IDEAS home Printed from https://ideas.repec.org/p/kyo/wpaper/886.html
   My bibliography  Save this paper

Deriving the Information Bounds for Nonlinear Panel Data Models with Fixed Effects

Author

Listed:
  • Haruo Iwakura

    (Graduate School of Economics, Kyoto University)

Abstract

This paper studies the asymptotic efficiency of estimates in nonlinear panel data models with fixed effects when both the cross-sectional sample size and the length of time series tend to infinity. The efficiency bounds for regular estimators are derived using the infinite-dimensional convolution theorem by van der Varrt and Wellner (1996). It should be noted that the number of fixed effects increases with the sample size, so they constitute an infinite-dimensional nuisance parameter. The presence of fixed effects makes our derivation of the efficiency bounds non-trivial, and the techniques to overcome the difficulties caused by fixed effects will be discussed in detail. Our results include the efficiency bounds for models containing unknown functions (for instance, a distribution function of error terms). We apply our results to show that the bias-corrected fixed effects estimator of Hahn and Newey (2004) is asymptotically efficient.

Suggested Citation

  • Haruo Iwakura, 2014. "Deriving the Information Bounds for Nonlinear Panel Data Models with Fixed Effects," KIER Working Papers 886, Kyoto University, Institute of Economic Research.
    • Handle: RePEc:kyo:wpaper:886
    as

    Download full text from publisher

    File URL: http://www.kier.kyoto-u.ac.jp/DP/DP886.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ivan A. Canay, 2011. "A simple approach to quantile regression for panel data," Econometrics Journal, Royal Economic Society, vol. 14(3), pages 368-386, October.
    2. Koenker, Roger, 2004. "Quantile regression for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 74-89, October.
    3. Jinyong Hahn & Guido Kuersteiner, 2002. "Asymptotically Unbiased Inference for a Dynamic Panel Model with Fixed Effects when Both "n" and "T" Are Large," Econometrica, Econometric Society, vol. 70(4), pages 1639-1657, July.
    4. Fernández-Val, Iván & Vella, Francis, 2011. "Bias corrections for two-step fixed effects panel data estimators," Journal of Econometrics, Elsevier, vol. 163(2), pages 144-162, August.
    5. Peter C. B. Phillips & Hyungsik R. Moon, 1999. "Linear Regression Limit Theory for Nonstationary Panel Data," Econometrica, Econometric Society, vol. 67(5), pages 1057-1112, September.
    6. Jinyong Hahn & Whitney Newey, 2004. "Jackknife and Analytical Bias Reduction for Nonlinear Panel Models," Econometrica, Econometric Society, vol. 72(4), pages 1295-1319, July.
    7. Hahn, Jinyong, 2002. "Optimal Inference With Many Instruments," Econometric Theory, Cambridge University Press, vol. 18(1), pages 140-168, February.
    8. Ivan Fernandez-Val, 2005. "Estimation of Structural Parameters and Marginal Effects in Binary Choice Panel Data Models with Fixed Effects," Boston University - Department of Economics - Working Papers Series WP2005-38, Boston University - Department of Economics.
    9. Hahn, Jinyong & Kuersteiner, Guido, 2011. "Bias Reduction For Dynamic Nonlinear Panel Models With Fixed Effects," Econometric Theory, Cambridge University Press, vol. 27(6), pages 1152-1191, December.
    10. Tiemen Woutersen, 2002. "Robustness against Incidental Parameters," University of Western Ontario, Departmental Research Report Series 20028, University of Western Ontario, Department of Economics.
    Full references (including those not matched with items on IDEAS)

