.
    4. Hansen, Lars Peter, 2013. "Uncertainty Outside and Inside Economic Models," Nobel Prize in Economics documents 2013-7, Nobel Prize Committee.
    5. Elena Corallo, 2005. "The effect of the war risk: a comparison of the consequences of the two Iraq wars on some financial variables," LIUC Papers in Economics 171, Cattaneo University (LIUC).
    6. Lars Peter Hansen, 2014. "Nobel Lecture: Uncertainty Outside and Inside Economic Models," Journal of Political Economy, University of Chicago Press, vol. 122(5), pages 945-987.
    7. Stanislav Anatolyev, 2005. "Optimal Instruments in Time Series: A Survey," Working Papers w0069, Center for Economic and Financial Research (CEFIR).
    8. Alastair R. Hall, 2015. "Econometricians Have Their Moments: GMM at 32," The Economic Record, The Economic Society of Australia, vol. 91(S1), pages 1-24, June.
    9. Gospodinov, Nikolay & Otsu, Taisuke, 2012. "Local GMM estimation of time series models with conditional moment restrictions," Journal of Econometrics, Elsevier, vol. 170(2), pages 476-490.
    10. Arellano, Manuel, 2016. "Modelling optimal instrumental variables for dynamic panel data models," Research in Economics, Elsevier, vol. 70(2), pages 238-261.
    11. West, Kenneth D., 2002. "Efficient GMM estimation of weak AR processes," Economics Letters, Elsevier, vol. 75(3), pages 415-418, May.
    12. Okui, Ryo, 2011. "Instrumental variable estimation in the presence of many moment conditions," Journal of Econometrics, Elsevier, vol. 165(1), pages 70-86.
    13. Kuersteiner, Guido M., 2012. "Kernel-weighted GMM estimators for linear time series models," Journal of Econometrics, Elsevier, vol. 170(2), pages 399-421.

  21. West,K.D. & Wong,K.F. & Anatolyev,S., 1999. "Feasible optimal instrumental variables estimation of linear models with moving average disturbances," Working papers 1, Wisconsin Madison - Social Systems.

    Cited by:

    1. Grammig, Joachim & Wellner, Marc, 2002. "Modeling the interdependence of volatility and inter-transaction duration processes," Journal of Econometrics, Elsevier, vol. 106(2), pages 369-400, February.

Articles

  1. Anatolyev, Stanislav & Sølvsten, Mikkel, 2023. "Testing many restrictions under heteroskedasticity," Journal of Econometrics, Elsevier, vol. 236(1).
    See citations under working paper version above.
  2. Anatolyev, Stanislav & Mikusheva, Anna, 2022. "Factor models with many assets: Strong factors, weak factors, and the two-pass procedure," Journal of Econometrics, Elsevier, vol. 229(1), pages 103-126.
    See citations under working paper version above.
  3. Anatolyev, Stanislav & Mikusheva, Anna, 2021. "Limit Theorems For Factor Models," Econometric Theory, Cambridge University Press, vol. 37(5), pages 1034-1074, October.
    See citations under working paper version above.
  4. Anatolyev Stanislav, 2019. "Volatility filtering in estimation of kurtosis (and variance)," Dependence Modeling, De Gruyter, vol. 7(1), pages 1-23, February.

    Cited by:

    1. Rustam Ibragimov & Jihyun Kim & Anton Skrobotov, 2020. "New robust inference for predictive regressions," Papers 2006.01191, arXiv.org, revised Mar 2023.
    2. Walter Distaso & Rustam Ibragimov & Alexander Semenov & Anton Skrobotov, 2020. "COVID-19: Tail Risk and Predictive Regressions," Papers 2009.02486, arXiv.org, revised Oct 2021.
    3. Carnero, M. Angeles & León, Angel & Ñíguez, Trino-Manuel, 2023. "Skewness in energy returns: estimation, testing and retain-->implications for tail risk," The Quarterly Review of Economics and Finance, Elsevier, vol. 90(C), pages 178-189.

  5. Stanislav Anatolyev, 2019. "Many Instruments And/Or Regressors: A Friendly Guide," Journal of Economic Surveys, Wiley Blackwell, vol. 33(2), pages 689-726, April.

    Cited by:

    1. Abhimanyu Gupta & Myung Hwan Seo, 2019. "Robust Inference on Infinite and Growing Dimensional Time Series Regression," Papers 1911.08637, arXiv.org, revised Apr 2023.
    2. Mayer, Alexander, 2022. "On the local power of some tests of strict exogeneity in linear fixed effects models," Econometrics and Statistics, Elsevier, vol. 24(C), pages 49-74.
    3. Anatolyev, Stanislav & Sølvsten, Mikkel, 2023. "Testing many restrictions under heteroskedasticity," Journal of Econometrics, Elsevier, vol. 236(1).
    4. Wang, Wenjie, 2022. "Wild bootstrap test of overidentification with many instruments and heteroskedasticity," MPRA Paper 115168, University Library of Munich, Germany.
    5. Kuanhao Jiang & Rajarshi Mukherjee & Subhabrata Sen & Pragya Sur, 2022. "A New Central Limit Theorem for the Augmented IPW Estimator: Variance Inflation, Cross-Fit Covariance and Beyond," Papers 2205.10198, arXiv.org, revised Oct 2022.
    6. Mayer, Alexander, 2020. "(Consistently) testing strict exogeneity against the alternative of predeterminedness in linear time-series models," Economics Letters, Elsevier, vol. 193(C).
    7. Johannes W. Ligtenberg, 2023. "Inference in IV models with clustered dependence, many instruments and weak identification," Papers 2306.08559, arXiv.org, revised Mar 2024.
    8. Carlos Velasco & Xuexin Wang, 2021. "Instrumental variable estimation via a continuum of instruments with an application to estimating the elasticity of intertemporal substitution in consumption," Working Papers 2024-09-06, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    9. Itchoko M.M. Mwa Ndjokou, Prince Piva Asaloko, 2024. "Empirical verification of the link between the digital divide and women's economic participation in Africa," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 21(1), pages 123-164, June.
    10. Anna Mikusheva & Mikkel S{o}lvsten, 2023. "Linear Regression with Weak Exogeneity," Papers 2308.08958, arXiv.org, revised Jan 2024.
    11. Helmut Farbmacher & Rebecca Groh & Michael Muhlegger & Gabriel Vollert, 2024. "Revisiting the Many Instruments Problem using Random Matrix Theory," Papers 2408.08580, arXiv.org.
    12. Wang, Wenjie, 2020. "On Bootstrap Validity for the Test of Overidentifying Restrictions with Many Instruments and Heteroskedasticity," MPRA Paper 104858, University Library of Munich, Germany.

  6. Stanislav Anatolyev & Nikolay Gospodinov, 2019. "Multivariate Return Decomposition: Theory and Implications," Econometric Reviews, Taylor & Francis Journals, vol. 38(5), pages 487-508, May.
    See citations under working paper version above.
  7. Anatolyev, Stanislav & Baruník, Jozef, 2019. "Forecasting dynamic return distributions based on ordered binary choice," International Journal of Forecasting, Elsevier, vol. 35(3), pages 823-835.
    See citations under working paper version above.
  8. Stanislav Anatolyev & Alena Skolkova, 2019. "Many instruments: Implementation in Stata," Stata Journal, StataCorp LP, vol. 19(4), pages 849-866, December.

