IDEAS home Printed from https://ideas.repec.org/a/eee/econom/v195y2016i1p71-85.html
   My bibliography  Save this article

Oracle inequalities, variable selection and uniform inference in high-dimensional correlated random effects panel data models

Author

Listed:
  • Kock, Anders Bredahl

Abstract

In this paper we study high-dimensional correlated random effects panel data models. Our setting is useful as it allows including time invariant covariates as under random effects yet allows for correlation between covariates and unobserved heterogeneity as under fixed effects. We use the Mundlak–Chamberlain device to model this correlation. Allowing for a flexible correlation structure naturally leads to a high-dimensional model in which least squares estimation easily becomes infeasible with even a moderate number of explanatory variables.

Suggested Citation

  • Kock, Anders Bredahl, 2016. "Oracle inequalities, variable selection and uniform inference in high-dimensional correlated random effects panel data models," Journal of Econometrics, Elsevier, vol. 195(1), pages 71-85.
    • Handle: RePEc:eee:econom:v:195:y:2016:i:1:p:71-85
    DOI: 10.1016/j.jeconom.2016.06.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304407616301221
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jeconom.2016.06.001?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Charlotte Christiansen & Juanna Schröter Joensen & Jesper Rangvid, 2008. "Are Economists More Likely to Hold Stocks?," Review of Finance, European Finance Association, vol. 12(3), pages 465-496.
    2. Alexandre Belloni & Victor Chernozhukov, 2011. "High Dimensional Sparse Econometric Models: An Introduction," Papers 1106.5242, arXiv.org, revised Sep 2011.
    3. Caner, Mehmet & Kock, Anders Bredahl, 2018. "Asymptotically honest confidence regions for high dimensional parameters by the desparsified conservative Lasso," Journal of Econometrics, Elsevier, vol. 203(1), pages 143-168.
    4. Chamberlain, Gary, 1984. "Panel data," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 22, pages 1247-1318, Elsevier.
    5. Zhiying Xu & Zhengfei Guan & T.S. Jayne & Roy Black, 2009. "Factors influencing the profitability of fertilizer use on maize in Zambia," Agricultural Economics, International Association of Agricultural Economists, vol. 40(4), pages 437-446, July.
    6. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    7. Qian, Junhui & Su, Liangjun, 2016. "Shrinkage estimation of common breaks in panel data models via adaptive group fused Lasso," Journal of Econometrics, Elsevier, vol. 191(1), pages 86-109.
    8. Papke, Leslie E. & Wooldridge, Jeffrey M., 2008. "Panel data methods for fractional response variables with an application to test pass rates," Journal of Econometrics, Elsevier, vol. 145(1-2), pages 121-133, July.
    9. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    10. Kock, Anders Bredahl, 2013. "Oracle Efficient Variable Selection In Random And Fixed Effects Panel Data Models," Econometric Theory, Cambridge University Press, vol. 29(1), pages 115-152, February.
    11. 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.
    12. Koenker, Roger, 2004. "Quantile regression for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 74-89, October.
    13. Degui Li & Junhui Qian & Su Liangjun, 2015. "Panel Data Models with Interactive Fixed Effects and Multiple Structural Breaks," Working Papers 12-2015, Singapore Management University, School of Economics.
    14. Jianqing Fan & Jinchi Lv & Lei Qi, 2011. "Sparse High-Dimensional Models in Economics," Annual Review of Economics, Annual Reviews, vol. 3(1), pages 291-317, September.
    15. Mehmet Caner & Xu Han, 2014. "Selecting the Correct Number of Factors in Approximate Factor Models: The Large Panel Case With Group Bridge Estimators," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(3), pages 359-374, July.
    16. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2011. "Inference for High-Dimensional Sparse Econometric Models," Papers 1201.0220, arXiv.org.