    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. Galvao, Antonio F. & Gu, Jiaying & Volgushev, Stanislav, 2020. "On the unbiased asymptotic normality of quantile regression with fixed effects," Journal of Econometrics, Elsevier, vol. 218(1), pages 178-215.
    2. Chen, Mingli & Fernández-Val, Iván & Weidner, Martin, 2021. "Nonlinear factor models for network and panel data," Journal of Econometrics, Elsevier, vol. 220(2), pages 296-324.
    3. Ivan Fernandez-Val & Martin Weidner, 2015. "Individual and time effects in nonlinear panel models with large N , T," CeMMAP working papers 17/15, Institute for Fiscal Studies.
    4. Fernández-Val, Iván & Vella, Francis, 2011. "Bias corrections for two-step fixed effects panel data estimators," Journal of Econometrics, Elsevier, vol. 163(2), pages 144-162, August.
    5. Fernández-Val, Iván & Weidner, Martin, 2016. "Individual and time effects in nonlinear panel models with large N, T," Journal of Econometrics, Elsevier, vol. 192(1), pages 291-312.
    6. Weidner, Martin & Zylkin, Thomas, 2021. "Bias and consistency in three-way gravity models," Journal of International Economics, Elsevier, vol. 132(C).
    7. Geert Dhaene & Koen Jochmans, 2015. "Split-panel Jackknife Estimation of Fixed-effect Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(3), pages 991-1030.
    8. Galvao, Antonio F. & Kato, Kengo, 2016. "Smoothed quantile regression for panel data," Journal of Econometrics, Elsevier, vol. 193(1), pages 92-112.
    9. Fernández-Val, Iván, 2009. "Fixed effects estimation of structural parameters and marginal effects in panel probit models," Journal of Econometrics, Elsevier, vol. 150(1), pages 71-85, May.
    10. Ivan Fernandez-Val & Martin Weidner, 2013. "Individual and time effects in nonlinear panel models with large N, T," CeMMAP working papers 60/13, Institute for Fiscal Studies.
    11. repec:hal:spmain:info:hdl:2441/f6h8764enu2lskk9p2m9mgp8l is not listed on IDEAS
    12. repec:spo:wpmain:info:hdl:2441/f6h8764enu2lskk9p2m9mgp8l is not listed on IDEAS
    13. repec:spo:wpecon:info:hdl:2441/f6h8764enu2lskk9p2m9mgp8l is not listed on IDEAS
    14. repec:hal:wpspec:info:hdl:2441/f6h8764enu2lskk9p2m9mgp8l is not listed on IDEAS
    15. Ivan Fernandez-Val & Martin Weidner, 2014. "Individual and time effects in nonlinear panel models with large N , T," CeMMAP working papers 32/14, Institute for Fiscal Studies.
    16. Galvao, Antonio F. & Wang, Liang, 2015. "Efficient minimum distance estimator for quantile regression fixed effects panel data," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 1-26.
    17. repec:spo:wpmain:info:hdl:2441/eu4vqp9ompqllr09ij4j0h0h1 is not listed on IDEAS
    18. Dhaene, Geert & Jochmans, Koen, 2016. "Likelihood Inference In An Autoregression With Fixed Effects," Econometric Theory, Cambridge University Press, vol. 32(5), pages 1178-1215, October.
    19. Schumann, Martin & Severini, Thomas A. & Tripathi, Gautam, 2021. "Integrated likelihood based inference for nonlinear panel data models with unobserved effects," Journal of Econometrics, Elsevier, vol. 223(1), pages 73-95.
    20. Pakel, Cavit, 2019. "Bias reduction in nonlinear and dynamic panels in the presence of cross-section dependence," Journal of Econometrics, Elsevier, vol. 213(2), pages 459-492.
    21. Fernández-Val, Iván & Gao, Wayne Yuan & Liao, Yuan & Vella, Francis, 2022. "Dynamic Heterogeneous Distribution Regression Panel Models, with an Application to Labor Income Processes," IZA Discussion Papers 15236, Institute of Labor Economics (IZA).
    22. Victor Chernozhukov & Iv·n Fern·ndez-Val & Alfred Galichon, 2010. "Quantile and Probability Curves Without Crossing," Econometrica, Econometric Society, vol. 78(3), pages 1093-1125, May.
    23. L. Hospido, 2012. "Modelling heterogeneity and dynamics in the volatility of individual wages," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(3), pages 386-414, April.
    24. repec:spo:wpmain:info:hdl:2441/1mc4dip81d9t8r0t57fe1h8lap is not listed on IDEAS
    25. Jochmans, Koen & Higgins, Ayden, 2022. "Bootstrap inference for fixed-effect models," TSE Working Papers 22-1328, Toulouse School of Economics (TSE), revised Dec 2023.
    26. Antonio F. Galvao & Thomas Parker & Zhijie Xiao, 2024. "Bootstrap Inference for Panel Data Quantile Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(2), pages 628-639, April.

    More about this item

    Keywords

    asymptotic efficiency; convolution theorem; double asymptotics; nonlinear panel data model; fixed effects; interactive effects; factor structure; incidental parameters.;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:kyo:wpaper:886. 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: Makoto Watanabe (email available below). General contact details of provider: https://edirc.repec.org/data/iekyojp.html .

    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.