    Cited by:

    1. Li, Junqing & Yang, Zhiyuan & Liu, Kaifeng, 2024. "Research on contracting institutions and convergence," China Economic Review, Elsevier, vol. 84(C).
    2. Luca Bettarelli & Michela Cella & Giovanna Iannantuoni & Elena Manzoni, 2015. "It's a matter of confidence: Institutions, government stability and economic outcomes," Working Papers 309, University of Milano-Bicocca, Department of Economics, revised Sep 2015.

  9. Anatolyev, Stanislav, 2018. "Almost unbiased variance estimation in linear regressions with many covariates," Economics Letters, Elsevier, vol. 169(C), pages 20-23.

    Cited by:

    1. Anatolyev, Stanislav & Sølvsten, Mikkel, 2023. "Testing many restrictions under heteroskedasticity," Journal of Econometrics, Elsevier, vol. 236(1).
    2. Ng Cheuk Fai, 2022. "Robust Inference in High Dimensional Linear Model with Cluster Dependence," Papers 2212.05554, arXiv.org.

  10. Stanislav Anatolyev & Nikita Kobotaev, 2018. "Modeling and forecasting realized covariance matrices with accounting for leverage," Econometric Reviews, Taylor & Francis Journals, vol. 37(2), pages 114-139, February.
    See citations under working paper version above.
  11. Anatolyev Stanislav & Kosenok Grigory, 2018. "Sequential Testing with Uniformly Distributed Size," Journal of Time Series Econometrics, De Gruyter, vol. 10(2), pages 1-22, July.
    See citations under working paper version above.
  12. Anatolyev, Stanislav & Yaskov, Pavel, 2017. "Asymptotics Of Diagonal Elements Of Projection Matrices Under Many Instruments/Regressors," Econometric Theory, Cambridge University Press, vol. 33(3), pages 717-738, June.

    Cited by:

    1. Kaffo, Maximilien & Wang, Wenjie, 2017. "On bootstrap validity for specification testing with many weak instruments," Economics Letters, Elsevier, vol. 157(C), pages 107-111.
    2. Hyunseok Jung & Xiaodong Liu, 2023. "Testing for Peer Effects without Specifying the Network Structure," Papers 2306.09806, arXiv.org, revised Jul 2024.
    3. Dörnemann, Nina & Dette, Holger, 2023. "Fluctuations of the diagonal entries of a large sample precision matrix," Statistics & Probability Letters, Elsevier, vol. 198(C).
    4. Pavel Yaskov, 2018. "LLN for Quadratic Forms of Long Memory Time Series and Its Applications in Random Matrix Theory," Journal of Theoretical Probability, Springer, vol. 31(4), pages 2032-2055, December.
    5. Zhenhong Huang & Chen Wang & Jianfeng Yao, 2023. "A specification test for the strength of instrumental variables," Papers 2302.14396, arXiv.org.
    6. Max-Sebastian Dov`i & Anders Bredahl Kock & Sophocles Mavroeidis, 2022. "A Ridge-Regularised Jackknifed Anderson-Rubin Test," Papers 2209.03259, arXiv.org, revised Nov 2023.
    7. Byunghoon Kang, 2018. "Higher Order Approximation of IV Estimators with Invalid Instruments," Working Papers 257105320, Lancaster University Management School, Economics Department.
    8. Dörnemann, Nina, 2023. "Likelihood ratio tests under model misspecification in high dimensions," Journal of Multivariate Analysis, Elsevier, vol. 193(C).
    9. Boot, Tom, 2023. "Joint inference based on Stein-type averaging estimators in the linear regression model," Journal of Econometrics, Elsevier, vol. 235(2), pages 1542-1563.
    10. Zhenhong Huang & Chen Wang & Jianfeng Yao, 2023. "The First-stage F Test with Many Weak Instruments," Papers 2302.14423, arXiv.org, revised Sep 2024.
    11. Richard, Patrick, 2019. "Residual bootstrap tests in linear models with many regressors," Journal of Econometrics, Elsevier, vol. 208(2), pages 367-394.

  13. Anatolyev, Stanislav & Gospodinov, Nikolay & Jamali, Ibrahim & Liu, Xiaochun, 2017. "Foreign exchange predictability and the carry trade: A decomposition approach," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 199-211.

    Cited by:

    1. Stanislav Anatolyev, 2021. "Directional news impact curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 94-107, January.
    2. Qian Zhang & Kuo-Jui Wu & Ming-Lang Tseng, 2019. "Exploring Carry Trade and Exchange Rate toward Sustainable Financial Resources: An application of the Artificial Intelligence UKF Method," Sustainability, MDPI, vol. 11(12), pages 1-26, June.
    3. Lumengo Bonga-Bonga & Tebogo Maake, 2021. "The Relationship between Carry Trade and Asset Markets in South Africa," JRFM, MDPI, vol. 14(7), pages 1-13, July.
    4. Jamali, Ibrahim & Yamani, Ehab, 2019. "Out-of-sample exchange rate predictability in emerging markets: Fundamentals versus technical analysis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 61(C), pages 241-263.
    5. Fu, Hsuan & Luger, Richard, 2022. "Multiple testing of the forward rate unbiasedness hypothesis across currencies," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 232-245.

  14. Stanislav Anatolyev & Anton Petukhov, 2016. "Uncovering the Skewness News Impact Curve," Journal of Financial Econometrics, Oxford University Press, vol. 14(4), pages 746-771.

    Cited by:

    1. Stanislav Anatolyev, 2021. "Directional news impact curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 94-107, January.
    2. Trung H. Le & Apostolos Kourtis & Raphael Markellos, 2023. "Modeling skewness in portfolio choice," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(6), pages 734-770, June.
    3. Ñíguez, Trino-Manuel & Perote, Javier, 2017. "Moments expansion densities for quantifying financial risk," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 53-69.
    4. Trung H. Le, 2024. "Forecasting VaR and ES in emerging markets: The role of time‐varying higher moments," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 402-414, March.
    5. León, Ángel & Ñíguez, Trino-Manuel, 2020. "Modeling asset returns under time-varying semi-nonparametric distributions," Journal of Banking & Finance, Elsevier, vol. 118(C).
    6. Le, Trung H., 2020. "Forecasting value at risk and expected shortfall with mixed data sampling," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1362-1379.
    7. León, Ángel & Ñíguez, Trino-Manuel, 2021. "The transformed Gram Charlier distribution: Parametric properties and financial risk applications," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 323-349.

  15. Stanislav Anatolyev & Stanislav Khrapov, 2015. "Right on Target, or Is it? The Role of Distributional Shape in Variance Targeting," Econometrics, MDPI, vol. 3(3), pages 1-23, August.

    Cited by:

    1. Anatolyev Stanislav, 2019. "Volatility filtering in estimation of kurtosis (and variance)," Dependence Modeling, De Gruyter, vol. 7(1), pages 1-23, February.

