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Benedikt M. Pötscher
(Benedikt M. Poetscher)

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. Benedikt M. Potscher & David Preinerstorfer, 2020. "How Reliable are Bootstrap-based Heteroskedasticity Robust Tests?," Papers 2005.04089, arXiv.org, revised Nov 2021.

    Cited by:

    1. Pötscher, Benedikt M. & Preinerstorfer, David, 2021. "Valid Heteroskedasticity Robust Testing," MPRA Paper 107420, University Library of Munich, Germany.

  2. Pötscher, Benedikt M. & Preinerstorfer, David, 2017. "Further Results on Size and Power of Heteroskedasticity and Autocorrelation Robust Tests, with an Application to Trend Testing," MPRA Paper 81053, University Library of Munich, Germany.

    Cited by:

    1. Pötscher, Benedikt M. & Preinerstorfer, David, 2018. "Controlling the size of autocorrelation robust tests," Journal of Econometrics, Elsevier, vol. 207(2), pages 406-431.

  3. Pötscher, Benedikt M. & Preinerstorfer, David, 2016. "Controlling the Size of Autocorrelation Robust Tests," MPRA Paper 75657, University Library of Munich, Germany.

    Cited by:

    1. Pötscher, Benedikt M. & Preinerstorfer, David, 2023. "How Reliable Are Bootstrap-Based Heteroskedasticity Robust Tests?," Econometric Theory, Cambridge University Press, vol. 39(4), pages 789-847, August.
    2. Sun, Yixiao & Yang, Jingjing, 2020. "Testing-optimal kernel choice in HAR inference," Journal of Econometrics, Elsevier, vol. 219(1), pages 123-136.
    3. Federico Belotti & Alessandro Casini & Leopoldo Catania & Stefano Grassi & Pierre Perron, 2023. "Simultaneous bandwidths determination for DK-HAC estimators and long-run variance estimation in nonparametric settings," Econometric Reviews, Taylor & Francis Journals, vol. 42(3), pages 281-306, February.
    4. Alessandro Casini, 2021. "Theory of Evolutionary Spectra for Heteroskedasticity and Autocorrelation Robust Inference in Possibly Misspecified and Nonstationary Models," Papers 2103.02981, arXiv.org.
    5. Casini, Alessandro, 2023. "Theory of evolutionary spectra for heteroskedasticity and autocorrelation robust inference in possibly misspecified and nonstationary models," Journal of Econometrics, Elsevier, vol. 235(2), pages 372-392.
    6. Martínez-Iriarte, Julián & Sun, Yixiao & Wang, Xuexin, 2019. "Asymptotic F Tests under Possibly Weak Identification," University of California at San Diego, Economics Working Paper Series qt6qk200q8, Department of Economics, UC San Diego.
    7. Pötscher, Benedikt M. & Preinerstorfer, David, 2021. "Valid Heteroskedasticity Robust Testing," MPRA Paper 107420, University Library of Munich, Germany.
    8. Alphonse Kayiranga & Baozhang Chen & Fei Wang & Winny Nthangeni & Adil Dilawar & Yves Hategekimana & Huifang Zhang & Lifeng Guo, 2022. "Spatiotemporal Variation in Gross Primary Productivity and Their Responses to Climate in the Great Lakes Region of Sub-Saharan Africa during 2001–2020," Sustainability, MDPI, vol. 14(5), pages 1-23, February.
    9. 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.
    10. Alessandro Casini & Taosong Deng & Pierre Perron, 2021. "Theory of Low Frequency Contamination from Nonstationarity and Misspecification: Consequences for HAR Inference," Papers 2103.01604, arXiv.org, revised Nov 2021.
    11. Pötscher, Benedikt M. & Preinerstorfer, David, 2017. "Further Results on Size and Power of Heteroskedasticity and Autocorrelation Robust Tests, with an Application to Trend Testing," MPRA Paper 81053, University Library of Munich, Germany.
    12. Alessandro Casini & Pierre Perron, 2021. "Prewhitened Long-Run Variance Estimation Robust to Nonstationarity," Papers 2103.02235, arXiv.org, revised Dec 2021.

  4. Bachoc, Francois & Leeb, Hannes & Pötscher, Benedikt M., 2014. "Valid confidence intervals for post-model-selection predictors," MPRA Paper 60643, University Library of Munich, Germany.

    Cited by:

    1. Isaiah Andrews & Toru Kitagawa & Adam McCloskey, 2019. "Inference on Winners," NBER Working Papers 25456, National Bureau of Economic Research, Inc.

  5. Preinerstorfer, David & Pötscher, Benedikt M., 2014. "On the Power of Invariant Tests for Hypotheses on a Covariance Matrix," MPRA Paper 55059, University Library of Munich, Germany.

    Cited by:

    1. Federico Martellosio & Grant Hillier, 2019. "Adjusted QMLE for the spatial autoregressive parameter," Papers 1909.08141, arXiv.org.
    2. Pötscher, Benedikt M. & Preinerstorfer, David, 2018. "Controlling the size of autocorrelation robust tests," Journal of Econometrics, Elsevier, vol. 207(2), pages 406-431.
    3. Federico Martellosio, 2020. "Non-Identifiability in Network Autoregressions," Papers 2011.11084, arXiv.org, revised Jun 2022.
    4. David Preinerstorfer, 2018. "How to avoid the zero-power trap in testing for correlation," Papers 1812.10752, arXiv.org.
    5. Preinerstorfer, David, 2014. "Finite Sample Properties of Tests Based on Prewhitened Nonparametric Covariance Estimators," MPRA Paper 58333, University Library of Munich, Germany.

  6. Preinerstorfer, David & Pötscher, Benedikt M., 2013. "On Size and Power of Heteroscedasticity and Autocorrelation Robust Tests," MPRA Paper 45675, University Library of Munich, Germany.

    Cited by:

    1. Hwang, Jungbin & Sun, Yixiao, 2015. "Asymptotic F and t Tests in an Efficient GMM Setting," University of California at San Diego, Economics Working Paper Series qt1c62d8xf, Department of Economics, UC San Diego.
    2. Pötscher, Benedikt M. & Preinerstorfer, David, 2023. "How Reliable Are Bootstrap-Based Heteroskedasticity Robust Tests?," Econometric Theory, Cambridge University Press, vol. 39(4), pages 789-847, August.
    3. Sun, Yixiao & Yang, Jingjing, 2020. "Testing-optimal kernel choice in HAR inference," Journal of Econometrics, Elsevier, vol. 219(1), pages 123-136.
    4. Federico Belotti & Alessandro Casini & Leopoldo Catania & Stefano Grassi & Pierre Perron, 2023. "Simultaneous bandwidths determination for DK-HAC estimators and long-run variance estimation in nonparametric settings," Econometric Reviews, Taylor & Francis Journals, vol. 42(3), pages 281-306, February.
    5. Casini, Alessandro, 2023. "Theory of evolutionary spectra for heteroskedasticity and autocorrelation robust inference in possibly misspecified and nonstationary models," Journal of Econometrics, Elsevier, vol. 235(2), pages 372-392.
    6. Eben Lazarus & Daniel J. Lewis & James H. Stock, 2021. "The Size‐Power Tradeoff in HAR Inference," Econometrica, Econometric Society, vol. 89(5), pages 2497-2516, September.
    7. Pötscher, Benedikt M. & Preinerstorfer, David, 2018. "Controlling the size of autocorrelation robust tests," Journal of Econometrics, Elsevier, vol. 207(2), pages 406-431.
    8. Preinerstorfer, David & Pötscher, Benedikt M., 2017. "On The Power Of Invariant Tests For Hypotheses On A Covariance Matrix," Econometric Theory, Cambridge University Press, vol. 33(1), pages 1-68, February.
    9. Hwang, Jungbin & Sun, Yixiao, 2018. "Should we go one step further? An accurate comparison of one-step and two-step procedures in a generalized method of moments framework," Journal of Econometrics, Elsevier, vol. 207(2), pages 381-405.
    10. Zimmermann, Georg & Pauly, Markus & Bathke, Arne C., 2020. "Multivariate analysis of covariance with potentially singular covariance matrices and non-normal responses," Journal of Multivariate Analysis, Elsevier, vol. 177(C).
    11. Pötscher, Benedikt M. & Preinerstorfer, David, 2021. "Valid Heteroskedasticity Robust Testing," MPRA Paper 107420, University Library of Munich, Germany.
    12. 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.
    13. David Preinerstorfer, 2018. "How to avoid the zero-power trap in testing for correlation," Papers 1812.10752, arXiv.org.
    14. Ioan Talpoş & Alexandru Avram & Roxana HeteÛ, 2013. "The Impact Of Fiscal Policy On Gross Domestic Product In The European Union. A Panel Var Model Aproach," Annales Universitatis Apulensis Series Oeconomica, Faculty of Sciences, "1 Decembrie 1918" University, Alba Iulia, vol. 2(15), pages 1-25.
    15. Pötscher, Benedikt M. & Preinerstorfer, David, 2017. "Further Results on Size and Power of Heteroskedasticity and Autocorrelation Robust Tests, with an Application to Trend Testing," MPRA Paper 81053, University Library of Munich, Germany.

  7. Leeb, Hannes & Pötscher, Benedikt M., 2012. "Testing in the Presence of Nuisance Parameters: Some Comments on Tests Post-Model-Selection and Random Critical Values," MPRA Paper 41459, University Library of Munich, Germany.

    Cited by:

    1. McCloskey, Adam, 2017. "Bonferroni-based size-correction for nonstandard testing problems," Journal of Econometrics, Elsevier, vol. 200(1), pages 17-35.
    2. Bachoc, Francois & Leeb, Hannes & Pötscher, Benedikt M., 2014. "Valid confidence intervals for post-model-selection predictors," MPRA Paper 60643, University Library of Munich, Germany.
    3. DiTraglia, Francis J., 2016. "Using invalid instruments on purpose: Focused moment selection and averaging for GMM," Journal of Econometrics, Elsevier, vol. 195(2), pages 187-208.
    4. Liu, Chu-An, 2015. "Distribution theory of the least squares averaging estimator," Journal of Econometrics, Elsevier, vol. 186(1), pages 142-159.
    5. Francis J. DiTraglia, 2011. "Using Invalid Instruments on Purpose: Focused Moment Selection and Averaging for GMM, Second Version," PIER Working Paper Archive 14-045, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 09 Dec 2014.

  8. Pötscher, Benedikt M. & Schneider, Ulrike, 2011. "Distributional results for thresholding estimators in high-dimensional Gaussian regression models," MPRA Paper 31882, University Library of Munich, Germany.

    Cited by:

    1. Leeb, Hannes & Pötscher, Benedikt M. & Ewald, Karl, 2014. "On various confidence intervals post-model-selection," MPRA Paper 52858, University Library of Munich, Germany.
    2. William Kengne, 2023. "On consistency for time series model selection," Statistical Inference for Stochastic Processes, Springer, vol. 26(2), pages 437-458, July.
    3. Ulrike Schneider, 2016. "Confidence Sets Based on Thresholding Estimators in High-Dimensional Gaussian Regression Models," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1412-1455, December.
    4. Leeb, Hannes & Pötscher, Benedikt M. & Kivaranovic, Danijel, 2018. "Comment on "Model Confidence Bounds for Variable Selection" by Yang Li, Yuetian Luo, Davide Ferrari, Xiaonan Hu, and Yichen Qin," MPRA Paper 90655, University Library of Munich, Germany.

  9. Gach, Florian & Pötscher, Benedikt M., 2010. "Non-Parametric Maximum Likelihood Density Estimation and Simulation-Based Minimum Distance Estimators," MPRA Paper 27512, University Library of Munich, Germany.

    Cited by:

    1. Jean-Jacques Forneron, 2019. "A Sieve-SMM Estimator for Dynamic Models," Papers 1902.01456, arXiv.org, revised Jan 2023.

  10. Nickl, Richard & Pötscher, Benedikt M., 2009. "Efficient Simulation-Based Minimum Distance Estimation and Indirect Inference," MPRA Paper 16608, University Library of Munich, Germany.

    Cited by:

    1. Jean-Jacques Forneron & Serena Ng, 2015. "The ABC of Simulation Estimation with Auxiliary Statistics," Papers 1501.01265, arXiv.org, revised Oct 2017.

  11. Pötscher, Benedikt M. & Schneider, Ulrike, 2008. "Confidence sets based on penalized maximum likelihood estimators," MPRA Paper 9062, University Library of Munich, Germany.

    Cited by:

    1. Matei Demetrescu & Uwe Hassler & Vladimir Kuzin, 2011. "Pitfalls of post-model-selection testing: experimental quantification," Empirical Economics, Springer, vol. 40(2), pages 359-372, April.
    2. McCloskey, Adam, 2017. "Bonferroni-based size-correction for nonstandard testing problems," Journal of Econometrics, Elsevier, vol. 200(1), pages 17-35.
    3. Pötscher, Benedikt M. & Schneider, Ulrike, 2011. "Distributional results for thresholding estimators in high-dimensional Gaussian regression models," MPRA Paper 31882, University Library of Munich, Germany.
    4. Yufeng Liu & Yichao Wu, 2011. "Simultaneous multiple non-crossing quantile regression estimation using kernel constraints," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(2), pages 415-437.
    5. Leeb, Hannes & Pötscher, Benedikt M. & Ewald, Karl, 2014. "On various confidence intervals post-model-selection," MPRA Paper 52858, University Library of Munich, Germany.
    6. Ulrike Schneider, 2016. "Confidence Sets Based on Thresholding Estimators in High-Dimensional Gaussian Regression Models," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1412-1455, December.

  12. Pötscher, Benedikt M. & Schneider, Ulrike, 2007. "On the distribution of the adaptive LASSO estimator," MPRA Paper 6913, University Library of Munich, Germany.

