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Individual and time effects in nonlinear panel models with large N, T

Author

Listed:
  • Ivan Fernandez-Val

    (Institute for Fiscal Studies and Boston University)

  • Martin Weidner

    () (Institute for Fiscal Studies and cemmap and UCL)

Abstract

Fixed effects estimators of nonlinear panel data models can be severely biased because of the incidental parameter problem. We develop analytical and jackknife bias corrections for nonlinear models with both individual and time effects. Under asymptotic sequences where the time-dimension (T) grows with the cross-sectional dimension (N), the time effects introduce additional incidental parameter bias. As the existing bias corrections apply to models with only individual effects, we derive the appropriate corrections for the case when both effects are present. The basis for the corrections are general asymptotic expansions of fixed effects estimators with incidental parameters in multiple dimensions. We apply the expansions to conditional maximum likelihood estimators with concave objective functions in parameters for panel models with additively separable individual and time effects. These estimators cover fixed effects estimators of the most popular limited dependent variable models such as logit, probit, ordered probit, Tobit and Poisson models. Our analysis therefore extends the use of large-T bias adjustments to an important class of models. We also analyze the properties of fixed effects estimators of functions of the data, parameters and individual and time effects including average partial effects. Here, we uncover that the incidental parameter bias is asymptotically of second order, because the rate of the convergence of the fixed effects estimators is slower for average partial effects than for model parameters. The bias corrections are still effective to improve finite-sample properties. View the supplementary document for this paper here.

Suggested Citation

  • Ivan Fernandez-Val & Martin Weidner, 2015. "Individual and time effects in nonlinear panel models with large N, T," CeMMAP working papers CWP17/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:cemmap:17/15
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Bryan S. Graham, 2014. "An econometric model of link formation with degree heterogeneity," NBER Working Papers 20341, National Bureau of Economic Research, Inc.
    3. Koen Jochmans, 2017. "Two-Way Models for Gravity," The Review of Economics and Statistics, MIT Press, vol. 99(3), pages 478-485, July.
    4. repec:eee:jeeman:v:86:y:2017:i:c:p:193-209 is not listed on IDEAS
    5. Larch, Mario & Yotov, Yoto, 2016. "General Equilibrium Trade Policy Analysis with Structural Gravity," School of Economics Working Paper Series 2016-9, LeBow College of Business, Drexel University.
    6. Geert Dhaene & Koen Jochmans, 2015. "Split-panel Jackknife Estimation of Fixed-effect Models," Review of Economic Studies, Oxford University Press, vol. 82(3), pages 991-1030.
    7. Gonçalves, Sílvia & Kaffo, Maximilien, 2015. "Bootstrap inference for linear dynamic panel data models with individual fixed effects," Journal of Econometrics, Elsevier, vol. 186(2), pages 407-426.
    8. Moon, Hyungsik Roger & Weidner, Martin, 2017. "Dynamic Linear Panel Regression Models With Interactive Fixed Effects," Econometric Theory, Cambridge University Press, vol. 33(01), pages 158-195, February.
    9. Bonhomme, Stéphane & Lamadon, Thibaut & Manresa, Elena, 2017. "Discretizing Unobserved Heterogeneity," Working Paper Series 2017:21, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    10. James E. Anderson & Mario Larch & Yoto V. Yotov, 2015. "Estimating General Equilibrium Trade Policy Effects: GE PPML," CESifo Working Paper Series 5592, CESifo Group Munich.
    11. Mario Cruz-Gonzalez & Ivan Fernandez-Val & Martin Weidner, 2016. "probitfe and logitfe: Bias corrections for probit and logit models with two-way fixed effects," Papers 1610.07714, arXiv.org, revised Feb 2017.
    12. Mario Larch & Joschka Wanner & Yoto V. Yotov & Thomas Zylkin, 2017. "The Currency Union Effect: A PPML Re-assessment with High-Dimensional Fixed Effects," CESifo Working Paper Series 6464, CESifo Group Munich.
    13. Áureo de Paula, 2015. "Econometrics of network models," CeMMAP working papers CWP52/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Torben G. Andersen & Nicola Fusari & Viktor Todorov & Rasmus T. Varneskov, 1001. "Unified Inference for Nonlinear Factor Models from Panels with Fixed and Large Time Span," CREATES Research Papers 2018-03, Department of Economics and Business Economics, Aarhus University.
    15. Koen Jochmans, 2016. "Semiparametric Analysis of Network Formation," Sciences Po Economics Discussion Papers 2016-02, Sciences Po Departement of Economics.
    16. Karyne B. Charbonneau, 2014. "Multiple Fixed Effects in Binary Response Panel Data Models," Staff Working Papers 14-17, Bank of Canada.
    17. repec:eee:jimfin:v:74:y:2017:i:c:p:88-114 is not listed on IDEAS
    18. Ida Johnsson & Hyungsik Roger Moon, 2017. "Estimation of Peer Effects in Endogenous Social Networks: Control Function Approach," Papers 1709.10024, arXiv.org.
    19. Koen Jochmans, 2017. "Two-Way Models for Gravity," The Review of Economics and Statistics, MIT Press, vol. 99(3), pages 478-485, July.
    20. Mingli Chen & Ivan Fernandez-Val & Martin Weidner, 2014. "Nonlinear Panel Models with Interactive Effects," Papers 1412.5647, arXiv.org.
    21. Figueiredo, Octávio & Guimarães, Paulo & Woodward, Douglas, 2015. "Industry localization, distance decay, and knowledge spillovers: Following the patent paper trail," Journal of Urban Economics, Elsevier, vol. 89(C), pages 21-31.
    22. Lake, James & Linask, Maia K., 2016. "Could tariffs be pro-cyclical?," Journal of International Economics, Elsevier, vol. 103(C), pages 124-146.
    23. David Powell, 2015. "Do Payroll Taxes in the United States Create Bunching at Kink Points?," Working Papers wp327, University of Michigan, Michigan Retirement Research Center.
    24. Van Gestel, R.; Müller, T.; Bosmans, J.;, 2017. "Learning from failure in healthcare: dynamic panel evidence of a physician shock effect," Health, Econometrics and Data Group (HEDG) Working Papers 17/24, HEDG, c/o Department of Economics, University of York.
    25. Gonzalo, Jesús & Dolado Lobregad, Juan José & Chen, Liang, 2017. "Quantile Factor Models," UC3M Working papers. Economics 25299, Universidad Carlos III de Madrid. Departamento de Economía.

    More about this item

    Keywords

    Panel data; nonlinear model; dynamic model; asymptotic bias correction; ?xed e?ects; time e?ects;

    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

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