  16. Anatolyev, Stanislav & Khabibullin, Renat & Prokhorov, Artem, 2014. "An algorithm for constructing high dimensional distributions from distributions of lower dimension," Economics Letters, Elsevier, vol. 123(3), pages 257-261.

    Cited by:

    1. Matsypura, Dmytro & Neo, Emily & Prokhorov, Artem, 2016. "Estimation of Hierarchical Archimedean Copulas as a Shortest Path Prob lem," Working Papers 2123/14745, University of Sydney Business School, Discipline of Business Analytics.

  17. Stanislav Anatolyev, 2013. "Instrumental variables estimation and inference in the presence of many exogenous regressors," Econometrics Journal, Royal Economic Society, vol. 16(1), pages 27-72, February.
    See citations under working paper version above.
  18. Anatolyev, Stanislav, 2012. "Inference in regression models with many regressors," Journal of Econometrics, Elsevier, vol. 170(2), pages 368-382.
    See citations under working paper version above.
  19. Anatolyev, Stanislav & Kosenok, Grigory, 2012. "Another Numerical Method Of Finding Critical Values For The Andrews Stability Test," Econometric Theory, Cambridge University Press, vol. 28(1), pages 239-246, February.

    Cited by:

    1. Carlos Castro & Stijn Ferrari, 2012. "Measuring and testing for the systemically important financial institutions," Working Paper Research 228, National Bank of Belgium.
    2. Stanislav Anatolyev & Grigory Kosenok, 2011. "Sequential Testing with Uniformly Distributed Size," Working Papers w0123, New Economic School (NES).
    3. Christis Katsouris, 2023. "Predictability Tests Robust against Parameter Instability," Papers 2307.15151, arXiv.org.

  20. Anatolyev, Stanislav & Gospodinov, Nikolay, 2011. "Specification Testing In Models With Many Instruments," Econometric Theory, Cambridge University Press, vol. 27(2), pages 427-441, April.
    See citations under working paper version above.
  21. Anatolyev, Stanislav & Gospodinov, Nikolay, 2010. "Modeling Financial Return Dynamics via Decomposition," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(2), pages 232-245.

    Cited by:

    1. Stanislav Anatolyev, 2021. "Directional news impact curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 94-107, January.
    2. Hambuckers, J. & Ulm, M., 2023. "On the role of interest rate differentials in the dynamic asymmetry of exchange rates," Economic Modelling, Elsevier, vol. 129(C).
    3. Frazier, David T. & Liu, Xiaochun, 2016. "A new approach to risk-return trade-off dynamics via decomposition," Journal of Economic Dynamics and Control, Elsevier, vol. 62(C), pages 43-55.
    4. Lee Tae-Hwy & Wang He & Xi Zhou & Zhang Ru, 2023. "Density Forecast of Financial Returns Using Decomposition and Maximum Entropy," Journal of Econometric Methods, De Gruyter, vol. 12(1), pages 57-83, January.
    5. Liu, Jingzhen, 2019. "Impacts of lagged returns on the risk-return relationship of Chinese aggregate stock market: Evidence from different data frequencies," Research in International Business and Finance, Elsevier, vol. 48(C), pages 243-257.
    6. Nyberg, Henri & Pönkä, Harri, 2016. "International sign predictability of stock returns: The role of the United States," Economic Modelling, Elsevier, vol. 58(C), pages 323-338.
    7. Pönkä, Harri, 2014. "Predicting the direction of US stock markets using industry returns," MPRA Paper 62942, University Library of Munich, Germany.
    8. Algieri, Bernardina & Leccadito, Arturo, 2019. "Ask CARL: Forecasting tail probabilities for energy commodities," Energy Economics, Elsevier, vol. 84(C).
    9. Nyberg, Henri, 2011. "Forecasting the direction of the US stock market with dynamic binary probit models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 561-578.
    10. Hadhri, Sinda & Ftiti, Zied, 2017. "Stock return predictability in emerging markets: Does the choice of predictors and models matter across countries?," Research in International Business and Finance, Elsevier, vol. 42(C), pages 39-60.
    11. Gu, Wentao & Peng, Yiqing, 2019. "Forecasting the market return direction based on a time-varying probability density model," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    12. James W. Taylor & Keming Yu, 2016. "Using auto-regressive logit models to forecast the exceedance probability for financial risk management," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(4), pages 1069-1092, October.
    13. Stanislav Anatolyev & Nikolay Gospodinov & Ibrahim Jamali & Xiaochun Liu, 2015. "Foreign exchange predictability during the financial crisis: implications for carry trade profitability," FRB Atlanta Working Paper 2015-6, Federal Reserve Bank of Atlanta.
    14. Liu, Xiaochun, 2011. "Modeling the time-varying skewness via decomposition for out-of-sample forecast," MPRA Paper 41248, University Library of Munich, Germany.
    15. Bertrand Candelon & Jameel Ahmed & Stefan Straetmans, 2014. "Predicting and Capitalizing on Stock Market Bears in the U.S," Working Papers 2014-409, Department of Research, Ipag Business School.
    16. Stanislav Anatolyev & Nikolay Gospodinov, 2019. "Multivariate Return Decomposition: Theory and Implications," Econometric Reviews, Taylor & Francis Journals, vol. 38(5), pages 487-508, May.
    17. Liu, Jiadong & Papailias, Fotis & Quinn, Barry, 2021. "Direction-of-change forecasting in commodity futures markets," International Review of Financial Analysis, Elsevier, vol. 74(C).
    18. Bernardina Algieri & Arturo Leccadito, 2020. "CARL and His POT: Measuring Risks in Commodity Markets," Risks, MDPI, vol. 8(1), pages 1-15, March.
    19. Liu, Xiaochun, 2017. "Unfolded risk-return trade-offs and links to Macroeconomic Dynamics," Journal of Banking & Finance, Elsevier, vol. 82(C), pages 1-19.
    20. Thomas Bury, 2013. "Predicting trend reversals using market instantaneous state," Papers 1310.8169, arXiv.org, revised Mar 2014.
    21. Lei, Heng & Xue, Minggao & Liu, Huiling, 2022. "Probability distribution forecasting of carbon allowance prices: A hybrid model considering multiple influencing factors," Energy Economics, Elsevier, vol. 113(C).
    22. Stanislav Anatolyev & Natalia Kryzhanovskaya, 2009. "Directional Prediction of Returns under Asymmetric Loss: Direct and Indirect Approaches," Working Papers w0136, Center for Economic and Financial Research (CEFIR).
    23. Ginker, Tim & Lieberman, Offer, 2017. "Robustness of binary choice models to conditional heteroscedasticity," Economics Letters, Elsevier, vol. 150(C), pages 130-134.
    24. Nikolay Gospodinov, 2017. "Asset Co-movements: Features and Challenges," FRB Atlanta Working Paper 2017-11, Federal Reserve Bank of Atlanta.
    25. de Resende, Charlene C. & Pereira, Adriano C.M. & Cardoso, Rodrigo T.N. & de Magalhães, A.R. Bosco, 2017. "Investigating market efficiency through a forecasting model based on differential equations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 199-212.
    26. Riza Erdugan & Nada Kulendran & Riccardo Natoli, 2019. "Incorporating financial market volatility to improve forecasts of directional changes in Australian share market returns," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 33(4), pages 417-445, December.
    27. Nyberg, Henri, 2013. "Predicting bear and bull stock markets with dynamic binary time series models," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3351-3363.
    28. Chen, Nan-Kuang & Chen, Shiu-Sheng & Chou, Yu-Hsi, 2013. "Further evidence on bear market predictability: The role of the external finance premium," MPRA Paper 49093, University Library of Munich, Germany.
    29. Stanislav Anatolyev & Jozef Barunik, 2017. "Forecasting dynamic return distributions based on ordered binary choice," Papers 1711.05681, arXiv.org, revised Jan 2019.
    30. Anatolyev, Stanislav & Gospodinov, Nikolay & Jamali, Ibrahim & Liu, Xiaochun, 2017. "Foreign exchange predictability and the carry trade: A decomposition approach," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 199-211.
    31. Andreea Röthig & Andreas Röthig & Carl Chiarella, 2015. "On Candlestick-based Trading Rules Profitability Analysis via Parametric Bootstraps and Multivariate Pair-Copula based Models," Research Paper Series 362, Quantitative Finance Research Centre, University of Technology, Sydney.
    32. Luis H. R. Alvarez E. & Paavo Salminen, 2017. "Timing in the presence of directional predictability: optimal stopping of skew Brownian motion," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 86(2), pages 377-400, October.
    33. Liu, Xiaochun, 2017. "Can macroeconomic dynamics explain the time variation of risk–return trade-offs in the U.S. financial market?," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 275-293.
    34. Stanislav Anatolyev, 2013. "Objects of nonstructural time series modeling (in Russian)," Quantile, Quantile, issue 11, pages 1-12, December.
    35. Nyberg, Henri, 2011. "Forecasting the direction of the US stock market with dynamic binary probit models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 561-578, April.
    36. Liu, Xiaochun & Luger, Richard, 2015. "Unfolded GARCH models," Journal of Economic Dynamics and Control, Elsevier, vol. 58(C), pages 186-217.
    37. Bury, Thomas, 2014. "Predicting trend reversals using market instantaneous state," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 404(C), pages 79-91.
    38. Yang, Minxian, 2019. "The risk return relationship: Evidence from index returns and realised variances," Journal of Economic Dynamics and Control, Elsevier, vol. 107(C), pages 1-1.