    Cited by:

    1. Ruth M. Pfeiffer & Andrew Redd & Raymond J. Carroll, 2017. "On the impact of model selection on predictor identification and parameter inference," Computational Statistics, Springer, vol. 32(2), pages 667-690, June.
    2. Hui Xiao & Yiguo Sun, 2019. "On Tuning Parameter Selection in Model Selection and Model Averaging: A Monte Carlo Study," JRFM, MDPI, vol. 12(3), pages 1-16, June.
    3. Schneider, Ulrike & Wagner, Martin, 2008. "Catching Growth Determinants with the Adaptive LASSO," Economics Series 232, Institute for Advanced Studies.
    4. Bruce E. Hansen, 2016. "The Risk of James--Stein and Lasso Shrinkage," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1456-1470, December.
    5. David Cheng & Abhishek Chakrabortty & Ashwin N. Ananthakrishnan & Tianxi Cai, 2020. "Estimating average treatment effects with a double‐index propensity score," Biometrics, The International Biometric Society, vol. 76(3), pages 767-777, September.
    6. Xianyi Wu & Xian Zhou, 2019. "On Hodges’ superefficiency and merits of oracle property in model selection," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(5), pages 1093-1119, October.
    7. Kun Chen & Kung-Sik Chan & Nils Chr. Stenseth, 2014. "Source-Sink Reconstruction Through Regularized Multicomponent Regression Analysis-With Application to Assessing Whether North Sea Cod Larvae Contributed to Local Fjord Cod in Skagerrak," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 560-573, June.
    8. Audrino, Francesco & Camponovo, Lorenzo & Roth, Constantin, 2015. "Testing the lag structure of assets’ realized volatility dynamics," Economics Working Paper Series 1501, University of St. Gallen, School of Economics and Political Science.
    9. Leeb, Hannes & Pötscher, Benedikt M. & Ewald, Karl, 2014. "On various confidence intervals post-model-selection," MPRA Paper 52858, University Library of Munich, Germany.
    10. Pötscher, Benedikt M. & Schneider, Ulrike, 2008. "Confidence sets based on penalized maximum likelihood estimators," MPRA Paper 9062, University Library of Munich, Germany.
    11. Gabriela Ciuperca, 2014. "Model selection by LASSO methods in a change-point model," Statistical Papers, Springer, vol. 55(2), pages 349-374, May.
    12. Hui, Francis K.C. & Müller, Samuel & Welsh, A.H., 2020. "The LASSO on latent indices for regression modeling with ordinal categorical predictors," Computational Statistics & Data Analysis, Elsevier, vol. 149(C).
    13. Ulrike Schneider, 2016. "Confidence Sets Based on Thresholding Estimators in High-Dimensional Gaussian Regression Models," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1412-1455, December.
    14. Leeb, Hannes & Pötscher, Benedikt M. & Kivaranovic, Danijel, 2018. "Comment on "Model Confidence Bounds for Variable Selection" by Yang Li, Yuetian Luo, Davide Ferrari, Xiaonan Hu, and Yichen Qin," MPRA Paper 90655, University Library of Munich, Germany.

  13. Pötscher, Benedikt M. & Leeb, Hannes, 2007. "On the distribution of penalized maximum likelihood estimators: The LASSO, SCAD, and thresholding," MPRA Paper 5615, University Library of Munich, Germany.

    Cited by:

    1. Xun Lu & Su Liangjun, 2015. "Shrinkage Estimation of Dynamic Panel Data Models with Interactive Fixed Effects," Working Papers 02-2015, Singapore Management University, School of Economics.
    2. Giurcanu, Mihai C., 2012. "Bootstrapping in non-regular smooth function models," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 78-93.
    3. McCloskey, Adam, 2017. "Bonferroni-based size-correction for nonstandard testing problems," Journal of Econometrics, Elsevier, vol. 200(1), pages 17-35.
    4. Anders Bredahl Kock, 2012. "On the Oracle Property of the Adaptive Lasso in Stationary and Nonstationary Autoregressions," CREATES Research Papers 2012-05, Department of Economics and Business Economics, Aarhus University.
    5. Ruth M. Pfeiffer & Andrew Redd & Raymond J. Carroll, 2017. "On the impact of model selection on predictor identification and parameter inference," Computational Statistics, Springer, vol. 32(2), pages 667-690, June.
    6. Hui Xiao & Yiguo Sun, 2019. "On Tuning Parameter Selection in Model Selection and Model Averaging: A Monte Carlo Study," JRFM, MDPI, vol. 12(3), pages 1-16, June.
    7. Maarten Marsman & Mijke Rhemtulla, 2022. "Guest Editors’ Introduction to The Special Issue “Network Psychometrics in Action”: Methodological Innovations Inspired by Empirical Problems," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 1-11, March.
    8. Gold, David & Lederer, Johannes & Tao, Jing, 2020. "Inference for high-dimensional instrumental variables regression," Journal of Econometrics, Elsevier, vol. 217(1), pages 79-111.
    9. Carvalho, Carlos & Masini, Ricardo & Medeiros, Marcelo C., 2018. "ArCo: An artificial counterfactual approach for high-dimensional panel time-series data," Journal of Econometrics, Elsevier, vol. 207(2), pages 352-380.
    10. Andreas Groll & Gerhard Tutz, 2017. "Variable selection in discrete survival models including heterogeneity," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(2), pages 305-338, April.
    11. Liu, Xiaodong & Prucha, Ingmar R., 2018. "A robust test for network generated dependence," Journal of Econometrics, Elsevier, vol. 207(1), pages 92-113.
    12. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey, 2016. "Double machine learning for treatment and causal parameters," CeMMAP working papers 49/16, Institute for Fiscal Studies.
    13. Laurin Charles & Boomsma Dorret & Lubke Gitta, 2016. "The use of vector bootstrapping to improve variable selection precision in Lasso models," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(4), pages 305-320, August.
    14. Yufeng Liu & Yichao Wu, 2011. "Simultaneous multiple non-crossing quantile regression estimation using kernel constraints," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(2), pages 415-437.
    15. Max H. Farrell, 2013. "Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations," Papers 1309.4686, arXiv.org, revised Feb 2018.
    16. Yoshimasa Uematsu & Takashi Yamagata, 2020. "Inference in Weak Factor Models," ISER Discussion Paper 1080, Institute of Social and Economic Research, Osaka University.
    17. Kwon, Sunghoon & Lee, Sangin & Kim, Yongdai, 2015. "Moderately clipped LASSO," Computational Statistics & Data Analysis, Elsevier, vol. 92(C), pages 53-67.
    18. Bruce E. Hansen, 2016. "The Risk of James--Stein and Lasso Shrinkage," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1456-1470, December.
    19. Xianyi Wu & Xian Zhou, 2019. "On Hodges’ superefficiency and merits of oracle property in model selection," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(5), pages 1093-1119, October.
    20. Pötscher, Benedikt M., 2007. "Confidence Sets Based on Sparse Estimators Are Necessarily Large," MPRA Paper 5677, University Library of Munich, Germany.
    21. Marcelo C. Medeiros & Eduardo F. Mendes, 2015. "l1-Regularization of High-Dimensional Time-Series Models with Flexible Innovations," Textos para discussão 636, Department of Economics PUC-Rio (Brazil).
    22. Qin, Yichen & Wang, Linna & Li, Yang & Li, Rong, 2023. "Visualization and assessment of model selection uncertainty," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).
    23. Harold D. Chiang, 2019. "Many Average Partial Effects: with an Application to Text Regression," 2019 Papers pch1836, Job Market Papers.
    24. Stephen S. M. Lee & Mehdi Soleymani, 2015. "A Simple Formula for Mixing Estimators With Different Convergence Rates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1463-1478, December.
    25. Heiss, Florian & Hetzenecker, Stephan & Osterhaus, Maximilian, 2022. "Nonparametric estimation of the random coefficients model: An elastic net approach," Journal of Econometrics, Elsevier, vol. 229(2), pages 299-321.
    26. Pötscher, Benedikt M. & Schneider, Ulrike, 2007. "On the distribution of the adaptive LASSO estimator," MPRA Paper 6913, University Library of Munich, Germany.
    27. Alexandre Belloni & Victor Chernozhukov & Ying Wei, 2016. "Post-Selection Inference for Generalized Linear Models With Many Controls," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 606-619, October.
    28. Christian Kascha & Carsten Trenkler, 2009. "Bootstrapping the likelihood ratio cointegration test in error correction models with unknown lag order," Working Paper 2009/12, Norges Bank.
    29. M. Marsman & K. Huth & L. J. Waldorp & I. Ntzoufras, 2022. "Objective Bayesian Edge Screening and Structure Selection for Ising Networks," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 47-82, March.
    30. Randy C. S. Lai & Jan Hannig & Thomas C. M. Lee, 2015. "Generalized Fiducial Inference for Ultrahigh-Dimensional Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 760-772, June.
    31. Leeb, Hannes & Pötscher, Benedikt M. & Ewald, Karl, 2014. "On various confidence intervals post-model-selection," MPRA Paper 52858, University Library of Munich, Germany.
    32. Pötscher, Benedikt M. & Schneider, Ulrike, 2008. "Confidence sets based on penalized maximum likelihood estimators," MPRA Paper 9062, University Library of Munich, Germany.
    33. Farrell, Max H., 2015. "Robust inference on average treatment effects with possibly more covariates than observations," Journal of Econometrics, Elsevier, vol. 189(1), pages 1-23.
    34. Bo Sun & Siyuan Cheng & Jingdong Xie & Xin Sun, 2022. "Identification of Generators’ Economic Withholding Behavior Based on a SCAD-Logit Model in Electricity Spot Market," Energies, MDPI, vol. 15(11), pages 1-23, June.
    35. Anders Bredahl Kock, 2013. "Oracle inequalities for high-dimensional panel data models," CREATES Research Papers 2013-20, Department of Economics and Business Economics, Aarhus University.
    36. Fousekis, Panos & Grigoriadis, Vasilis, 2022. "Conditional tail price risk spillovers in coffee markets across quality, physical space, and time: Empirical analysis with penalized quantile regressions," Economic Modelling, Elsevier, vol. 106(C).
    37. David Drukker, 2019. "Inference after lasso model selection," 2019 Stata Conference 3, Stata Users Group.
    38. William Kengne, 2023. "On consistency for time series model selection," Statistical Inference for Stochastic Processes, Springer, vol. 26(2), pages 437-458, July.
    39. Tae-Hwy Lee & Zhou Xi & Ru Zhang, 2013. "Testing for Neglected Nonlinearity Using Regularized Artificial Neural Networks," Working Papers 201422, University of California at Riverside, Department of Economics, revised Apr 2012.
    40. Hui, Francis K.C. & Müller, Samuel & Welsh, A.H., 2020. "The LASSO on latent indices for regression modeling with ordinal categorical predictors," Computational Statistics & Data Analysis, Elsevier, vol. 149(C).
    41. Anders Bredahl Kock & Laurent A.F. Callot, 2012. "Oracle Efficient Estimation and Forecasting with the Adaptive LASSO and the Adaptive Group LASSO in Vector Autoregressions," CREATES Research Papers 2012-38, Department of Economics and Business Economics, Aarhus University.
    42. Ulrike Schneider, 2016. "Confidence Sets Based on Thresholding Estimators in High-Dimensional Gaussian Regression Models," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1412-1455, December.
    43. Tino Werner, 2022. "Asymptotic linear expansion of regularized M-estimators," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(1), pages 167-194, February.
    44. Leeb, Hannes & Pötscher, Benedikt M. & Kivaranovic, Danijel, 2018. "Comment on "Model Confidence Bounds for Variable Selection" by Yang Li, Yuetian Luo, Davide Ferrari, Xiaonan Hu, and Yichen Qin," MPRA Paper 90655, University Library of Munich, Germany.
    45. Masayuki Hirukawa & Di Liu & Irina Murtazashvili & Artem Prokhorov, 2023. "DS-HECK: double-lasso estimation of Heckman selection model," Empirical Economics, Springer, vol. 64(6), pages 3167-3195, June.
    46. Kramlinger, Peter & Schneider, Ulrike & Krivobokova, Tatyana, 2023. "Uniformly valid inference based on the Lasso in linear mixed models," Journal of Multivariate Analysis, Elsevier, vol. 198(C).
    47. Jun Zhang & Junpeng Zhu & Yan Zhou & Xia Cui & Tao Lu, 2020. "Multiplicative regression models with distortion measurement errors," Statistical Papers, Springer, vol. 61(5), pages 2031-2057, October.
    48. Latouche, Pierre & Mattei, Pierre-Alexandre & Bouveyron, Charles & Chiquet, Julien, 2016. "Combining a relaxed EM algorithm with Occam’s razor for Bayesian variable selection in high-dimensional regression," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 177-190.

  14. Pötscher, Benedikt M., 2007. "Confidence Sets Based on Sparse Estimators Are Necessarily Large," MPRA Paper 5677, University Library of Munich, Germany.

    Cited by:

    1. Schneider, Ulrike & Wagner, Martin, 2008. "Catching Growth Determinants with the Adaptive LASSO," Economics Series 232, Institute for Advanced Studies.
    2. Pötscher, Benedikt M. & Schneider, Ulrike, 2007. "On the distribution of the adaptive LASSO estimator," MPRA Paper 6913, University Library of Munich, Germany.
    3. Pötscher, Benedikt M. & Schneider, Ulrike, 2008. "Confidence sets based on penalized maximum likelihood estimators," MPRA Paper 9062, University Library of Munich, Germany.
    4. Andrews, Donald W.K. & Guggenberger, Patrik, 2009. "Incorrect asymptotic size of subsampling procedures based on post-consistent model selection estimators," Journal of Econometrics, Elsevier, vol. 152(1), pages 19-27, September.