    17. Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
    18. 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.
    19. Yingying Fan & Cheng Yong Tang, 2013. "Tuning parameter selection in high dimensional penalized likelihood," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(3), pages 531-552, June.
    20. Chamberlain, Gary, 1982. "Multivariate regression models for panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 5-46, January.
    21. Lamarche, Carlos, 2010. "Robust penalized quantile regression estimation for panel data," Journal of Econometrics, Elsevier, vol. 157(2), pages 396-408, August.
    22. Anders Bredahl Kock & Haihan Tang, 2014. "Inference in High-dimensional Dynamic Panel Data Models," CREATES Research Papers 2014-58, Department of Economics and Business Economics, Aarhus University.
    23. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    24. Cun-Hui Zhang & Stephanie S. Zhang, 2014. "Confidence intervals for low dimensional parameters in high dimensional linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 217-242, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ilias Chronopoulos & Katerina Chrysikou & George Kapetanios, 2022. "High Dimensional Generalised Penalised Least Squares," Papers 2207.07055, arXiv.org, revised Oct 2023.
    2. Vogt, M. & Walsh, C. & Linton, O., 2022. "CCE Estimation of High-Dimensional Panel Data Models with Interactive Fixed Effects," Janeway Institute Working Papers 2218, Faculty of Economics, University of Cambridge.
    3. Harold D. Chiang, 2018. "Many Average Partial Effects: with An Application to Text Regression," Papers 1812.09397, arXiv.org, revised Jan 2022.
    4. Caner, Mehmet, 2023. "Generalized linear models with structured sparsity estimators," Journal of Econometrics, Elsevier, vol. 236(2).
    5. Babii, Andrii & Ball, Ryan T. & Ghysels, Eric & Striaukas, Jonas, 2023. "Machine learning panel data regressions with heavy-tailed dependent data: Theory and application," Journal of Econometrics, Elsevier, vol. 237(2).
    6. Oliver Linton & Maximilian Ruecker & Michael Vogt & Christopher Walsh, 2022. "Estimation and Inference in High-Dimensional Panel Data Models with Interactive Fixed Effects," Papers 2206.12152, arXiv.org, revised Nov 2024.
    7. Lamarche, Carlos & Parker, Thomas, 2023. "Wild bootstrap inference for penalized quantile regression for longitudinal data," Journal of Econometrics, Elsevier, vol. 235(2), pages 1799-1826.
    8. Harold D. Chiang & Kengo Kato & Yukun Ma & Yuya Sasaki, 2022. "Multiway Cluster Robust Double/Debiased Machine Learning," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1046-1056, June.
    9. Mehmet Caner & Kfir Eliaz, 2021. "Shoiuld Humans Lie to Machines: The Incentive Compatibility of Lasso and General Weighted Lasso," Papers 2101.01144, arXiv.org, revised Sep 2021.
    10. Jiatong Li & Hongqiang Yan, 2024. "Uniform Inference in High-Dimensional Threshold Regression Models," Papers 2404.08105, arXiv.org, revised Aug 2024.
    11. Linton, O. B. & Rücker, M. & Vogt, M. & Walsh, C., 2024. "Estimation and Inference in High-Dimensional Panel Data Models with Interactive Fixed Effects," Janeway Institute Working Papers 2429, Faculty of Economics, University of Cambridge.
    12. Xi Chen & Ye Luo & Martin Spindler, 2019. "Adaptive Discrete Smoothing for High-Dimensional and Nonlinear Panel Data," Papers 1912.12867, arXiv.org, revised Jan 2020.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Caner, Mehmet & Kock, Anders Bredahl, 2018. "Asymptotically honest confidence regions for high dimensional parameters by the desparsified conservative Lasso," Journal of Econometrics, Elsevier, vol. 203(1), pages 143-168.
    2. Anders Bredahl Kock & Haihan Tang, 2014. "Inference in High-dimensional Dynamic Panel Data Models," CREATES Research Papers 2014-58, Department of Economics and Business Economics, Aarhus University.
    3. Kock, Anders Bredahl & Callot, Laurent, 2015. "Oracle inequalities for high dimensional vector autoregressions," Journal of Econometrics, Elsevier, vol. 186(2), pages 325-344.