  22. Anatolyev, Stanislav & Kosenok, Grigory, 2009. "Tests in contingency tables as regression tests," Economics Letters, Elsevier, vol. 105(2), pages 189-192, November.
    See citations under working paper version above.
  23. Anatolyev, Stanislav, 2009. "Nonparametric Retrospection and Monitoring of Predictability of Financial Returns," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(2), pages 149-160.
    See citations under working paper version above.
  24. Anatolyev, Stanislav, 2009. "Dynamic modeling under linear-exponential loss," Economic Modelling, Elsevier, vol. 26(1), pages 82-89, January.
    See citations under working paper version above.
  25. Anatolyev Stanislav, 2009. "Multi-Market Direction-of-Change Modeling Using Dependence Ratios," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(1), pages 1-24, March.

    Cited by:

    1. Stanislav Anatolyev, 2021. "Directional news impact curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 94-107, January.
    2. Nyberg, Henri & Pönkä, Harri, 2016. "International sign predictability of stock returns: The role of the United States," Economic Modelling, Elsevier, vol. 58(C), pages 323-338.
    3. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    4. Stanislav Anatolyev & Nikolay Gospodinov, 2019. "Multivariate Return Decomposition: Theory and Implications," Econometric Reviews, Taylor & Francis Journals, vol. 38(5), pages 487-508, May.
    5. Stanislav Anatolyev & Natalia Kryzhanovskaya, 2009. "Directional Prediction of Returns under Asymmetric Loss: Direct and Indirect Approaches," Working Papers w0136, Center for Economic and Financial Research (CEFIR).
    6. Kajal Lahiri & Liu Yang, 2023. "Predicting binary outcomes based on the pair-copula construction," Empirical Economics, Springer, vol. 64(6), pages 3089-3119, June.
    7. Kajal Lahiri & Liu Yang, 2015. "A Non-linear Forecast Combination Procedure for Binary Outcomes," CESifo Working Paper Series 5175, CESifo.
    8. Stanislav Anatolyev, 2013. "Objects of nonstructural time series modeling (in Russian)," Quantile, Quantile, issue 11, pages 1-12, December.

  26. Kenneth West & Ka-fu Wong & Stanislav Anatolyev, 2009. "Instrumental Variables Estimation of Heteroskedastic Linear Models Using All Lags of Instruments," Econometric Reviews, Taylor & Francis Journals, vol. 28(5), pages 441-467.
    See citations under working paper version above.
  27. Anatolyev, Stanislav, 2008. "A 10-year retrospective on the determinants of Russian stock returns," Research in International Business and Finance, Elsevier, vol. 22(1), pages 56-67, January.

    Cited by:

    1. Alexander Muravyev, 2009. "Dual Class Stock in Russia: Explaining a Pricing Anomaly," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 45(2), pages 21-43, March.
    2. Manuel Hoffmann & Matthias Neuenkirch, 2017. "The pro-Russian conflict and its impact on stock returns in Russia and the Ukraine," International Economics and Economic Policy, Springer, vol. 14(1), pages 61-73, January.
    3. Delia-Elena Diaconaşu, 2015. "CENTRAL AND EASTERN EUROPEAN STOCK MARKETS IN TIMES OF CRISIS (International Conference "Recent Advances in Economic and Social Research", 13-14 mai 2015, București)," Institute for Economic Forecasting Conference Proceedings 151205, Institute for Economic Forecasting.
    4. Agata Lozinskaia & Anastasiia Saltykova, 2019. "Fundamental Factors Affecting The Moex Russia Index: Structural Break Detection In A Long-Term Time Series," HSE Working papers WP BRP 77/FE/2019, National Research University Higher School of Economics.
    5. Gelman, Sergey & Kliger, Doron, 2021. "The effect of time-induced stress on financial decision making in real markets: The case of traffic congestion," Journal of Economic Behavior & Organization, Elsevier, vol. 185(C), pages 814-841.
    6. Samson, Lucie, 2013. "Asset prices and exchange risk: Empirical evidence from Canada," Research in International Business and Finance, Elsevier, vol. 28(C), pages 35-44.
    7. Oleg N. Salmanov & Natalia V. Babina & Marina V. Samoshkina & Irina P. Drachena & Irina P. Salmanova, 2020. "The Effects Of Volatility And Changes In Conditional Correlations In The Stock Markets Of Russia And Developed Countries," Economic Annals, Faculty of Economics and Business, University of Belgrade, vol. 65(227), pages 67-94, October –.
    8. Middleton, C.A.J. & Fifield, S.G.M. & Power, D.M., 2008. "An investigation of the benefits of portfolio investment in Central and Eastern European stock markets," Research in International Business and Finance, Elsevier, vol. 22(2), pages 162-174, June.
    9. Apergis, Nicholas & Artikis, Panagiotis & Sorros, John, 2011. "Asset pricing and foreign exchange risk," Research in International Business and Finance, Elsevier, vol. 25(3), pages 308-328, September.
    10. Naresh, G. & Vasudevan, Gopala & Mahalakshmi, S. & Thiyagarajan, S., 2018. "Spillover effect of US dollar on the stock indices of BRICS," Research in International Business and Finance, Elsevier, vol. 44(C), pages 359-368.
    11. Babecký, Jan & Komárek, Lubos & Komárková, Zlatuse, 2012. "Integration of Chinese and Russian stock markets with world markets: National and sectoral Perspectives," BOFIT Discussion Papers 4/2012, Bank of Finland Institute for Emerging Economies (BOFIT).