  15. Pötscher, Benedikt M., 2006. "The Distribution of Model Averaging Estimators and an Impossibility Result Regarding Its Estimation," MPRA Paper 73, University Library of Munich, Germany, revised Jul 2006.

    Cited by:

    1. Schomaker, Michael & Heumann, Christian, 2014. "Model selection and model averaging after multiple imputation," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 758-770.
    2. Pötscher, Benedikt M. & Leeb, Hannes, 2009. "On the distribution of penalized maximum likelihood estimators: The LASSO, SCAD, and thresholding," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 2065-2082, October.
    3. Lu, Xun & Su, Liangjun, 2015. "Jackknife model averaging for quantile regressions," Journal of Econometrics, Elsevier, vol. 188(1), pages 40-58.
    4. Pötscher, Benedikt M. & Schneider, Ulrike, 2007. "On the distribution of the adaptive LASSO estimator," MPRA Paper 6913, University Library of Munich, Germany.
    5. Leeb, Hannes & Pötscher, Benedikt M. & Ewald, Karl, 2014. "On various confidence intervals post-model-selection," MPRA Paper 52858, University Library of Munich, Germany.
    6. Michael Schomaker & Christian Heumann, 2020. "When and when not to use optimal model averaging," Statistical Papers, Springer, vol. 61(5), pages 2221-2240, October.
    7. Xinyu Zhang & Alan T. K. Wan & Sherry Z. Zhou, 2011. "Focused Information Criteria, Model Selection, and Model Averaging in a Tobit Model With a Nonzero Threshold," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 132-142, June.
    8. Liu, Chu-An, 2015. "Distribution theory of the least squares averaging estimator," Journal of Econometrics, Elsevier, vol. 186(1), pages 142-159.
    9. Hounyo, Ulrich & Lahiri, Kajal, 2023. "Estimating the variance of a combined forecast: Bootstrap-based approach," Journal of Econometrics, Elsevier, vol. 232(2), pages 445-468.
    10. Wan, Alan T.K. & Zhang, Xinyu & Zou, Guohua, 2010. "Least squares model averaging by Mallows criterion," Journal of Econometrics, Elsevier, vol. 156(2), pages 277-283, June.

  16. Hannes Leeb & Benedikt M. Poetscher, 2005. "Sparse Estimators and the Oracle Property, or the Return of Hodges' Estimator," Cowles Foundation Discussion Papers 1500, Cowles Foundation for Research in Economics, Yale University, revised Apr 2007.

    Cited by:

    1. Matei Demetrescu & Uwe Hassler & Vladimir Kuzin, 2011. "Pitfalls of post-model-selection testing: experimental quantification," Empirical Economics, Springer, vol. 40(2), pages 359-372, April.
    2. Zhipeng Liao & Peter C.B. Phillips, 2012. "Automated Estimation of Vector Error Correction Models," Cowles Foundation Discussion Papers 1873, Cowles Foundation for Research in Economics, Yale University.
    3. Nicholas G. Polson & James G. Scott, 2016. "Mixtures, envelopes and hierarchical duality," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(4), pages 701-727, September.
    4. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2018. "Economic predictions with big data: the illusion of sparsity," Staff Reports 847, Federal Reserve Bank of New York.
    5. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2014. "Valid post-selection inference in high-dimensional approximately sparse quantile regression models," CeMMAP working papers 53/14, Institute for Fiscal Studies.
    6. Xun Lu & Su Liangjun, 2015. "Shrinkage Estimation of Dynamic Panel Data Models with Interactive Fixed Effects," Working Papers 02-2015, Singapore Management University, School of Economics.
    7. Phillip Heiler & Jana Mareckova, 2019. "Shrinkage for Categorical Regressors," Papers 1901.01898, arXiv.org.
    8. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Post-selection and post-regularization inference in linear models with many controls and instruments," CeMMAP working papers 02/15, Institute for Fiscal Studies.
    9. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Robust inference in high-dimensional approximately sparse quantile regression models," CeMMAP working papers 70/13, Institute for Fiscal Studies.
    10. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    11. Chatterjee, A. & Gupta, S. & Lahiri, S.N., 2015. "On the residual empirical process based on the ALASSO in high dimensions and its functional oracle property," Journal of Econometrics, Elsevier, vol. 186(2), pages 317-324.
    12. Anders Bredahl Kock, 2012. "On the Oracle Property of the Adaptive Lasso in Stationary and Nonstationary Autoregressions," CREATES Research Papers 2012-05, Department of Economics and Business Economics, Aarhus University.
    13. Knaus, Michael C., 2018. "A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student's Skills," IZA Discussion Papers 11547, Institute of Labor Economics (IZA).
    14. Alexandre Belloni & Victor Chernozhukov & Abhishek Kaul, 2017. "Confidence bands for coefficients in high dimensional linear models with error-in-variables," CeMMAP working papers 22/17, Institute for Fiscal Studies.
    15. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
    16. Minsu Chang & Francis J. DiTraglia, 2020. "A Generalized Focused Information Criterion for GMM," Papers 2011.07085, arXiv.org.
    17. Schneider, Ulrike & Wagner, Martin, 2008. "Catching Growth Determinants with the Adaptive LASSO," Economics Series 232, Institute for Advanced Studies.
    18. Carvalho, Carlos & Masini, Ricardo & Medeiros, Marcelo C., 2018. "ArCo: An artificial counterfactual approach for high-dimensional panel time-series data," Journal of Econometrics, Elsevier, vol. 207(2), pages 352-380.
    19. Eustasio Barrio, 2010. "Comments on: l 1 -penalization for mixture regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(2), pages 276-279, August.
    20. Pötscher, Benedikt M. & Leeb, Hannes, 2009. "On the distribution of penalized maximum likelihood estimators: The LASSO, SCAD, and thresholding," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 2065-2082, October.
    21. Liu, Xiaodong & Prucha, Ingmar R., 2018. "A robust test for network generated dependence," Journal of Econometrics, Elsevier, vol. 207(1), pages 92-113.
    22. Michael C. Knaus & Michael Lechner & Anthony Strittmatter, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," Papers 1810.13237, arXiv.org, revised Dec 2018.
    23. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey, 2016. "Double machine learning for treatment and causal parameters," CeMMAP working papers 49/16, Institute for Fiscal Studies.
    24. Malene Kallestrup-Lamb & Anders Bredahl Kock & Johannes Tang Kristensen, 2013. "Lassoing the Determinants of Retirement," CREATES Research Papers 2013-21, Department of Economics and Business Economics, Aarhus University.
    25. Gallant, A. Ronald & Hong, Han & Leung, Michael P. & Li, Jessie, 2022. "Constrained estimation using penalization and MCMC," Journal of Econometrics, Elsevier, vol. 228(1), pages 85-106.
    26. Pötscher, Benedikt M. & Schneider, Ulrike, 2011. "Distributional results for thresholding estimators in high-dimensional Gaussian regression models," MPRA Paper 31882, University Library of Munich, Germany.
    27. Susan M. Shortreed & Ashkan Ertefaie, 2017. "Outcome‐adaptive lasso: Variable selection for causal inference," Biometrics, The International Biometric Society, vol. 73(4), pages 1111-1122, December.
    28. DiTraglia, Francis J., 2016. "Using invalid instruments on purpose: Focused moment selection and averaging for GMM," Journal of Econometrics, Elsevier, vol. 195(2), pages 187-208.
    29. Yufeng Liu & Yichao Wu, 2011. "Simultaneous multiple non-crossing quantile regression estimation using kernel constraints," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(2), pages 415-437.
    30. Jean-Pierre Dubé & Sanjog Misra, 2017. "Personalized Pricing and Consumer Welfare," NBER Working Papers 23775, National Bureau of Economic Research, Inc.
    31. Yoshimasa Uematsu & Takashi Yamagata, 2020. "Inference in Weak Factor Models," ISER Discussion Paper 1080, Institute of Social and Economic Research, Osaka University.
    32. Jorg Stoye, 2009. "More on Confidence Intervals for Partially Identified Parameters," Econometrica, Econometric Society, vol. 77(4), pages 1299-1315, July.
    33. Kwon, Sunghoon & Lee, Sangin & Kim, Yongdai, 2015. "Moderately clipped LASSO," Computational Statistics & Data Analysis, Elsevier, vol. 92(C), pages 53-67.
    34. Bruce E. Hansen, 2016. "The Risk of James--Stein and Lasso Shrinkage," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1456-1470, December.
    35. Li, Jing & Li, Liyao & Liu, Shimeng, 2022. "Attenuation of agglomeration economies: Evidence from the universe of Chinese manufacturing firms," Journal of Urban Economics, Elsevier, vol. 130(C).
    36. Yoshimasa Uematsu & Takashi Yamagata, 2019. "Estimation of Weak Factor Models," ISER Discussion Paper 1053r, Institute of Social and Economic Research, Osaka University, revised Mar 2020.
    37. Zhu, Li-Ping & Zhu, Li-Xing, 2009. "Nonconcave penalized inverse regression in single-index models with high dimensional predictors," Journal of Multivariate Analysis, Elsevier, vol. 100(5), pages 862-875, May.
    38. Xianyi Wu & Xian Zhou, 2019. "On Hodges’ superefficiency and merits of oracle property in model selection," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(5), pages 1093-1119, October.
    39. Pötscher, Benedikt M., 2007. "Confidence Sets Based on Sparse Estimators Are Necessarily Large," MPRA Paper 5677, University Library of Munich, Germany.
    40. Marcelo C. Medeiros & Eduardo F. Mendes, 2015. "l1-Regularization of High-Dimensional Time-Series Models with Flexible Innovations," Textos para discussão 636, Department of Economics PUC-Rio (Brazil).
    41. Kun Chen & Kung-Sik Chan & Nils Chr. Stenseth, 2014. "Source-Sink Reconstruction Through Regularized Multicomponent Regression Analysis-With Application to Assessing Whether North Sea Cod Larvae Contributed to Local Fjord Cod in Skagerrak," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 560-573, June.
    42. Jiaying Gu & Stanislav Volgushev, 2018. "Panel Data Quantile Regression with Grouped Fixed Effects," Papers 1801.05041, arXiv.org, revised Aug 2018.
    43. Jie Ding & Vahid Tarokh & Yuhong Yang, 2018. "Model Selection Techniques -- An Overview," Papers 1810.09583, arXiv.org.
    44. Medeiros, Marcelo C. & Mendes, Eduardo F., 2016. "ℓ1-regularization of high-dimensional time-series models with non-Gaussian and heteroskedastic errors," Journal of Econometrics, Elsevier, vol. 191(1), pages 255-271.
    45. Audrino, Francesco & Camponovo, Lorenzo & Roth, Constantin, 2015. "Testing the lag structure of assets’ realized volatility dynamics," Economics Working Paper Series 1501, University of St. Gallen, School of Economics and Political Science.
    46. Pötscher, Benedikt M. & Schneider, Ulrike, 2007. "On the distribution of the adaptive LASSO estimator," MPRA Paper 6913, University Library of Munich, Germany.
    47. Alexandre Belloni & Victor Chernozhukov & Ying Wei, 2016. "Post-Selection Inference for Generalized Linear Models With Many Controls," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 606-619, October.
    48. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Uniform Post Selection Inference for LAD Regression and Other Z-estimation problems," Papers 1304.0282, arXiv.org, revised Oct 2020.
    49. Pötscher, Benedikt M. & Schneider, Ulrike, 2008. "Confidence sets based on penalized maximum likelihood estimators," MPRA Paper 9062, University Library of Munich, Germany.
    50. Ruoyao Shi & Zhipeng Liao, 2018. "An Averaging GMM Estimator Robust to Misspecification," Working Papers 201803, University of California at Riverside, Department of Economics.
    51. Farrell, Max H., 2015. "Robust inference on average treatment effects with possibly more covariates than observations," Journal of Econometrics, Elsevier, vol. 189(1), pages 1-23.
    52. Cheng, Xu & Liao, Zhipeng, 2015. "Select the valid and relevant moments: An information-based LASSO for GMM with many moments," Journal of Econometrics, Elsevier, vol. 186(2), pages 443-464.
    53. Robert Adamek & Stephan Smeekes & Ines Wilms, 2020. "Lasso Inference for High-Dimensional Time Series," Papers 2007.10952, arXiv.org, revised Sep 2022.
    54. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Uniform post selection inference for LAD regression models," CeMMAP working papers 24/13, Institute for Fiscal Studies.
    55. Xu Cheng & Zhipeng Liao, 2012. "Select the Valid and Relevant Moments: A One-Step Procedure for GMM with Many Moments," PIER Working Paper Archive 12-045, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    56. Ruoyao Shi, 2021. "An Averaging Estimator for Two Step M Estimation in Semiparametric Models," Working Papers 202105, University of California at Riverside, Department of Economics.
    57. Chui, David & Wing Cheng, Wui & Chi Chow, Sheung & LI, Ya, 2020. "Eastern Halloween effect: A stochastic dominance approach," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 68(C).
    58. Anders Bredahl Kock & Laurent A.F. Callot, 2012. "Oracle Efficient Estimation and Forecasting with the Adaptive LASSO and the Adaptive Group LASSO in Vector Autoregressions," CREATES Research Papers 2012-38, Department of Economics and Business Economics, Aarhus University.
    59. Francis J. DiTraglia, 2011. "Using Invalid Instruments on Purpose: Focused Moment Selection and Averaging for GMM, Second Version," PIER Working Paper Archive 14-045, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 09 Dec 2014.
    60. Francis DiTraglia, 2011. "Using Invalid Instruments on Purpose: Focused Moment Selection and Averaging for GMM, Second Version," PIER Working Paper Archive 15-027, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 10 Aug 2015.
    61. Gu, Jiaying & Volgushev, Stanislav, 2019. "Panel data quantile regression with grouped fixed effects," Journal of Econometrics, Elsevier, vol. 213(1), pages 68-91.
    62. Leeb, Hannes & Pötscher, Benedikt M. & Kivaranovic, Danijel, 2018. "Comment on "Model Confidence Bounds for Variable Selection" by Yang Li, Yuetian Luo, Davide Ferrari, Xiaonan Hu, and Yichen Qin," MPRA Paper 90655, University Library of Munich, Germany.
    63. Masayuki Hirukawa & Di Liu & Irina Murtazashvili & Artem Prokhorov, 2023. "DS-HECK: double-lasso estimation of Heckman selection model," Empirical Economics, Springer, vol. 64(6), pages 3167-3195, June.
    64. Heiler, Phillip & Mareckova, Jana, 2021. "Shrinkage for categorical regressors," Journal of Econometrics, Elsevier, vol. 223(1), pages 161-189.
    65. Xu Cheng & Zhipeng Liao & Ruoyao Shi, 2013. "Uniform Asymptotic Risk of Averaging GMM Estimator Robust to Misspecification, Second Version," PIER Working Paper Archive 15-017, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 25 Mar 2015.
    66. Brandon Koch & David M. Vock & Julian Wolfson, 2018. "Covariate selection with group lasso and doubly robust estimation of causal effects," Biometrics, The International Biometric Society, vol. 74(1), pages 8-17, March.