    4. Mehmet Caner & Anders Bredahl Kock, 2016. "Oracle Inequalities for Convex Loss Functions with Nonlinear Targets," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1377-1411, December.
    5. Georges Bresson & Guy Lacroix & Mohammad Arshad Rahman, 2021. "Bayesian panel quantile regression for binary outcomes with correlated random effects: an application on crime recidivism in Canada," Empirical Economics, Springer, vol. 60(1), pages 227-259, January.
    6. Tesfaye, Wondimagegn & Tirivayi, Nyasha, 2020. "Crop diversity, household welfare and consumption smoothing under risk: Evidence from rural Uganda," World Development, Elsevier, vol. 125(C).
    7. Arnd Kölling & Claus Schnabel, 2022. "Owners, external managers and industrial relations in German establishments," British Journal of Industrial Relations, London School of Economics, vol. 60(2), pages 424-443, June.
    8. 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.
    9. Stefan Hochguertel & Henry Ohlsson, 2009. "Compensatory inter vivos gifts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(6), pages 993-1023.
    10. Egger, Peter & Keuschnigg, Christian & Merlo, Valeria & Wamser, Georg, 2011. "Corporate Taxes, Internal Borrowing, and the Lending Capacity within Multinational Firms," Economics Working Paper Series 1142, University of St. Gallen, School of Economics and Political Science.
    11. Becker, Annette & Hottenrott, Hanna & Mukherjee, Anwesha, 2022. "Division of labor in R&D? Firm size and specialization in corporate research," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 1-23.
    12. Mason, Nicole & Tembo, Solomon, 2015. "Do input Subsidies Reduce Poverty among Smallholder Farm Households? Panel Survey Evidence from Zambia," 2015 Conference, August 9-14, 2015, Milan, Italy 212233, International Association of Agricultural Economists.
    13. Nicolas Sirven, 2012. "On the Socio-Economic Determinants of Frailty: Findings from Panel and Retrospective Data from SHARE," Working Papers DT52, IRDES institut for research and information in health economics, revised Dec 2012.
    14. Arnd Kölling, 2022. "Monopsony power and the demand for low-skilled workers," The Economic and Labour Relations Review, , vol. 33(2), pages 377-395, June.
    15. Yoshimasa Uematsu & Takashi Yamagata, 2019. "Estimation of Weak Factor Models," DSSR Discussion Papers 96, Graduate School of Economics and Management, Tohoku University.
    16. Nicole M. Mason & Ayala Wineman & Solomon T. Tembo, 2020. "Reducing poverty by ‘ignoring the experts’? Evidence on input subsidies in Zambia," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 12(5), pages 1157-1172, October.
    17. William J. Burke & Thom. S. Jayne & J. Roy Black, 2017. "Factors explaining the low and variable profitability of fertilizer application to maize in Zambia," Agricultural Economics, International Association of Agricultural Economists, vol. 48(1), pages 115-126, January.
    18. Mason, Nicole M. & Tembo, Solomon T., 2014. "Do input subsidies reduce poverty among smallholder farm households? Evidence from Zambia," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170617, Agricultural and Applied Economics Association.
    19. Genya Kobayashi & Hideo Kozumi, 2012. "Bayesian analysis of quantile regression for censored dynamic panel data," Computational Statistics, Springer, vol. 27(2), pages 359-380, June.
    20. Harding, Matthew & Lamarche, Carlos, 2014. "Estimating and testing a quantile regression model with interactive effects," Journal of Econometrics, Elsevier, vol. 178(P1), pages 101-113.

    More about this item

    Keywords

    Panel data; Lasso; Oracle inequality; Sup-norm bounds; High-dimensional models; Weak sparsity; Correlated random effects; Mundlak–Chamberlain; Variable selection; Uniform inference;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:econom:v:195:y:2016:i:1:p:71-85. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jeconom .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.