  28. Stanislav Anatolyev, 2007. "Optimal instruments (in Russian)," Quantile, Quantile, issue 2, pages 61-69, March.

    Cited by:

    1. Ibragimov Marat & Jovlon Karimov & Elena Permyakova, 2013. "Unemployment and output dynamics in CIS countries: Okun's law revisited," EERC Working Paper Series 13/04e, EERC Research Network, Russia and CIS.

  29. Anatolyev, Stanislav, 2007. "Redundancy Of Lagged Regressors Revisited," Econometric Theory, Cambridge University Press, vol. 23(2), pages 364-368, April.

    Cited by:

    1. Hao, Bowen & Prokhorov, Artem & Qian, Hailong, 2018. "Moment redundancy test with application to efficiency-improving copulas," Economics Letters, Elsevier, vol. 171(C), pages 29-33.
    2. Carrasco, Marine & Florens, Jean-Pierre, 2014. "On The Asymptotic Efficiency Of Gmm," Econometric Theory, Cambridge University Press, vol. 30(2), pages 372-406, April.
    3. Stanislav Anatolyev, 2005. "Optimal Instruments in Time Series: A Survey," Working Papers w0069, Center for Economic and Financial Research (CEFIR).

  30. Stanislav Anatolyev & Victor Kitov, 2007. "Using All Observations when Forecasting under Structural Breaks," Finnish Economic Papers, Finnish Economic Association, vol. 20(2), pages 166-176, Autumn.

    Cited by:

    1. Inoue, Atsushi & Jin, Lu & Rossi, Barbara, 2017. "Rolling window selection for out-of-sample forecasting with time-varying parameters," Journal of Econometrics, Elsevier, vol. 196(1), pages 55-67.

  31. Stanislav Anatolyev, 2007. "Optimal Instruments In Time Series: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 21(1), pages 143-173, February.
    See citations under working paper version above.
  32. Stanislav Anatolyev, 2007. "The basics of bootstrapping (in Russian)," Quantile, Quantile, issue 3, pages 1-12, September.

    Cited by:

    1. Pavel Dovbnya, 2020. "Announcements of Sanctions and the Russian Equity Market: An Event Study Approach," Russian Journal of Money and Finance, Bank of Russia, vol. 79(1), pages 74-92, March.
    2. Parshakov, Petr, 2015. "Estimation of skill of Russian mutual fund managers," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 37(1), pages 57-66.
    3. Nazrullaeva, Eugenia, 2010. "Modeling the relationship between investment processes and costs structure applied to Russian economic activities in 2005-2009," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 19(3), pages 38-61.

  33. Anatolyev, Stanislav, 2006. "Kernel estimation under linear-exponential loss," Economics Letters, Elsevier, vol. 91(1), pages 39-43, April.

    Cited by:

    1. Anatolyev, Stanislav, 2009. "Dynamic modeling under linear-exponential loss," Economic Modelling, Elsevier, vol. 26(1), pages 82-89, January.

  34. Anatolyev, Stanislav & Gerko, Alexander, 2005. "A Trading Approach to Testing for Predictability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 455-461, October.

    Cited by:

    1. Pablo Pincheira, 2008. "Combining Tests of Predictive Ability Theory and Evidence for Chilean and Canadian Exchange Rates," Working Papers Central Bank of Chile 459, Central Bank of Chile.
    2. Stanislav Anatolyev, 2021. "Directional news impact curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 94-107, January.
    3. Nicolás Magner & Nicolás Hardy, 2022. "Cryptocurrency Forecasting: More Evidence of the Meese-Rogoff Puzzle," Mathematics, MDPI, vol. 10(13), pages 1-27, July.
    4. Kian-Ping Lim & Robert Brooks, 2009. "On the validity of conventional statistical tests given evidence of nonsynchronous trading and nonlinear dynamics in returns generating process: a further note," Applied Economics Letters, Taylor & Francis Journals, vol. 16(6), pages 649-652.
    5. Nicolas S. Magner & Nicolás Hardy & Tiago Ferreira & Jaime F. Lavin, 2023. "“Agree to Disagree”: Forecasting Stock Market Implied Volatility Using Financial Report Tone Disagreement Analysis," Mathematics, MDPI, vol. 11(7), pages 1-16, March.
    6. Pablo Pincheira, 2013. "A Simple Out-of-Sample Test for the Martingale Difference Hypothesis," Working Papers Central Bank of Chile 698, Central Bank of Chile.
    7. Michał Dominik Stasiak & Żaneta Staszak, 2024. "Modelling and Forecasting Crude Oil Prices Using Trend Analysis in a Binary-Temporal Representation," Energies, MDPI, vol. 17(14), pages 1-13, July.
    8. Ayedi Ahmed & Marjène Gana & Stéphane Goutte & Khaled Guesmi, 2023. "Managing Portfolio Risk During the BREXIT Crisis: A Cross-Quantilogram Analysis of Stock Markets and Commodities Across European Countries, the US, and BRICS," Working Papers halshs-04068651, HAL.
    9. Pablo Pincheira, 2006. "Shrinkage Based Tests of the Martingale Difference Hypothesis," Working Papers Central Bank of Chile 376, Central Bank of Chile.
    10. Pincheira, Pablo & Hardy, Nicolás, 2019. "Forecasting Aluminum Prices with Commodity Currencies," MPRA Paper 97005, University Library of Munich, Germany.
    11. Iyke, Bernard Njindan & Phan, Dinh Hoang Bach & Narayan, Paresh Kumar, 2022. "Exchange rate return predictability in times of geopolitical risk," International Review of Financial Analysis, Elsevier, vol. 81(C).
    12. Siroos Khademalomoom & Paresh Kumar Narayan & Susan Sunila Sharma, 2019. "Higher Moments and Exchange Rate Behavior," The Financial Review, Eastern Finance Association, vol. 54(1), pages 201-229, February.
    13. Stanislav Anatolyev, 2006. "Testing for predictability (in Russian)," Quantile, Quantile, issue 1, pages 39-42, September.
    14. Stanislav Anatolyev & Nikolay Gospodinov, 2007. "Modeling Financial Return Dynamics by Decomposition," Working Papers w0095, New Economic School (NES).
    15. Narayan, Paresh Kumar & Sharma, Susan Sunila & Phan, Dinh Hoang Bach & Liu, Guangqiang, 2020. "Predicting exchange rate returns," Emerging Markets Review, Elsevier, vol. 42(C).
    16. Bohumil Stádník & Algita Miečinskienė, 2015. "Complex Model of Market Price Development and its Simulation," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 16(4), pages 786-807, August.
    17. George Athanasopoulos & Rob J Hyndman & Raffaele Mattera, 2023. "Improving out-of-sample Forecasts of Stock Price Indexes with Forecast Reconciliation and Clustering," Monash Econometrics and Business Statistics Working Papers 17/23, Monash University, Department of Econometrics and Business Statistics.
    18. Anatolyev Stanislav, 2009. "Multi-Market Direction-of-Change Modeling Using Dependence Ratios," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(1), pages 1-24, March.
    19. Dick, Christian D. & MacDonald, Ronald & Menkhoff, Lukas, 2014. "Exchange rate forecasts and expected fundamentals," Kiel Working Papers 1974, Kiel Institute for the World Economy (IfW Kiel).
    20. Stanislav Anatolyev, 2006. "Nonparametric retrospection and monitoring of predictability of financial returns," Working Papers w0071, Center for Economic and Financial Research (CEFIR).
    21. Sudarshan Kumar & Sobhesh Kumar Agarwalla & Jayanth R. Varma & Vineet Virmani, 2023. "Harvesting the volatility smile in a large emerging market: A Dynamic Nelson–Siegel approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(11), pages 1615-1644, November.
    22. Anatolyev, Stanislav, 2005. "A Ten-year retrospection of the behavior of Russian stock returns," BOFIT Discussion Papers 9/2005, Bank of Finland Institute for Emerging Economies (BOFIT).
    23. Çakmaklı, Cem & van Dijk, Dick, 2016. "Getting the most out of macroeconomic information for predicting excess stock returns," International Journal of Forecasting, Elsevier, vol. 32(3), pages 650-668.
    24. Hardy, Nicolás & Ferreira, Tiago & Quinteros, Maria J. & Magner, Nicolás S., 2023. "“Watch your tone!”: Forecasting mining industry commodity prices with financial report tone," Resources Policy, Elsevier, vol. 86(PA).
    25. Jying-Nan Wang & Jiangze Du & Chonghui Jiang & Kin-Keung Lai, 2019. "Chinese Currency Exchange Rates Forecasting with EMD-Based Neural Network," Complexity, Hindawi, vol. 2019, pages 1-15, October.
    26. Blaskowitz, Oliver & Herwartz, Helmut, 2011. "On economic evaluation of directional forecasts," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1058-1065, October.
    27. Pincheira Brown, Pablo & Hardy, Nicolás, 2019. "Forecasting base metal prices with the Chilean exchange rate," Resources Policy, Elsevier, vol. 62(C), pages 256-281.
    28. Kozhan, Roman & Salmon, Mark, 2009. "Uncertainty aversion in a heterogeneous agent model of foreign exchange rate formation," Journal of Economic Dynamics and Control, Elsevier, vol. 33(5), pages 1106-1122, May.
    29. 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.
    30. Òscar Jordà & Alan M. Taylor, 2011. "Performance Evaluation of Zero Net-Investment Strategies," NBER Working Papers 17150, National Bureau of Economic Research, Inc.
    31. Kozhan, Roman & Salmon, Mark, 2012. "The information content of a limit order book: The case of an FX market," Journal of Financial Markets, Elsevier, vol. 15(1), pages 1-28.
    32. Massimo Guidolin & Erwin Hansen & Gabriel Cabrera, 2023. "Time-Varying Risk Aversion and International Stock Returns," BAFFI CAREFIN Working Papers 23203, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    33. Samuel W. Malone & Robert B. Gramacy & Enrique Ter Horst, 2016. "Timing Foreign Exchange Markets," Econometrics, MDPI, vol. 4(1), pages 1-23, March.
    34. Blaskowitz, Oliver J. & Herwartz, Helmut, 2008. "Testing directional forecast value in the presence of serial correlation," SFB 649 Discussion Papers 2008-073, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    35. Luis H. R. Alvarez E. & Paavo Salminen, 2017. "Timing in the presence of directional predictability: optimal stopping of skew Brownian motion," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 86(2), pages 377-400, October.
    36. 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.
    37. Narayan, Paresh Kumar & Sharma, Susan Sunila, 2015. "Is carbon emissions trading profitable?," Economic Modelling, Elsevier, vol. 47(C), pages 84-92.
    38. Constantin Bürgi & Dorine Boumans, 2020. "Categorical Forecasts and Non-Categorical Loss Functions," CESifo Working Paper Series 8266, CESifo.
    39. Dick, Christian D. & MacDonald, Ronald & Menkhoff, Lukas, 2011. "Individual exchange rate forecasts and expected fundamentals," ZEW Discussion Papers 11-062, ZEW - Leibniz Centre for European Economic Research.

  35. Stanislav Anatolyev, 2005. "GMM, GEL, Serial Correlation, and Asymptotic Bias," Econometrica, Econometric Society, vol. 73(3), pages 983-1002, May.