  17. Hannes Leeb & Benedikt M. Potscher, 2003. "Can One Estimate the Conditional Distribution of Post-Model-Selection Estimators?," Cowles Foundation Discussion Papers 1444, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2018. "Economic predictions with big data: the illusion of sparsity," Staff Reports 847, Federal Reserve Bank of New York.
    2. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2014. "Valid post-selection inference in high-dimensional approximately sparse quantile regression models," CeMMAP working papers 53/14, Institute for Fiscal Studies.
    3. Alexandre Belloni & Victor Chernozhukov & Christian Hansen & Damian Kozbur, 2014. "Inference in High Dimensional Panel Models with an Application to Gun Control," Papers 1411.6507, arXiv.org.
    4. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Post-selection and post-regularization inference in linear models with many controls and instruments," CeMMAP working papers 02/15, Institute for Fiscal Studies.
    5. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Robust inference in high-dimensional approximately sparse quantile regression models," CeMMAP working papers 70/13, Institute for Fiscal Studies.
    6. Ahmadi, Maryam & Manera, Matteo & Sadeghzadeh, Mehdi, 2019. "The investment-uncertainty relationship in the oil and gas industry," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    7. Schomaker, Michael & Heumann, Christian, 2014. "Model selection and model averaging after multiple imputation," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 758-770.
    8. McCloskey, Adam, 2017. "Bonferroni-based size-correction for nonstandard testing problems," Journal of Econometrics, Elsevier, vol. 200(1), pages 17-35.
    9. Aman Ullah & Huansha Wang, 2013. "Parametric and Nonparametric Frequentist Model Selection and Model Averaging," Econometrics, MDPI, vol. 1(2), pages 1-23, September.
    10. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "Inference on treatment effects after selection amongst high-dimensional controls," CeMMAP working papers 26/13, Institute for Fiscal Studies.
    11. Damian Kozbur, 2015. "Testing-Based Forward Model Selection," ECON - Working Papers 283, Department of Economics - University of Zurich, revised Apr 2018.
    12. Wan, Alan T.K. & Zhang, Xinyu & Wang, Shouyang, 2014. "Frequentist model averaging for multinomial and ordered logit models," International Journal of Forecasting, Elsevier, vol. 30(1), pages 118-128.
    13. Belloni, Alexandre & Chen, Mingli & Chernozhukov, Victor, 2016. "Quantile Graphical Models : Prediction and Conditional Independence with Applications to Financial Risk Management," Economic Research Papers 269321, University of Warwick - Department of Economics.
    14. Pötscher, Benedikt M. & Leeb, Hannes, 2009. "On the distribution of penalized maximum likelihood estimators: The LASSO, SCAD, and thresholding," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 2065-2082, October.
    15. Ahmadi, Maryam & Manera, Matteo & Sadeghzadeh, Mehdi, 2016. "Global oil market and the U.S. stock returns," Energy, Elsevier, vol. 114(C), pages 1277-1287.
    16. Karthik Muralidharan & Mauricio Romero & Kaspar Wüthrich, 2020. "Factorial Designs, Model Selection, and (Incorrect) Inference in Randomized Experiments," CESifo Working Paper Series 8137, CESifo.
    17. Munday, Tim & Brookes, James, 2021. "Mark my words: the transmission of central bank communication to the general public via the print media," Bank of England working papers 944, Bank of England.
    18. Deckers, Thomas & Hanck, Christoph, 2009. "Multiple Testing Techniques in Growth Econometrics," MPRA Paper 17843, University Library of Munich, Germany.
    19. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey, 2016. "Double machine learning for treatment and causal parameters," CeMMAP working papers 49/16, Institute for Fiscal Studies.
    20. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "High dimensional methods and inference on structural and treatment effects," CeMMAP working papers 59/13, Institute for Fiscal Studies.
    21. Sida Peng, 2019. "Heterogeneous Endogenous Effects in Networks," Papers 1908.00663, arXiv.org.
    22. Ivan A. Canay & Andres Santos & Azeem M. Shaikh, 2012. "On the testability of identification in some nonparametric models with endogeneity," CeMMAP working papers 18/12, Institute for Fiscal Studies.
    23. Kaspar Wuthrich & Ying Zhu, 2019. "Omitted variable bias of Lasso-based inference methods: A finite sample analysis," Papers 1903.08704, arXiv.org, revised Sep 2021.
    24. Danquah, Michael & Iddrisu, Abdul Malik & Boakye, Ernest Owusu & Owusu, Solomon, 2021. "Do gender wage differences within households influence women's empowerment and welfare? Evidence from Ghana," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 916-932.
    25. Philipp Ketz, 2022. "Allowing for weak identification when testing GARCH-X type models," Papers 2210.11398, arXiv.org.
    26. Magnus, Jan R. & Wan, Alan T.K. & Zhang, Xinyu, 2011. "Weighted average least squares estimation with nonspherical disturbances and an application to the Hong Kong housing market," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1331-1341, March.
    27. Hannes Leeb & Benedikt M. Potscher, 2003. "Can One Estimate the Conditional Distribution of Post-Model-Selection Estimators?," Cowles Foundation Discussion Papers 1444, Cowles Foundation for Research in Economics, Yale University.
    28. Richard Berk, 2009. "What Now? Some Brief Reflections on Model-Free Data Analysis," International Econometric Review (IER), Econometric Research Association, vol. 1(1), pages 18-27, April.
    29. Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2014. "Program evaluation with high-dimensional data," CeMMAP working papers CWP33/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    30. Abe, Ryosuke & Kato, Hironori, 2017. "What led to the establishment of a rail-oriented city? Determinants of urban rail supply in Tokyo, Japan, 1950–2010," Transport Policy, Elsevier, vol. 58(C), pages 72-79.
    31. Hongjian Shi & Marc Hallin & Mathias Drton & Fang Han, 2020. "Rate-Optimality of Consistent Distribution-Free Tests of Independence Based on Center-Outward Ranks and Signs," Working Papers ECARES 2020-23, ULB -- Universite Libre de Bruxelles.
    32. Ekaterina V. Astafyeva & Maria Yu. Turuntseva, 2023. "Анализ Возможностей Улучшения Качества Прогнозов Цен На Природные Ресурсы Методами Комбинирования На Основе Регрессионных Оценок Весов," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 12, pages 24-33, December.
    33. Alexandre Belloni & Victor Chernozhukov & Ying Wei, 2013. "Honest confidence regions for a regression parameter in logistic regression with a large number of controls," CeMMAP working papers CWP67/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    34. Su, Jiun-Hua, 2021. "Model selection in utility-maximizing binary prediction," Journal of Econometrics, Elsevier, vol. 223(1), pages 96-124.
    35. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2017. "Quantile graphical models: prediction and conditional independence with applications to systemic risk," CeMMAP working papers 54/17, Institute for Fiscal Studies.
    36. Zhimeng Sun & Zhi Su & Jingyi Ma, 2014. "Focused vector information criterion model selection and model averaging regression with missing response," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 77(3), pages 415-432, April.
    37. Chee Yin Yip & Hock Eam Lim & Hooi Hooi Lean, 2016. "Effectiveness of a Cluster of Determinants to Increase Economic Growth Rate: A Combined Statistical Criteria Approach," International Journal of Economics and Financial Issues, Econjournals, vol. 6(2), pages 728-735.
    38. Phillips, Peter C.B., 2005. "Automated Discovery In Econometrics," Econometric Theory, Cambridge University Press, vol. 21(1), pages 3-20, February.
    39. Liu, Chu-An, 2012. "A plug-in averaging estimator for regressions with heteroskedastic errors," MPRA Paper 41414, University Library of Munich, Germany.
    40. Serkan Aglasan & Barry K. Goodwin & Roderick M. Rejesus, 2023. "Risk effects of GM corn: Evidence from crop insurance outcomes and high‐dimensional methods," Agricultural Economics, International Association of Agricultural Economists, vol. 54(1), pages 110-126, January.
    41. Pötscher, Benedikt M. & Schneider, Ulrike, 2007. "On the distribution of the adaptive LASSO estimator," MPRA Paper 6913, University Library of Munich, Germany.
    42. Aglasan, Serkan & Goodwin, Barry K. & Rejesus, Roderick, 2020. "Genetically Modified Rootworm-Resistant Corn, Risk, and Weather: Evidence from High Dimensional Methods," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 305181, Agricultural and Applied Economics Association.
    43. Leeb, Hannes & Pötscher, Benedikt M. & Ewald, Karl, 2014. "On various confidence intervals post-model-selection," MPRA Paper 52858, University Library of Munich, Germany.
    44. Damian Kozbur, 2013. "Inference in additively separable models with a high-dimensional set of conditioning variables," ECON - Working Papers 284, Department of Economics - University of Zurich, revised Apr 2018.
    45. Hansen, Bruce E., 2005. "Challenges For Econometric Model Selection," Econometric Theory, Cambridge University Press, vol. 21(1), pages 60-68, February.
    46. Steven E. Pav, 2014. "Bounds on Portfolio Quality," Papers 1409.5936, arXiv.org.
    47. Farrell, Max H., 2015. "Robust inference on average treatment effects with possibly more covariates than observations," Journal of Econometrics, Elsevier, vol. 189(1), pages 1-23.
    48. Ng, Serena, 2013. "Variable Selection in Predictive Regressions," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 752-789, Elsevier.
    49. Sheena McConnell & Elizabeth A. Stuart & Barbara Devaney, 2008. "The Truncation-by-Death Problem," Evaluation Review, , vol. 32(2), pages 157-186, April.
    50. Xinyu Zhang & Alan T. K. Wan & Sherry Z. Zhou, 2011. "Focused Information Criteria, Model Selection, and Model Averaging in a Tobit Model With a Nonzero Threshold," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 132-142, June.
    51. Cheng, Xu & Hansen, Bruce E., 2015. "Forecasting with factor-augmented regression: A frequentist model averaging approach," Journal of Econometrics, Elsevier, vol. 186(2), pages 280-293.
    52. Christian Hansen & Damian Kozbur & Sanjog Misra, 2016. "Targeted undersmoothing," ECON - Working Papers 282, Department of Economics - University of Zurich, revised Apr 2018.
    53. Konstantin Gorgen & Melanie Schienle, 2019. "How have German University Tuition Fees Affected Enrollment Rates: Robust Model Selection and Design-based Inference in High-Dimensions," Papers 1909.08299, arXiv.org, revised Jan 2021.
    54. Tae-Hwy Lee & Zhou Xi & Ru Zhang, 2013. "Testing for Neglected Nonlinearity Using Regularized Artificial Neural Networks," Working Papers 201422, University of California at Riverside, Department of Economics, revised Apr 2012.
    55. Jeffrey S. Racine & Christopher F. Parmeter, 2012. "Data-Driven Model Evaluation: A Test for Revealed Performance," Department of Economics Working Papers 2012-13, McMaster University.
    56. Liu, Chu-An, 2015. "Distribution theory of the least squares averaging estimator," Journal of Econometrics, Elsevier, vol. 186(1), pages 142-159.
    57. Xu Cheng & Bruce E. Hansen, 2012. "Forecasting with Factor-Augmented Regression: A Frequentist Model Averaging Approach, Second Version," PIER Working Paper Archive 13-061, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 03 Sep 2013.
    58. Lasanthi C. R. Pelawa Watagoda & David J. Olive, 2021. "Bootstrapping multiple linear regression after variable selection," Statistical Papers, Springer, vol. 62(2), pages 681-700, April.
    59. Wan, Alan T.K. & Zhang, Xinyu & Zou, Guohua, 2010. "Least squares model averaging by Mallows criterion," Journal of Econometrics, Elsevier, vol. 156(2), pages 277-283, June.
    60. Leeb, Hannes & Pötscher, Benedikt M. & Kivaranovic, Danijel, 2018. "Comment on "Model Confidence Bounds for Variable Selection" by Yang Li, Yuetian Luo, Davide Ferrari, Xiaonan Hu, and Yichen Qin," MPRA Paper 90655, University Library of Munich, Germany.
    61. Masayuki Hirukawa & Di Liu & Irina Murtazashvili & Artem Prokhorov, 2023. "DS-HECK: double-lasso estimation of Heckman selection model," Empirical Economics, Springer, vol. 64(6), pages 3167-3195, June.
    62. Schomaker, Michael & Wan, Alan T.K. & Heumann, Christian, 2010. "Frequentist Model Averaging with missing observations," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3336-3347, December.
    63. Pötscher, Benedikt M., 2006. "The Distribution of Model Averaging Estimators and an Impossibility Result Regarding Its Estimation," MPRA Paper 73, University Library of Munich, Germany, revised Jul 2006.
    64. Bruce E. Hansen, 2007. "Least Squares Model Averaging," Econometrica, Econometric Society, vol. 75(4), pages 1175-1189, July.
    65. Ghosh, D. & Yuan, Z., 2009. "An improved model averaging scheme for logistic regression," Journal of Multivariate Analysis, Elsevier, vol. 100(8), pages 1670-1681, September.

  18. Hannes Leeb & Benedikt M. Pötscher, 2003. "Performance Limits for Estimators of the Risk or Distribution of Shrinkage-Type Estimators, and Some General Lower Risk-Bound Results," Vienna Economics Papers vie0301, University of Vienna, Department of Economics.

    Cited by:

    1. Li, Haiqi & Chen, Xingyi & Liang, Jufang, 2022. "Shrinkage estimation of panel data models with interactive effects," Economics Letters, Elsevier, vol. 210(C).
    2. Leeb, Hannes & Potscher, Benedikt M., 2008. "Sparse estimators and the oracle property, or the return of Hodges' estimator," Journal of Econometrics, Elsevier, vol. 142(1), pages 201-211, January.
    3. Alberto Abadie & Maximilian Kasy, 2019. "Choosing Among Regularized Estimators in Empirical Economics: The Risk of Machine Learning," The Review of Economics and Statistics, MIT Press, vol. 101(5), pages 743-762, December.
    4. Christian T. Brownlees & Giampiero M. Gallo, 2007. "Comparison of Volatility Measures: a Risk Management Perspective," Econometrics Working Papers Archive wp2007_15, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    5. Audrino, Francesco & Camponovo, Lorenzo & Roth, Constantin, 2015. "Testing the lag structure of assets’ realized volatility dynamics," Economics Working Paper Series 1501, University of St. Gallen, School of Economics and Political Science.
    6. Tae-Hwy Lee & Zhou Xi & Ru Zhang, 2013. "Testing for Neglected Nonlinearity Using Regularized Artificial Neural Networks," Working Papers 201422, University of California at Riverside, Department of Economics, revised Apr 2012.
    7. Ganggang Xu & Suojin Wang & Jianhua Z. Huang, 2014. "Focused information criterion and model averaging based on weighted composite quantile regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(2), pages 365-381, June.
    8. Leeb, Hannes & Pötscher, Benedikt M. & Kivaranovic, Danijel, 2018. "Comment on "Model Confidence Bounds for Variable Selection" by Yang Li, Yuetian Luo, Davide Ferrari, Xiaonan Hu, and Yichen Qin," MPRA Paper 90655, University Library of Munich, Germany.
    9. Andrews, Donald W.K. & Guggenberger, Patrik, 2009. "Incorrect asymptotic size of subsampling procedures based on post-consistent model selection estimators," Journal of Econometrics, Elsevier, vol. 152(1), pages 19-27, September.
    10. Pötscher, Benedikt M., 2006. "The Distribution of Model Averaging Estimators and an Impossibility Result Regarding Its Estimation," MPRA Paper 73, University Library of Munich, Germany, revised Jul 2006.

  19. Benedikt M. Pötscher, 2001. "Nonlinear Functions and Convergence to Brownian Motion: Beyond the Continuous Mapping Theorem," Vienna Economics Papers vie0203, University of Vienna, Department of Economics.

    Cited by:

    1. Jiti Gao & Peter C.B. Phillips, 2011. "Semiparametric Estimation in Multivariate Nonstationary Time Series Models," Monash Econometrics and Business Statistics Working Papers 17/11, Monash University, Department of Econometrics and Business Statistics.
    2. Ioannis Kasparis & Peter C. B. Phillips & Tassos Magdalinos, 2014. "Nonlinearity Induced Weak Instrumentation," Econometric Reviews, Taylor & Francis Journals, vol. 33(5-6), pages 676-712, August.
    3. Jiti Gao & Peter C. B. Phillips, 2010. "Semiparametric Estimation in Time Series of Simultaneous Equations," Cowles Foundation Discussion Papers 1769, Cowles Foundation for Research in Economics, Yale University.
    4. Andreou, Elena & Kasparis, Ioannis & Phillips, Peter C. B., 2013. "Nonparametric Predictive Regression," CEPR Discussion Papers 9570, C.E.P.R. Discussion Papers.
    5. Pötscher, Benedikt M., 2011. "On the Order of Magnitude of Sums of Negative Powers of Integrated Processes," MPRA Paper 28287, University Library of Munich, Germany.
    6. Phillips, Peter C.B., 2009. "Local Limit Theory And Spurious Nonparametric Regression," Econometric Theory, Cambridge University Press, vol. 25(6), pages 1466-1497, December.
    7. Rustam Ibragimov & Peter C.B. Phillips, 2004. "Regression Asymptotics Using Martingale Convergence Methods," Cowles Foundation Discussion Papers 1473, Cowles Foundation for Research in Economics, Yale University.
    8. Vanessa Berenguer-Rico & Bent Nielsen, 2015. "Cumulated sum of squares statistics for non-linear and non-stationary regressions," Economics Papers 2015-W09, Economics Group, Nuffield College, University of Oxford.
    9. Kasparis, Ioannis, 2010. "The Bierens test for certain nonstationary models," Journal of Econometrics, Elsevier, vol. 158(2), pages 221-230, October.
    10. Berenguer Rico, Vanessa & Gonzalo, Jesús, 2013. "Co-summability from linear to non-linear cointegration," UC3M Working papers. Economics we1312, Universidad Carlos III de Madrid. Departamento de Economía.
    11. Lee, Jungick & de Jong, Robert M., 2008. "Exponential functionals of integrated processes," Economics Letters, Elsevier, vol. 100(2), pages 181-184, August.
    12. Ioannis Kasparis, 2008. "Functional Form Misspecification in Regressions with a Unit Root," University of Cyprus Working Papers in Economics 2-2008, University of Cyprus Department of Economics.
    13. Youngsoo Bae & Robert M. de Jong, 2007. "Money demand function estimation by nonlinear cointegration," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(4), pages 767-793.
    14. Yoon, Gawon, 2005. "Long-memory property of nonlinear transformations of break processes," Economics Letters, Elsevier, vol. 87(3), pages 373-377, June.
    15. Berenguer-Rico, Vanessa & Gonzalo, Jesús, 2014. "Summability of stochastic processes—A generalization of integration for non-linear processes," Journal of Econometrics, Elsevier, vol. 178(P2), pages 331-341.
    16. Gao, Jiti & Phillips, Peter C.B., 2013. "Semiparametric estimation in triangular system equations with nonstationarity," Journal of Econometrics, Elsevier, vol. 176(1), pages 59-79.

  20. Hannes Leeb & Benedikt M. Poetscher, 2000. "The Finite-Sample Distribution of Post-Model-Selection Estimators, and Uniform Versus Non-Uniform Approximations," Econometrics 0004001, University Library of Munich, Germany.

    Cited by:

    1. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2012. "Model selection when there are multiple breaks," Journal of Econometrics, Elsevier, vol. 169(2), pages 239-246.
    2. Aman Ullah & Huansha Wang, 2013. "Parametric and Nonparametric Frequentist Model Selection and Model Averaging," Econometrics, MDPI, vol. 1(2), pages 1-23, September.
    3. Ruth M. Pfeiffer & Andrew Redd & Raymond J. Carroll, 2017. "On the impact of model selection on predictor identification and parameter inference," Computational Statistics, Springer, vol. 32(2), pages 667-690, June.
    4. Pötscher, Benedikt M. & Leeb, Hannes, 2009. "On the distribution of penalized maximum likelihood estimators: The LASSO, SCAD, and thresholding," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 2065-2082, October.
    5. Jennifer Castle & Xiaochuan Qin & W. Robert Reed, 2011. "Using Model Selection Algorthims to Obtain Reliable Coefficient Estimates," Working Papers in Economics 11/03, University of Canterbury, Department of Economics and Finance.
    6. Pötscher, Benedikt M. & Schneider, Ulrike, 2011. "Distributional results for thresholding estimators in high-dimensional Gaussian regression models," MPRA Paper 31882, University Library of Munich, Germany.
    7. Hassler, Uwe, 2010. "Testing regression coefficients after model selection through sign restrictions," Economics Letters, Elsevier, vol. 107(2), pages 220-223, May.
    8. Magnus, Jan R. & Wan, Alan T.K. & Zhang, Xinyu, 2011. "Weighted average least squares estimation with nonspherical disturbances and an application to the Hong Kong housing market," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1331-1341, March.
    9. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2013. "Model Selection in Equations with Many ‘Small’ Effects," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(1), pages 6-22, February.
    10. Danilov, D.L. & Magnus, J.R., 2001. "On the Harm that Pretesting Does," Discussion Paper 2001-37, Tilburg University, Center for Economic Research.
    11. Zhimeng Sun & Zhi Su & Jingyi Ma, 2014. "Focused vector information criterion model selection and model averaging regression with missing response," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 77(3), pages 415-432, April.
    12. Chee Yin Yip & Hock Eam Lim & Hooi Hooi Lean, 2016. "Effectiveness of a Cluster of Determinants to Increase Economic Growth Rate: A Combined Statistical Criteria Approach," International Journal of Economics and Financial Issues, Econjournals, vol. 6(2), pages 728-735.
    13. Hendry, David F., 2018. "Deciding between alternative approaches in macroeconomics," International Journal of Forecasting, Elsevier, vol. 34(1), pages 119-135.
    14. Liu, Chu-An, 2012. "A plug-in averaging estimator for regressions with heteroskedastic errors," MPRA Paper 41414, University Library of Munich, Germany.
    15. David Hendry & Soren Johansen, 2012. "Model Discovery and Trygve Haavelmo's Legacy," Economics Series Working Papers 598, University of Oxford, Department of Economics.
    16. Pötscher, Benedikt M. & Schneider, Ulrike, 2007. "On the distribution of the adaptive LASSO estimator," MPRA Paper 6913, University Library of Munich, Germany.
    17. Leeb, Hannes & Pötscher, Benedikt M. & Ewald, Karl, 2014. "On various confidence intervals post-model-selection," MPRA Paper 52858, University Library of Munich, Germany.
    18. Zhang, Xinyu & Yu, Jihai, 2018. "Spatial weights matrix selection and model averaging for spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 203(1), pages 1-18.
    19. Min, Aleksey & Holzmann, Hajo & Czado, Claudia, 2010. "Model selection strategies for identifying most relevant covariates in homoscedastic linear models," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3194-3211, December.
    20. Cheng, Xu & Hansen, Bruce E., 2015. "Forecasting with factor-augmented regression: A frequentist model averaging approach," Journal of Econometrics, Elsevier, vol. 186(2), pages 280-293.
    21. Tae-Hwy Lee & Zhou Xi & Ru Zhang, 2013. "Testing for Neglected Nonlinearity Using Regularized Artificial Neural Networks," Working Papers 201422, University of California at Riverside, Department of Economics, revised Apr 2012.
    22. Ali Mehrabani & Aman Ullah, 2022. "Weighted Average Estimation in Panel Data," Working Papers 202209, University of California at Riverside, Department of Economics, revised Apr 2022.
    23. Liu, Chu-An, 2015. "Distribution theory of the least squares averaging estimator," Journal of Econometrics, Elsevier, vol. 186(1), pages 142-159.
    24. Castle Jennifer L. & Doornik Jurgen A & Hendry David F., 2011. "Evaluating Automatic Model Selection," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-33, February.
    25. Xu Cheng & Bruce E. Hansen, 2012. "Forecasting with Factor-Augmented Regression: A Frequentist Model Averaging Approach, Second Version," PIER Working Paper Archive 13-061, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 03 Sep 2013.
    26. Giuseppe De Luca & Jan R. Magnus & Franco Peracchi, 2017. "Weighted-average least squares estimation of generalized linear models," EIEF Working Papers Series 1711, Einaudi Institute for Economics and Finance (EIEF), revised Aug 2017.
    27. Wan, Alan T.K. & Zhang, Xinyu & Zou, Guohua, 2010. "Least squares model averaging by Mallows criterion," Journal of Econometrics, Elsevier, vol. 156(2), pages 277-283, June.
    28. Schomaker, Michael & Wan, Alan T.K. & Heumann, Christian, 2010. "Frequentist Model Averaging with missing observations," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3336-3347, December.
    29. Leeb, Hannes & Pötscher, Benedikt M., 2012. "Testing in the Presence of Nuisance Parameters: Some Comments on Tests Post-Model-Selection and Random Critical Values," MPRA Paper 41459, University Library of Munich, Germany.
    30. Pötscher, Benedikt M., 2006. "The Distribution of Model Averaging Estimators and an Impossibility Result Regarding Its Estimation," MPRA Paper 73, University Library of Munich, Germany, revised Jul 2006.
    31. Mehmet Caner, 2021. "A Starting Note: A Historical Perspective in Lasso," International Econometric Review (IER), Econometric Research Association, vol. 13(1), pages 1-3, March.
    32. Bruce E. Hansen, 2007. "Least Squares Model Averaging," Econometrica, Econometric Society, vol. 75(4), pages 1175-1189, July.
    33. Danilov, Dmitry & Magnus, J.R.Jan R., 2004. "On the harm that ignoring pretesting can cause," Journal of Econometrics, Elsevier, vol. 122(1), pages 27-46, September.
    34. Duplinskiy, A., 2014. "Is regularization necessary? A Wald-type test under non-regular conditions," Research Memorandum 025, Maastricht University, Graduate School of Business and Economics (GSBE).