    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. Chang, Jinyuan & Chen, Song Xi & Chen, Xiaohong, 2014. "High Dimensional Generalized Empirical Likelihood for Moment Restrictions with Dependent Data," MPRA Paper 59640, University Library of Munich, Germany.
    3. Alastair R. Hall & Yuyi Li & Chris D. Orme & Arthur Sinko, 2013. "Testing for Structural Instability in Moment Restriction Models: an Info-metric Approach," Economics Discussion Paper Series 1326, Economics, The University of Manchester.
    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. Yoshihide Kakizawa, 2013. "Frequency domain generalized empirical likelihood method," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(6), pages 691-716, November.
    6. Jason Allen & Allan Gregory & Katsumi Shimotsu, 2008. "Empirical Likelihood Block Bootstrapping," Staff Working Papers 08-18, Bank of Canada.
    7. La Vecchia, Davide & Moor, Alban & Scaillet, Olivier, 2020. "A higher-order correct fast moving-average bootstrap for dependent data," Working Papers unige:129395, University of Geneva, Geneva School of Economics and Management.
    8. Anatolyev, Stanislav, 2008. "Method-of-moments estimation and choice of instruments: Numerical computations," Economics Letters, Elsevier, vol. 100(2), pages 217-220, August.
    9. Caio Vigo Pereira & Marcio Laurini, 2020. "Portfolio Efficiency Tests with Conditioning Information - Comparing GMM and GEL Estimators," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202014, University of Kansas, Department of Economics, revised Sep 2020.
    10. McAdam, Peter & Willman, Alpo, 2011. "Technology, utilization and inflation: what drives the New Keynesian Phillips Curve?," Working Paper Series 1369, European Central Bank.
    11. Alastair R. Hall, 2013. "Generalized Method of Moments," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 14, pages 313-333, Edward Elgar Publishing.
    12. Iglesias, Emma M. & Phillips, Garry D.A., 2008. "Asymptotic bias of GMM and GEL under possible nonstationary spatial dependence," Economics Letters, Elsevier, vol. 99(2), pages 393-397, May.
    13. Hill, Jonathan B. & Prokhorov, Artem, 2016. "GEL estimation for heavy-tailed GARCH models with robust empirical likelihood inference," Journal of Econometrics, Elsevier, vol. 190(1), pages 18-45.
    14. Yuichi Kitamura, 2006. "Empirical Likelihood Methods in Econometrics: Theory and Practice," CIRJE F-Series CIRJE-F-430, CIRJE, Faculty of Economics, University of Tokyo.
    15. Stanislav Anatolyev, 2005. "Optimal Instruments in Time Series: A Survey," Working Papers w0069, Center for Economic and Financial Research (CEFIR).
    16. Yoshitsugu Kitazawa, 2014. "Consistent estimation for the full-fledged fixed effects zero-inflated Poisson model," Discussion Papers 66, Kyushu Sangyo University, Faculty of Economics.
    17. Damba Lkhagvasuren, 2009. "Large Locational Differences in Unemployment Despite High Labor Mobility: Impact of Moving Cost on Aggregate Unemployment and Welfare," Working Papers 09009, Concordia University, Department of Economics, revised Mar 2010.
    18. Paul Levine & Luis F. Martins & Vasco J. Gabriel, 2006. "Robust Estimates of the New Keynesian Phillips Curve," School of Economics Discussion Papers 0206, School of Economics, University of Surrey.
    19. Kenneth D. West & Ka-fu Wong & Stanislav Anatolyev, 2007. "Instrumental Variables Estimation of Heteroskedastic Linear Models Using All Lags of Instruments," NBER Working Papers 13134, National Bureau of Economic Research, Inc.
    20. Zhang, Jia & Shi, Haoming & Tian, Lemeng & Xiao, Fengjun, 2019. "Penalized generalized empirical likelihood in high-dimensional weakly dependent data," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 270-283.
    21. Xu, Ke-Li, 2020. "Inference of local regression in the presence of nuisance parameters," Journal of Econometrics, Elsevier, vol. 218(2), pages 532-560.
    22. Jungbin Hwang & Gonzalo Valdés, 2020. "Finite-sample Corrected Inference for Two-step GMM in Time Series," Working papers 2020-02, University of Connecticut, Department of Economics.
    23. Grigory Franguridi & Bulat Gafarov & Kaspar Wüthrich, 2021. "Conditional Quantile Estimators: A Small Sample Theory," CESifo Working Paper Series 9046, CESifo.
    24. Alain Guay & Jean-Francois Lamarche, 2005. "The Information Content of Implied Probabilities to Detect Structural Change," Working Papers 0804, Brock University, Department of Economics, revised Oct 2008.
    25. Gospodinov, Nikolay & Otsu, Taisuke, 2012. "Local GMM estimation of time series models with conditional moment restrictions," Journal of Econometrics, Elsevier, vol. 170(2), pages 476-490.
    26. Jun Lu & Wen Gan & Lei Shi, 2022. "Local influence analysis for GMM estimation," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(1), pages 1-23, March.
    27. Eric S. Lin & Ta-Sheng Chou, 2018. "Finite-sample refinement of GMM approach to nonlinear models under heteroskedasticity of unknown form," Econometric Reviews, Taylor & Francis Journals, vol. 37(1), pages 1-28, January.
    28. Alain Guay & Florian Pelgrin, 2016. "Using Implied Probabilities to Improve the Estimation of Unconditional Moment Restrictions for Weakly Dependent Data," Econometric Reviews, Taylor & Francis Journals, vol. 35(3), pages 344-372, March.
    29. Vasco Gabriel & Paul Levine & Christopher Spencer & Bo Yang, 2008. "On the (ir)relevance of direct supply-side effects of monetary policy," School of Economics Discussion Papers 0408, School of Economics, University of Surrey.
    30. Seojeong Lee, 2018. "Asymptotic Refinements of a Misspecification-Robust Bootstrap for Generalized Empirical Likelihood Estimators," Papers 1806.00953, arXiv.org, revised Jun 2018.
    31. Jin, Fei & Lee, Lung-fei, 2019. "GEL estimation and tests of spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 208(2), pages 585-612.
    32. Martins, Luis F. & Gabriel, Vasco J., 2009. "New Keynesian Phillips Curves and potential identification failures: A Generalized Empirical Likelihood analysis," Journal of Macroeconomics, Elsevier, vol. 31(4), pages 561-571, December.
    33. Herbst, Edward P. & Johannsen, Benjamin K., 2024. "Bias in local projections," Journal of Econometrics, Elsevier, vol. 240(1).
    34. Hwang, Jungbin & Valdés, Gonzalo, 2023. "Finite-sample corrected inference for two-step GMM in time series," Journal of Econometrics, Elsevier, vol. 234(1), pages 327-352.
    35. Alain Guay & Florian Pelgrin, 2007. "Using Implied Probabilities to Improve Estimation with Unconditional Moment Restrictions," Cahiers de recherche 0747, CIRPEE.
    36. Sowell, Fallaw, 2006. "The Empirical Saddlepoint Approximation for GMM Estimators," MPRA Paper 3356, University Library of Munich, Germany, revised May 2007.
    37. Li, Haiqi & Fan, Rui & Park, Sung Y., 2018. "Generalized empirical likelihood specification test robust to local misspecification," Economics Letters, Elsevier, vol. 171(C), pages 149-153.
    38. Guggenberger, Patrik & Ramalho, Joaquim J.S. & Smith, Richard J., 2012. "GEL statistics under weak identification," Journal of Econometrics, Elsevier, vol. 170(2), pages 331-349.

  36. Anatolyev, Stanislav & Kosenok, Grigory, 2005. "An Alternative To Maximum Likelihood Based On Spacings," Econometric Theory, Cambridge University Press, vol. 21(2), pages 472-476, April.

    Cited by:

    1. Grigoriy Volovskiy & Udo Kamps, 2020. "Maximum product of spacings prediction of future record values," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(7), pages 853-868, October.
    2. Suparna Basu & Sanjay K. Singh & Umesh Singh, 2019. "Estimation of Inverse Lindley Distribution Using Product of Spacings Function for Hybrid Censored Data," Methodology and Computing in Applied Probability, Springer, vol. 21(4), pages 1377-1394, December.
    3. Liang Wang & Sanku Dey & Yogesh Mani Tripathi, 2022. "Classical and Bayesian Inference of the Inverse Nakagami Distribution Based on Progressive Type-II Censored Samples," Mathematics, MDPI, vol. 10(12), pages 1-18, June.
    4. Hanem Mohamed & Salwa A. Mousa & Amina E. Abo-Hussien & Magda M. Ismail, 2022. "Estimation of the Daily Recovery Cases in Egypt for COVID-19 Using Power Odd Generalized Exponential Lomax Distribution," Annals of Data Science, Springer, vol. 9(1), pages 71-99, February.
    5. Prashant Kumar Sonker & Mukesh Kumar & Agni Saroj, 2023. "Stress–strength reliability models on power-Muth distribution," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 173-195, March.
    6. Sanku Dey & Ahmed Elshahhat & Mazen Nassar, 2023. "Analysis of progressive type-II censored gamma distribution," Computational Statistics, Springer, vol. 38(1), pages 481-508, March.
    7. Mazen Nassar & Ahmed Elshahhat, 2023. "Statistical Analysis of Inverse Weibull Constant-Stress Partially Accelerated Life Tests with Adaptive Progressively Type I Censored Data," Mathematics, MDPI, vol. 11(2), pages 1-29, January.
    8. Cesar Augusto Taconeli & Idemauro Antonio Rodrigues Lara, 2022. "Improved confidence intervals based on ranked set sampling designs within a parametric bootstrap approach," Computational Statistics, Springer, vol. 37(5), pages 2267-2293, November.
    9. Mazen Nassar & Farouq Mohammad A. Alam, 2022. "Analysis of Modified Kies Exponential Distribution with Constant Stress Partially Accelerated Life Tests under Type-II Censoring," Mathematics, MDPI, vol. 10(5), pages 1-26, March.
    10. Mohamed Sief & Xinsheng Liu & Abd El-Raheem Mohamed Abd El-Raheem, 2024. "Inference for a constant-stress model under progressive type-II censored data from the truncated normal distribution," Computational Statistics, Springer, vol. 39(5), pages 2791-2820, July.
    11. Suparna Basu & Sanjay Kumar Singh & Umesh Singh, 2017. "Parameter estimation of inverse Lindley distribution for Type-I censored data," Computational Statistics, Springer, vol. 32(1), pages 367-385, March.

  37. Anatolyev, Stanislav, 2004. "Inference when a nuisance parameter is weakly identified under the null hypothesis," Economics Letters, Elsevier, vol. 84(2), pages 245-254, August.

    Cited by:

    1. Jiake Li & Wei Wang & Meng Li & Qiao Li & Zeming Liu & Wei Chen & Yanan Wang, 2022. "Impact of Land Management Scale on the Carbon Emissions of the Planting Industry in China," Land, MDPI, vol. 11(6), pages 1-15, May.
    2. Efthymios G. Tsionas & Kien C. Tran & Panayotis G. Michaelides, 2019. "Bayesian inference in threshold stochastic frontier models," Empirical Economics, Springer, vol. 56(2), pages 399-422, February.

  38. Anatolyev, Stanislav, 2003. "03.1.2. Redundancy of Lagged Regressors in a Conditionally Heteroskedastic Time Series Regression," Econometric Theory, Cambridge University Press, vol. 19(1), pages 225-226, February.

    Cited by:

    1. Stanislav Anatolyev, 2005. "Optimal Instruments in Time Series: A Survey," Working Papers w0069, Center for Economic and Financial Research (CEFIR).

  39. Anatolyev, Stanislav, 2003. "02.5.2. Durbin–Watson Statistic and Random Individual Effects," Econometric Theory, Cambridge University Press, vol. 19(5), pages 882-883, October.

    Cited by:

    1. Nadiia Davydenko & Natalia Wasilewska & Zoya Titenko & Mirosław Wasilewski, 2024. "Substantiation of the Risk Neutralization Mechanism in the Financial Security Management of Agricultural Enterprises," Sustainability, MDPI, vol. 16(3), pages 1-18, January.
    2. Rodríguez Fernández, Mercedes, 2015. "Company financial performance: Does board size matter? Case of the EUROSTOXX50 index," Cuadernos de Gestión, Universidad del País Vasco - Instituto de Economía Aplicada a la Empresa (IEAE).

  40. Stanislav Anatolyev & Sergey Korepanov, 2003. "The term structure of Russian interest rates," Applied Economics Letters, Taylor & Francis Journals, vol. 10(13), pages 867-870.

    Cited by:

    1. Stanislav Anatolyev & Dmitry Shakin, 2006. "Trade intensity in the Russian stock market:dynamics, distribution and determinants," Working Papers w0070, New Economic School (NES).
    2. Minoas Koukouritakis & Leo Michelis, 2008. "The term structure of interest rates in the 12 newest EU countries," Applied Economics, Taylor & Francis Journals, vol. 40(4), pages 479-490.
    3. Benjamin Tabak, 2009. "Testing the expectations hypothesis in the Brazilian term structure of interest rates: a cointegration analysis," Applied Economics, Taylor & Francis Journals, vol. 41(21), pages 2681-2689.

  41. Anatolyev, Stanislav, 2003. "The Form Of The Optimal Nonlinear Instrument For Multiperiod Conditional Moment Restrictions," Econometric Theory, Cambridge University Press, vol. 19(4), pages 602-609, August.

    Cited by:

    1. Stanislav Anatolyev, 2005. "Optimal Instruments in Time Series: A Survey," Working Papers w0069, Center for Economic and Financial Research (CEFIR).
    2. Stanislav Anatolyev, 2007. "Optimal instruments (in Russian)," Quantile, Quantile, issue 2, pages 61-69, March.
    3. Kenneth D. West & Ka-fu Wong & Stanislav Anatolyev, 2007. "Instrumental Variables Estimation of Heteroskedastic Linear Models Using All Lags of Instruments," NBER Working Papers 13134, National Bureau of Economic Research, Inc.
    4. Alastair R. Hall, 2015. "Econometricians Have Their Moments: GMM at 32," The Economic Record, The Economic Society of Australia, vol. 91(S1), pages 1-24, June.
    5. 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.

  42. Stanislav Anatolyev & Andrey Vasnev, 2002. "Markov chain approximation in bootstrapping autoregressions," Economics Bulletin, AccessEcon, vol. 3(19), pages 1-8.

    Cited by:

    1. Roy Cerqueti & Paolo Falbo & Cristian Pelizzari & Federica Ricca & Andrea Scozzari, 2012. "A Mixed Integer Linear Programming Approach to Markov Chain Bootstrapping," Working Papers 67-2012, Macerata University, Department of Finance and Economic Sciences, revised Nov 2012.
    2. Cerqueti, Roy & Falbo, Paolo & Guastaroba, Gianfranco & Pelizzari, Cristian, 2013. "A Tabu Search heuristic procedure in Markov chain bootstrapping," European Journal of Operational Research, Elsevier, vol. 227(2), pages 367-384.
    3. Cerqueti, Roy & Falbo, Paolo & Pelizzari, Cristian, 2013. "Relevant States and Memory in Markov Chain Bootstrapping and Simulation," MPRA Paper 46250, University Library of Munich, Germany.
    4. Roy Cerqueti & Paolo Falbo & Cristian Pelizzari & Federica Ricca & Andrea Scozzari, 2017. "A mixed integer linear program to compress transition probability matrices in Markov chain bootstrapping," Annals of Operations Research, Springer, vol. 248(1), pages 163-187, January.

  43. Anatolyev, Stanislav, 1999. "Nonparametric estimation of nonlinear rational expectation models," Economics Letters, Elsevier, vol. 62(1), pages 1-6, January.

    Cited by:

    1. Stanislav Anatolyev, 2005. "Optimal Instruments in Time Series: A Survey," Working Papers w0069, Center for Economic and Financial Research (CEFIR).

Software components

    Sorry, no citations of software components recorded.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.