  21. Hannes Leeb & Benedikt Poetscher, 1999. "The variance of an integrated process need not diverge to infinity," Econometrics 9907001, University Library of Munich, Germany.

    Cited by:

    1. Paulauskas, Vygantas, 2007. "On unit roots for spatial autoregressive models," Journal of Multivariate Analysis, Elsevier, vol. 98(1), pages 209-226, January.
    2. Dietmar Bauer & Martin Wagner, 2003. "A Canonical Form for Unit Root Processes in the State Space Framework," Diskussionsschriften dp0312, Universitaet Bern, Departement Volkswirtschaft.
    3. Dietmar Bauer & Martin Wagner, 2003. "On Polynomial Cointegration in the State Space Framework," Diskussionsschriften dp0313, Universitaet Bern, Departement Volkswirtschaft.

  22. Benedikt M. Pötscher & Ingmar R. Prucha, 1999. "Basic Elements of Asymptotic Theory," Electronic Working Papers 99-001, University of Maryland, Department of Economics.

    Cited by:

    1. Mutl, Jan, 2009. "Consistent Estimation of Global VAR Models," Economics Series 234, Institute for Advanced Studies.
    2. Harry H. Kelejian & Ingmar R. Prucha, 1997. "Estimation of Spatial Regression Models with Autoregressive Errors by Two Stage Least Squares Procedures: A Serious Problem," Electronic Working Papers 97-001, University of Maryland, Department of Economics.
    3. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.

  23. Benedikt M. Pötscher, 1999. "Lower Risk Bounds and Properties of Confidence Sets For Ill-Posed Estimation Problems with Applications to Spectral Density and Persistence Estimation, Unit Roots,and Estimation of Long Memory Paramet," Vienna Economics Papers vie0202, University of Vienna, Department of Economics.

    Cited by:

    1. Xiao, Zhijie & Lima, Luiz Renato Regis de Oliveira, 2006. "Testing covariance stationarity," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 632, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    2. DUFOUR, Jean-Marie & JOUINI, Tarek, 2005. "Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing," Cahiers de recherche 16-2005, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    3. Dufour, Jean-Marie & Jouini, Tarek, 2006. "Finite-sample simulation-based inference in VAR models with application to Granger causality testing," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 229-254.
    4. Davidson James & Rambaccussing Dooruj, 2015. "A Test of the Long Memory Hypothesis Based on Self-Similarity," Journal of Time Series Econometrics, De Gruyter, vol. 7(2), pages 115-141, July.
    5. Muller, Ulrich K., 2007. "A theory of robust long-run variance estimation," Journal of Econometrics, Elsevier, vol. 141(2), pages 1331-1352, December.
    6. Jean‐Marie Dufour, 2003. "Identification, weak instruments, and statistical inference in econometrics," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 36(4), pages 767-808, November.
    7. Ivan A. Canay & Andres Santos & Azeem M. Shaikh, 2012. "On the testability of identification in some nonparametric models with endogeneity," CeMMAP working papers 18/12, Institute for Fiscal Studies.
    8. Xiao, Zhijie, 2012. "Robust inference in nonstationary time series models," Journal of Econometrics, Elsevier, vol. 169(2), pages 211-223.
    9. Chaker Aloui, 2003. "Long-Range Dependence in Daily Volatility on Tunisian Stock Market," Working Papers 0340, Economic Research Forum, revised Dec 2003.
    10. Preinerstorfer, David & Pötscher, Benedikt M., 2016. "On Size And Power Of Heteroskedasticity And Autocorrelation Robust Tests," Econometric Theory, Cambridge University Press, vol. 32(2), pages 261-358, April.
    11. Harvey, David I. & Leybourne, Stephen J. & Taylor, A.M. Robert, 2007. "A simple, robust and powerful test of the trend hypothesis," Journal of Econometrics, Elsevier, vol. 141(2), pages 1302-1330, December.
    12. Tsay, Wen-Jen, 2004. "Testing for contemporaneous correlation of disturbances in seemingly unrelated regressions with serial dependence," Economics Letters, Elsevier, vol. 83(1), pages 69-76, April.
    13. Manveer Kaur Mangat & Erhard Reschenhofer, 2020. "Frequency-Domain Evidence for Climate Change," Econometrics, MDPI, vol. 8(3), pages 1-15, July.

  24. Benedikt M. Potscher & Ingmar R. Prucha, 1994. "On the Formulation of Uniform Laws of Large Numbers: A Truncation Approach," NBER Technical Working Papers 0085, National Bureau of Economic Research, Inc.

    Cited by:

    1. Jenish, Nazgul & Prucha, Ingmar R., 2009. "Central limit theorems and uniform laws of large numbers for arrays of random fields," Journal of Econometrics, Elsevier, vol. 150(1), pages 86-98, May.
    2. Juan R. A. Bobenrieth & Eugenio S. A. Bobenrieth & Andrés F. Villegas & Brian D. Wright, 2022. "Estimation of Endogenous Volatility Models with Exponential Trends," Mathematics, MDPI, vol. 10(15), pages 1-27, July.

  25. Potscher, Benedikt M. & Prucha, Ingmar R., 1987. "A Uniform Law of Large Numbers for Dependent and Heterogeneous Data Process," Working Papers 87-26, C.V. Starr Center for Applied Economics, New York University.

    Cited by:

    1. Laurent Barras & Patrick Gagliardini & Olivier Scaillet, 2022. "Skill, Scale, and Value Creation in the Mutual Fund Industry," Journal of Finance, American Finance Association, vol. 77(1), pages 601-638, February.
    2. Francq, Christian & Horvath, Lajos & Zakoian, Jean-Michel, 2008. "Sup-tests for linearity in a general nonlinear AR(1) model when the supremum is taken over the full parameter space," MPRA Paper 16669, University Library of Munich, Germany.
    3. Christian FRANCQ & Lajos HORVATH & Jean-Michel ZAKOIAN, 2009. "Sup-Tests for Linearity in a General Nonlinear AR(1) Model," Working Papers 2009-16, Center for Research in Economics and Statistics.
    4. de Jong, Robert M., 1998. "Uniform laws of large numbers and stochastic Lipschitz-continuity," Journal of Econometrics, Elsevier, vol. 86(2), pages 243-268, June.
    5. Benedikt M. Potscher & Ingmar R. Prucha, 1994. "On the Formulation of Uniform Laws of Large Numbers: A Truncation Approach," NBER Technical Working Papers 0085, National Bureau of Economic Research, Inc.
    6. Andrew J. Patton & Johanna F. Ziegel & Rui Chen, 2017. "Dynamic Semiparametric Models for Expected Shortfall (and Value-at-Risk)," Papers 1707.05108, arXiv.org.
    7. Benedikt M. Pötscher & Ingmar R. Prucha, 1999. "Basic Elements of Asymptotic Theory," Electronic Working Papers 99-001, University of Maryland, Department of Economics.
    8. Liangjun Su & Zhenlin Yang, 2009. "Asymptotics and Bootstrap for Transformed Panel Data Regressions," Working Papers 03-2009, Singapore Management University, School of Economics.
    9. Andrews, Donald W. K. & Fair, Ray C., 1987. "Inference in Econometric Models with Structural Change," Working Papers 636, California Institute of Technology, Division of the Humanities and Social Sciences.
    10. Jenish, Nazgul & Prucha, Ingmar R., 2009. "Central limit theorems and uniform laws of large numbers for arrays of random fields," Journal of Econometrics, Elsevier, vol. 150(1), pages 86-98, May.
    11. Jin Seo Cho & Peter C. B. Phillips & Juwon Seo, 2019. "Parametric Inference on the Mean of Functional Data Applied to Lifetime Income Curves," Working papers 2019rwp-153, Yonsei University, Yonsei Economics Research Institute.
    12. Hess, Christian & Seri, Raffaello & Choirat, Christine, 2010. "Ergodic theorems for extended real-valued random variables," Stochastic Processes and their Applications, Elsevier, vol. 120(10), pages 1908-1919, September.
    13. Jin Seo Cho & Meng Huang & Halbert White, 2021. "Testing a Constant Mean Function Using Functional Regression," Working papers 2021rwp-190, Yonsei University, Yonsei Economics Research Institute.
    14. Jin Seo Cho & Meng Huang & Halbert White, 2009. "Testing for a Constant Mean Function using Functional Regression," Discussion Paper Series 0915, Institute of Economic Research, Korea University.
    15. Yoon-Jae Whang & Donald W.K. Andrews, 1991. "Tests of Specification for Parametric and Semiparametric Models," Cowles Foundation Discussion Papers 968, Cowles Foundation for Research in Economics, Yale University.
    16. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
    17. Juan R. A. Bobenrieth & Eugenio S. A. Bobenrieth & Andrés F. Villegas & Brian D. Wright, 2022. "Estimation of Endogenous Volatility Models with Exponential Trends," Mathematics, MDPI, vol. 10(15), pages 1-27, July.
    18. Kirill Evdokimov & Yuichi Kitamura & Taisuke Otsu, 2014. "Robust estimation of moment condition models with weakly dependent data," STICERD - Econometrics Paper Series 579, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    19. Erhan Bayraktar & Ulrich Horst & Ronnie Sircar, 2007. "Queueing Theoretic Approaches to Financial Price Fluctuations," Papers math/0703832, arXiv.org.
    20. Jenish, Nazgul & Prucha, Ingmar R., 2012. "On spatial processes and asymptotic inference under near-epoch dependence," Journal of Econometrics, Elsevier, vol. 170(1), pages 178-190.
    21. Otsu, Taisuke & Seo, Myung Hwan & Whang, Yoon-Jae, 2012. "Testing for non-nested conditional moment restrictions using unconditional empirical likelihood," Journal of Econometrics, Elsevier, vol. 167(2), pages 370-382.
    22. Joris Pinkse & Margaret Slade & Lihong Shen, 2006. "Dynamic Spatial Discrete Choice Using One-step GMM: An Application to Mine Operating Decisions," Spatial Economic Analysis, Taylor & Francis Journals, vol. 1(1), pages 53-99.
    23. Joris Pinkse, 2000. "Feasible Multivariate Nonparametric Estimation Using Weak Separability," Econometric Society World Congress 2000 Contributed Papers 1241, Econometric Society.

Articles

  1. Pötscher, Benedikt M. & Preinerstorfer, David, 2023. "How Reliable Are Bootstrap-Based Heteroskedasticity Robust Tests?," Econometric Theory, Cambridge University Press, vol. 39(4), pages 789-847, August.
    See citations under working paper version above.
  2. Pötscher, Benedikt M. & Preinerstorfer, David, 2018. "Controlling the size of autocorrelation robust tests," Journal of Econometrics, Elsevier, vol. 207(2), pages 406-431.
    See citations under working paper version above.
  3. Preinerstorfer, David & Pötscher, Benedikt M., 2017. "On The Power Of Invariant Tests For Hypotheses On A Covariance Matrix," Econometric Theory, Cambridge University Press, vol. 33(1), pages 1-68, February.
    See citations under working paper version above.
  4. Preinerstorfer, David & Pötscher, Benedikt M., 2016. "On Size And Power Of Heteroskedasticity And Autocorrelation Robust Tests," Econometric Theory, Cambridge University Press, vol. 32(2), pages 261-358, April.
    See citations under working paper version above.
  5. Pötscher, Benedikt M. & Leeb, Hannes, 2009. "On the distribution of penalized maximum likelihood estimators: The LASSO, SCAD, and thresholding," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 2065-2082, October.
    See citations under working paper version above.
  6. Leeb, Hannes & Pötscher, Benedikt M., 2008. "Can One Estimate The Unconditional Distribution Of Post-Model-Selection Estimators?," Econometric Theory, Cambridge University Press, vol. 24(2), pages 338-376, April.
    See citations under working paper version above.
  7. Leeb, Hannes & Potscher, Benedikt M., 2008. "Sparse estimators and the oracle property, or the return of Hodges' estimator," Journal of Econometrics, Elsevier, vol. 142(1), pages 201-211, January.
    See citations under working paper version above.
  8. Richard Nickl & Benedikt M. Pötscher, 2007. "Bracketing Metric Entropy Rates and Empirical Central Limit Theorems for Function Classes of Besov- and Sobolev-Type," Journal of Theoretical Probability, Springer, vol. 20(2), pages 177-199, June.

    Cited by:

    1. Denis Belomestny & Tobias Hübner & Volker Krätschmer, 2022. "Solving optimal stopping problems under model uncertainty via empirical dual optimisation," Finance and Stochastics, Springer, vol. 26(3), pages 461-503, July.

  9. Leeb, Hannes & Pötscher, Benedikt M., 2006. "Performance Limits For Estimators Of The Risk Or Distribution Of Shrinkage-Type Estimators, And Some General Lower Risk-Bound Results," Econometric Theory, Cambridge University Press, vol. 22(1), pages 69-97, February.
    See citations under working paper version above.
  10. Leeb, Hannes & Pötscher, Benedikt M., 2005. "Model Selection And Inference: Facts And Fiction," Econometric Theory, Cambridge University Press, vol. 21(1), pages 21-59, February.

    Cited by:

    1. Matei Demetrescu & Uwe Hassler & Vladimir Kuzin, 2011. "Pitfalls of post-model-selection testing: experimental quantification," Empirical Economics, Springer, vol. 40(2), pages 359-372, April.
    2. Zhipeng Liao & Peter C.B. Phillips, 2012. "Automated Estimation of Vector Error Correction Models," Cowles Foundation Discussion Papers 1873, Cowles Foundation for Research in Economics, Yale University.
    3. Di Iorio, Francesca & Fachin, Stefano, 2021. "Evaluating restricted common factor models for non-stationary data," Econometrics and Statistics, Elsevier, vol. 17(C), pages 64-75.
    4. Ioannis Kasparis & Peter C. B. Phillips & Tassos Magdalinos, 2014. "Nonlinearity Induced Weak Instrumentation," Econometric Reviews, Taylor & Francis Journals, vol. 33(5-6), pages 676-712, August.
    5. Donald W.K. Andrews & Patrik Guggenberger, 2007. "Validity of Subsampling and "Plug-in Asymptotic" Inference for Parameters Defined by Moment Inequalities," Cowles Foundation Discussion Papers 1620, Cowles Foundation for Research in Economics, Yale University.
    6. 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.
    7. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2018. "Economic predictions with big data: the illusion of sparsity," Staff Reports 847, Federal Reserve Bank of New York.
    8. Joel L. Horowitz & Lars Nesheim, 2019. "Using penalized likelihood to select parameters in a random coefficients multinomial logit model," CeMMAP working papers CWP50/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Timothy B. Armstrong & Michal Kolesár, 2018. "Optimal Inference in a Class of Regression Models," Econometrica, Econometric Society, vol. 86(2), pages 655-683, March.
    10. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2014. "Valid post-selection inference in high-dimensional approximately sparse quantile regression models," CeMMAP working papers 53/14, Institute for Fiscal Studies.
    11. Ben Gillen & Erik Snowberg & Leeat Yariv, 2015. "Experimenting with Measurement Error: Techniques with Applications to the Caltech Cohort Study," NBER Working Papers 21517, National Bureau of Economic Research, Inc.
    12. Toshiaki Tsukurimichi & Yu Inatsu & Vo Nguyen Le Duy & Ichiro Takeuchi, 2022. "Conditional selective inference for robust regression and outlier detection using piecewise-linear homotopy continuation," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(6), pages 1197-1228, December.
    13. Xun Lu & Su Liangjun, 2015. "Shrinkage Estimation of Dynamic Panel Data Models with Interactive Fixed Effects," Working Papers 02-2015, Singapore Management University, School of Economics.
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    201. Oliver Dukes & Vahe Avagyan & Stijn Vansteelandt, 2020. "Doubly robust tests of exposure effects under high‐dimensional confounding," Biometrics, The International Biometric Society, vol. 76(4), pages 1190-1200, December.
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  11. Potscher, Benedikt M. & Prucha, Ingmar R., 2004. "Contributions to econometrics, time-series analysis, and systems identification: a Festschrift in honor of Manfred Deistler," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 1-5.

    Cited by:

    1. Jaghdani, Tinoush Jamali & Brümmer, Bernhard, 2011. "Demand for Irrigation Water for Pistachio Production from Depleting Groundwater Resources: Spatial Econometric Approach," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114457, European Association of Agricultural Economists.

  12. Findley, David F. & Potscher, Benedikt M. & Wei, Ching-Zong, 2004. "Modeling of time series arrays by multistep prediction or likelihood methods," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 151-187.

    Cited by:

    1. Guillaume Chevillon, 2006. "Multi-step Forecasting in Unstable Economies: Robustness Issues in the Presence of Location Shifts," Economics Series Working Papers 257, University of Oxford, Department of Economics.
    2. Chevillon, Guillaume, 2009. "Multi-step forecasting in emerging economies: An investigation of the South African GDP," International Journal of Forecasting, Elsevier, vol. 25(3), pages 602-628, July.
    3. Boubacar Mainassara, Y. & Francq, C., 2011. "Estimating structural VARMA models with uncorrelated but non-independent error terms," Journal of Multivariate Analysis, Elsevier, vol. 102(3), pages 496-505, March.
    4. Chevillon, Guillaume, 2016. "Multistep forecasting in the presence of location shifts," International Journal of Forecasting, Elsevier, vol. 32(1), pages 121-137.
    5. McElroy, Tucker & Wildi, Marc, 2013. "Multi-step-ahead estimation of time series models," International Journal of Forecasting, Elsevier, vol. 29(3), pages 378-394.

  13. Pötscher, Benedikt M., 2004. "Nonlinear Functions And Convergence To Brownian Motion: Beyond The Continuous Mapping Theorem," Econometric Theory, Cambridge University Press, vol. 20(1), pages 1-22, February.
    See citations under working paper version above.
  14. Leeb, Hannes & Pötscher, Benedikt M., 2003. "The Finite-Sample Distribution Of Post-Model-Selection Estimators And Uniform Versus Nonuniform Approximations," Econometric Theory, Cambridge University Press, vol. 19(1), pages 100-142, February.
    See citations under working paper version above.
  15. Benedikt M. Poetscher, 2002. "Lower Risk Bounds and Properties of Confidence Sets for Ill-Posed Estimation Problems with Applications to Spectral Density and Persistence Estimation, Unit Roots, and Estimation of Long Memory Parame," Econometrica, Econometric Society, vol. 70(3), pages 1035-1065, May.
    See citations under working paper version above.
  16. Leeb, Hannes & Pötscher, Benedikt M., 2001. "The Variance Of An Integrated Process Need Not Diverge To Infinity, And Related Results On Partial Sums Of Stationary Processes," Econometric Theory, Cambridge University Press, vol. 17(4), pages 671-685, August.

    Cited by:

    1. Paulauskas, Vygantas, 2007. "On unit roots for spatial autoregressive models," Journal of Multivariate Analysis, Elsevier, vol. 98(1), pages 209-226, January.
    2. Dietmar Bauer & Martin Wagner, 2003. "A Canonical Form for Unit Root Processes in the State Space Framework," Diskussionsschriften dp0312, Universitaet Bern, Departement Volkswirtschaft.
    3. Dietmar Bauer & Martin Wagner, 2003. "On Polynomial Cointegration in the State Space Framework," Diskussionsschriften dp0313, Universitaet Bern, Departement Volkswirtschaft.

  17. Erhard Reschenhofer & Benedikt M. Pötscher & Michael A. Hauser, 1999. "Measuring persistence in aggregate output: ARMA models, fractionally integrated ARMA models and nonparametric procedures," Empirical Economics, Springer, vol. 24(2), pages 243-269.

    Cited by:

    1. Hassler, Uwe, 2012. "Impulse responses of antipersistent processes," Economics Letters, Elsevier, vol. 116(3), pages 454-456.
    2. Patrick Fève & Alain Guay, 2010. "Identification of Technology Shocks in Structural Vars," Economic Journal, Royal Economic Society, vol. 120(549), pages 1284-1318, December.
    3. Johnson, Paul & Papageorgiou, Chris, 2018. "What Remains of Cross-Country Convergence?," MPRA Paper 89355, University Library of Munich, Germany.
    4. Silverberg, Gerald & Verspagen, Bart, 2000. "A Note on Michelacci and Zaffaroni, Long Memory, and Time Series of Economic Growth," Research Memorandum 031, Maastricht University, Maastricht Economic Research Institute on Innovation and Technology (MERIT).
    5. Koop, G. & Ley, E. & Osiewalski, J. & Steel, M. F. J., 1997. "Bayesian analysis of long memory and persistence using ARFIMA models," LIDAM Reprints CORE 1246, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    6. O. Mikhail & C. J. Eberwein & J. Handa, 2006. "Estimating persistence in Canadian unemployment: evidence from a Bayesian ARFIMA," Applied Economics, Taylor & Francis Journals, vol. 38(15), pages 1809-1819.
    7. Belbute, José, 2013. "Does final demand for energy in Portugal exhibit long memory?," MPRA Paper 45717, University Library of Munich, Germany.
    8. Hassler, Uwe & Hosseinkouchack, Mehdi, 2014. "Effect of the order of fractional integration on impulse responses," Economics Letters, Elsevier, vol. 125(2), pages 311-314.
    9. Chaker Aloui, 2003. "Long-Range Dependence in Daily Volatility on Tunisian Stock Market," Working Papers 0340, Economic Research Forum, revised Dec 2003.
    10. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    11. Aaron D. Smallwood & Stefan C. Norrbin, 2006. "Generalized long memory processes, failure of cointegration tests and exchange rate dynamics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(4), pages 409-417, May.
    12. Laura Mayoral, 2005. "The Persistence of Inflation in OECD Countries:a Fractionally Integrated Approach," Working Papers 259, Barcelona School of Economics.
    13. Stefan C. Norrbin & Aaron D. Smallwood, 2006. "Generalized long memory processes, failure of cointegration tests and exchange rate dynamics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(4), pages 409-417.
    14. Stefan Norrbin & Aaron Smallwood, 2010. "Generalized long memory and mean reversion of the real exchange rate," Applied Economics, Taylor & Francis Journals, vol. 42(11), pages 1377-1386.
    15. Ossama Mikhail & Curtis J. Eberwein & Jagdish Handa, 2003. "Testing and Estimating Persistence in Canadian Unemployment," Econometrics 0311004, University Library of Munich, Germany.
    16. Lovcha, Yuliya & Pérez Laborda, Àlex, 2016. "Structural shocks and dinamic elasticities in a long memory model of the US gasoline retail market," Working Papers 2072/261538, Universitat Rovira i Virgili, Department of Economics.
    17. Mercedes Alda & Luis Ferruz, 2012. "Linear and nonlinear financial time series: evidence in a sample of pension funds in Spain and the United Kingdom," Applied Economics Letters, Taylor & Francis Journals, vol. 19(18), pages 1933-1937, December.
    18. Simeon Coleman, 2008. "Inflation persistence in the Franc Zone: evidence from disaggregated prices," NBS Discussion Papers in Economics 2008/16, Economics, Nottingham Business School, Nottingham Trent University.
    19. Derek Bond & Michael J. Harrison & Edward J. O'Brien, 2005. "Testing for Long Memory and Nonlinear Time Series: A Demand for Money Study," Trinity Economics Papers tep20021, Trinity College Dublin, Department of Economics.
    20. Giorgio Canarella & Stephen M. Miller, 2016. "Inflation Targeting: New Evidence from Fractional Integration and Cointegration," Working papers 2016-08, University of Connecticut, Department of Economics.
    21. Guay, Alain & Pelgrin, Florian, 2023. "Structural VAR models in the Frequency Domain," Journal of Econometrics, Elsevier, vol. 236(1).

  18. Pötscher, B.M., 1995. "Comment on “The Effect of Model Selection on Confidence Regions and Prediction Regions” by P. Kabaila," Econometric Theory, Cambridge University Press, vol. 11(3), pages 550-559, June.

    Cited by:

    1. Ivanov Ventzislav & Kilian Lutz, 2005. "A Practitioner's Guide to Lag Order Selection For VAR Impulse Response Analysis," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(1), pages 1-36, March.

  19. Potscher, Benedikt M., 1995. "Comment on 'Adaptive estimation in time series regression models' by D.G. Steigerwald," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 123-129.

    Cited by:

    1. Drost, F.C. & Klaassen, C.A.J., 1997. "Efficient estimation in semiparametric GARCH models," Other publications TiSEM c7de3f1c-c456-433e-a1c6-2, Tilburg University, School of Economics and Management.
    2. Drost, F.C. & Klaassen, C.A.J. & Werker, B.J.M., 1997. "Adaptive estimation in time-series models," Other publications TiSEM aa253902-af93-4e1e-b974-2, Tilburg University, School of Economics and Management.
    3. Steigerwald, Douglas G., 1995. "Reply to B.M. Potscher's comment on 'adaptive estimation in time series regression models'," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 131-132.

  20. Potscher, Benedikt M. & Prucha, Ingmar R., 1994. "Generic uniform convergence and equicontinuity concepts for random functions : An exploration of the basic structure," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 23-63.

    Cited by:

    1. Francisco Blasques & Siem Jan Koopman & Andre Lucas, 2014. "Maximum Likelihood Estimation for Score-Driven Models," Tinbergen Institute Discussion Papers 14-029/III, Tinbergen Institute, revised 23 Oct 2017.
    2. Luciano de Castro & Antonio F. Galvao & David M. Kaplan, 2017. "Smoothed instrumental variables quantile regression, with estimation of quantile Euler equations," Working Papers 1710, Department of Economics, University of Missouri, revised 28 Feb 2018.
    3. Benedikt M. Pötscher & Ingmar R. Prucha, 1999. "Basic Elements of Asymptotic Theory," Electronic Working Papers 99-001, University of Maryland, Department of Economics.
    4. Luciano de Castro & Antonio F. Galvao & David M. Kaplan & Xin Liu, 2017. "Smoothed GMM for quantile models," Papers 1707.03436, arXiv.org, revised Feb 2018.
    5. Jenish, Nazgul & Prucha, Ingmar R., 2009. "Central limit theorems and uniform laws of large numbers for arrays of random fields," Journal of Econometrics, Elsevier, vol. 150(1), pages 86-98, May.
    6. Mohammad Hashem Pesaran & Yongcheol Shin, 1999. "Long-Run Structural Modelling," Edinburgh School of Economics Discussion Paper Series 44, Edinburgh School of Economics, University of Edinburgh.
    7. Jenish, Nazgul & Prucha, Ingmar R., 2012. "On spatial processes and asymptotic inference under near-epoch dependence," Journal of Econometrics, Elsevier, vol. 170(1), pages 178-190.
    8. Heinz König & Michael Lechner, 1994. "Some Recent Developments in Microeconometrics - A Survey," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 130(III), pages 299-331, September.

  21. Pötscher, B.M., 1991. "Noninvertibility and Pseudo-Maximum Likelihood Estimation of Misspecified ARMA Models," Econometric Theory, Cambridge University Press, vol. 7(4), pages 435-449, December.

    Cited by:

    1. Potscher, Benedikt M., 1995. "Comment on 'Adaptive estimation in time series regression models' by D.G. Steigerwald," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 123-129.
    2. Luukkonen, Ritva & Saikkonen, Pentti, 1996. "Power of the Lagrange multiplier test for testing an autoregressive unit root," Economics Letters, Elsevier, vol. 51(1), pages 27-35, April.
    3. Vougas, Dimitrios V., 2008. "New exact ML estimation and inference for a Gaussian MA(1) process," Economics Letters, Elsevier, vol. 99(1), pages 172-176, April.
    4. James Morley & Irina B. Panovska & Tara M. Sinclair, 2013. "Testing Stationarity for Unobserved Components Models," Discussion Papers 2012-41A, School of Economics, The University of New South Wales.
    5. Guyon, Xavier & Yao, Jian-feng, 1999. "On the Underfitting and Overfitting Sets of Models Chosen by Order Selection Criteria," Journal of Multivariate Analysis, Elsevier, vol. 70(2), pages 221-249, August.
    6. Findley, David F. & Potscher, Benedikt M. & Wei, Ching-Zong, 2004. "Modeling of time series arrays by multistep prediction or likelihood methods," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 151-187.
    7. James Morley & Irina B. Panovska & Tara M. Sinclair, 2014. "Testing Stationarity for Unobserved Components Models," Discussion Papers 2012-41B, School of Economics, The University of New South Wales.

  22. Pötscher, B.M., 1991. "Effects of Model Selection on Inference," Econometric Theory, Cambridge University Press, vol. 7(2), pages 163-185, June.

    Cited by:

    1. Potscher, Benedikt M., 1995. "Comment on 'Adaptive estimation in time series regression models' by D.G. Steigerwald," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 123-129.
    2. Dufour, Jean-Marie & Pelletier, Denis & Renault, Eric, 2006. "Short run and long run causality in time series: inference," Journal of Econometrics, Elsevier, vol. 132(2), pages 337-362, June.
    3. Leeb, Hannes & Potscher, Benedikt M., 2008. "Sparse estimators and the oracle property, or the return of Hodges' estimator," Journal of Econometrics, Elsevier, vol. 142(1), pages 201-211, January.
    4. Xiao, Ruli, 2018. "Identification and estimation of incomplete information games with multiple equilibria," Journal of Econometrics, Elsevier, vol. 203(2), pages 328-343.
    5. Ruth M. Pfeiffer & Andrew Redd & Raymond J. Carroll, 2017. "On the impact of model selection on predictor identification and parameter inference," Computational Statistics, Springer, vol. 32(2), pages 667-690, June.
    6. Doran, Howard E. & Schmidt, Peter, 2006. "GMM estimators with improved finite sample properties using principal components of the weighting matrix, with an application to the dynamic panel data model," Journal of Econometrics, Elsevier, vol. 133(1), pages 387-409, July.
    7. Gernot Doppelhofer & Melvyn Weeks, 2007. "Jointness of Growth Determinants," CESifo Working Paper Series 1978, CESifo.
    8. Gernot Doppelhofer & Xavier Sala I Martin & Melvyn Weeks, 2005. "Jointness of Determinants of Economics Growth," Money Macro and Finance (MMF) Research Group Conference 2005 54, Money Macro and Finance Research Group.
    9. George Kapetanios, 2000. "Incorporating lag order selection uncertainty in parameter inference for AR models," National Institute of Economic and Social Research (NIESR) Discussion Papers 167, National Institute of Economic and Social Research.
    10. Pötscher, Benedikt M. & Leeb, Hannes, 2009. "On the distribution of penalized maximum likelihood estimators: The LASSO, SCAD, and thresholding," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 2065-2082, October.
    11. Hong, Han & Preston, Bruce, 2012. "Bayesian averaging, prediction and nonnested model selection," Journal of Econometrics, Elsevier, vol. 167(2), pages 358-369.
    12. Cragg, John G. & Donald, Stephen G., 1997. "Inferring the rank of a matrix," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 223-250.
    13. Hassler, Uwe, 2010. "Testing regression coefficients after model selection through sign restrictions," Economics Letters, Elsevier, vol. 107(2), pages 220-223, May.
    14. Hidalgo, Javier, 2002. "Consistent order selection with strongly dependent data and its application to efficient estimation," LSE Research Online Documents on Economics 6856, London School of Economics and Political Science, LSE Library.
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    16. Sengupta, Sanchita & Kurkalova, Lyubov A. & Kling, Catherine L., 2006. "Avoiding biases from data-dependent specification search: an application to a tillage choice model," 2006 Annual meeting, July 23-26, Long Beach, CA 21399, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
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    26. Lumsdaine, Robin L. & Okui, Ryo & Wang, Wendun, 2023. "Estimation of panel group structure models with structural breaks in group memberships and coefficients," Journal of Econometrics, Elsevier, vol. 233(1), pages 45-65.
    27. Zaka Ratsimalahelo, 2003. "Rank Test Based On Matrix Perturbation Theory," Econometrics 0306008, University Library of Munich, Germany.
    28. Marcos Herrera & Jesus Mur & Manuel Ruiz-Marin, 2017. "A Comparison Study on Criteria to Select the Most Adequate Weighting Matrix," Working Papers 18, Instituto de Estudios Laborales y del Desarrollo Económico (IELDE) - Universidad Nacional de Salta - Facultad de Ciencias Económicas, Jurídicas y Sociales.
    29. Leeb, Hannes & Pötscher, Benedikt M., 2003. "The Finite-Sample Distribution Of Post-Model-Selection Estimators And Uniform Versus Nonuniform Approximations," Econometric Theory, Cambridge University Press, vol. 19(1), pages 100-142, February.
    30. Paruolo Paolo, 2004. "Automated Inference and the Future of Econometrics: A comment," Economics and Quantitative Methods qf04025, Department of Economics, University of Insubria.
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    33. Christian Kascha & Carsten Trenkler, 2009. "Bootstrapping the likelihood ratio cointegration test in error correction models with unknown lag order," Working Paper 2009/12, Norges Bank.
    34. Leeb, Hannes & Pötscher, Benedikt M. & Ewald, Karl, 2014. "On various confidence intervals post-model-selection," MPRA Paper 52858, University Library of Munich, Germany.
    35. d’Artis Kancs & Julda Kielyte, 2002. "Migration in the Enlarged European Union: Empirical Evidence for Labour Mobility in the Baltic States," EERI Research Paper Series EERI_RP_2002_04, Economics and Econometrics Research Institute (EERI), Brussels.
    36. Hansen, Bruce E., 2005. "Challenges For Econometric Model Selection," Econometric Theory, Cambridge University Press, vol. 21(1), pages 60-68, February.
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  23. B. M. Pötscher, 1990. "Estimation Of Autoregressive Moving‐Average Order Given An Infinite Number Of Models And Approximation Of Spectral Densities," Journal of Time Series Analysis, Wiley Blackwell, vol. 11(2), pages 165-179, March.

    Cited by:

    1. Galeano, Pedro & Peña, Daniel, 2004. "Model selection criteria and quadratic discrimination in ARMA and SETAR time series models," DES - Working Papers. Statistics and Econometrics. WS ws041406, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Donald W.K. Andrews & Liu, Xuemei Liu & Werner Ploberger, 1996. "Tests of Seasonal and Non-Seasonal Serial Correlation," Cowles Foundation Discussion Papers 1124, Cowles Foundation for Research in Economics, Yale University.
    3. Jesús Gonzalo & Jean‐Yves Pitarakis, 2002. "Lag length estimation in large dimensional systems," Journal of Time Series Analysis, Wiley Blackwell, vol. 23(4), pages 401-423, July.
    4. Klasen, Stephan & Herzer, Dierk & Nowak-Lehmann D., Felicitas, 2007. "In search of FDI-led growth in developing countries," Proceedings of the German Development Economics Conference, Göttingen 2007 14, Verein für Socialpolitik, Research Committee Development Economics.
    5. Nankervis, John C. & Savin, N. E., 2010. "Testing for Serial Correlation: Generalized Andrews–Ploberger Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(2), pages 246-255.
    6. Alonso Fernández, Andrés Modesto & Peña, Daniel & Romo, Juan, 2001. "Introducing model uncertainty in time series bootstrap," DES - Working Papers. Statistics and Econometrics. WS ws011409, Universidad Carlos III de Madrid. Departamento de Estadística.

  24. Potscher, Benedikt M & Prucha, Ingmar R, 1989. "A Uniform Law of Large Numbers for Dependent and Heterogeneous Data Processes," Econometrica, Econometric Society, vol. 57(3), pages 675-683, May.
    See citations under working paper version above.
  25. B. M. Pötscher & E. Reschenhofer, 1988. "Discriminating Between Two Spectral Densities In Case Of Replicated Observations," Journal of Time Series Analysis, Wiley Blackwell, vol. 9(3), pages 221-224, May.

    Cited by:

    1. Jentsch, Carsten & Pauly, Markus, 2012. "A note on using periodogram-based distances for comparing spectral densities," Statistics & Probability Letters, Elsevier, vol. 82(1), pages 158-164.
    2. Preuß, Philip & Hildebrandt, Thimo, 2013. "Comparing spectral densities of stationary time series with unequal sample sizes," Statistics & Probability Letters, Elsevier, vol. 83(4), pages 1174-1183.
    3. Mahmoudi, Mohammad Reza, 2021. "A computational technique to classify several fractional Brownian motion processes," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).

  26. Pötscher, B. M., 1987. "Convergence results for maximum likelihood type estimators in multivariable ARMA models," Journal of Multivariate Analysis, Elsevier, vol. 21(1), pages 29-52, February.

    Cited by:

    1. Vicky Fasen-Hartmann & Celeste Mayer, 2022. "Whittle estimation for continuous-time stationary state space models with finite second moments," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(2), pages 233-270, April.
    2. Ghysels, Eric, 2016. "Macroeconomics and the reality of mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 294-314.
    3. Bühlmann, Peter, 1995. "Moving-average representation of autoregressive approximations," Stochastic Processes and their Applications, Elsevier, vol. 60(2), pages 331-342, December.
    4. Findley, David F. & Potscher, Benedikt M. & Wei, Ching-Zong, 2004. "Modeling of time series arrays by multistep prediction or likelihood methods," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 151-187.

  27. Potscher, Benedikt M. & Prucha, Ingmar R., 1986. "A class of partially adaptive one-step m-estimators for the non-linear regression model with dependent observations," Journal of Econometrics, Elsevier, vol. 32(2), pages 219-251, July.

    Cited by:

    1. Olivier Darné & Amélie Charles, 2012. "A note on the uncertain trend in US real GNP: Evidence from robust unit root tests," Economics Bulletin, AccessEcon, vol. 32(3), pages 2399-2406.
    2. Xiao, Zhijie & Lima, Luiz Renato, 2004. "Testing unit root based on partially adaptive estimation," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 528, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    3. Robinson, P.M., 2010. "Efficient estimation of the semiparametric spatial autoregressive model," Journal of Econometrics, Elsevier, vol. 157(1), pages 6-17, July.
    4. Xiao, Zhijie, 2004. "Estimating average economic growth in time series data with persistency," Journal of Macroeconomics, Elsevier, vol. 26(4), pages 699-724, December.
    5. Phillips, Robert F., 1997. "On the robustness of two alternatives to least squares: A Monte Carlo study," Economics Letters, Elsevier, vol. 56(1), pages 21-26, September.
    6. Luiz Lima & Jaime de Jesus Filho, 2008. "Further investigation of the uncertain trend in US GDP," Applied Economics, Taylor & Francis Journals, vol. 40(9), pages 1207-1216.
    7. Duchesne, Pierre, 2004. "On robust testing for conditional heteroscedasticity in time series models," Computational Statistics & Data Analysis, Elsevier, vol. 46(2), pages 227-256, June.
    8. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
    9. Mayte Suarez Farinãs & Carlos Eduardo Pedreira & Marcelo C. Medeiros, 2003. "Local-global neural networks: a new approach for nonlinear time series modelling," Textos para discussão 470, Department of Economics PUC-Rio (Brazil).
    10. Kelejian, Harry H. & Prucha, Ingmar R., 2007. "HAC estimation in a spatial framework," Journal of Econometrics, Elsevier, vol. 140(1), pages 131-154, September.
    11. Peter M Robinson, 2007. "Efficient Estimation of the SemiparametricSpatial Autoregressive Model," STICERD - Econometrics Paper Series 515, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    12. José Luis Aznarte & Marcelo Cunha Medeiros & José Manuel Benítez Sánchez, 2010. "Linearity Testing Against a Fuzzy Rule-based Model," Textos para discussão 566, Department of Economics PUC-Rio (Brazil).
    13. Liebscher, Eckhard, 2003. "Strong convergence of estimators in nonlinear autoregressive models," Journal of Multivariate Analysis, Elsevier, vol. 84(2), pages 247-261, February.
    14. Joel Corrêa da Rosa & Álvaro Veiga & Marcelo C. Medeiros, 2003. "Three-structured smooth transition regression models based on CART algorithm," Textos para discussão 469, Department of Economics PUC-Rio (Brazil).
    15. Robinson, Peter M., 2007. "Efficient estimation of the semiparametric spatial autoregressive model," LSE Research Online Documents on Economics 4535, London School of Economics and Political Science, LSE Library.

  28. B. Pötscher, 1985. "The behaviour of the Lagrangian multiplier test in testing the orders of an ARMA-model," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 32(1), pages 129-150, December.

    Cited by:

    1. Potscher, Benedikt M., 1995. "Comment on 'Adaptive estimation in time series regression models' by D.G. Steigerwald," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 123-129.
    2. Hallin, Marc & Paindaveine, Davy, 2005. "Affine-invariant aligned rank tests for the multivariate general linear model with VARMA errors," Journal of Multivariate Analysis, Elsevier, vol. 93(1), pages 122-163, March.
    3. Donald W.K. Andrews & Werner Ploberger, 1994. "Testing for Serial Correlation Against an ARMA(1,1) Process," Cowles Foundation Discussion Papers 1077, Cowles Foundation for Research in Economics, Yale University.

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