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THIS SHOCK IS DIFFERENT: ESTIMATION AND INFERENCE IN MISSPECIFIED TWO-WAY FIXED EFFECTS PANEL REGRESSIONS

Published online by Cambridge University Press:  27 October 2025

Artūras Juodis*
Affiliation:
University of Amsterdam and Tinbergen Institute
*
Artūras Juodis, Amsterdam School of Economics, University of Amsterdam, Amsterdam, The Netherlands, e-mail: a.juodis@uva.nl.
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Abstract

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We investigate the properties of the linear two-way fixed effects (FE) estimator for panel data when the underlying data generating process (DGP) does not have a linear parametric structure. The FE estimator is consistent for some pseudo-true value and we characterize the corresponding asymptotic distribution. We show that the rate of convergence is determined by the degree of model misspecification, and that the asymptotic distribution can be non-normal. We propose a novel autoregressive double adaptive wild (AdaWild) bootstrap procedure applicable for a large class of DGPs. Monte Carlo simulations show that it performs well for panels of small and moderate dimensions. We use data from U.S. manufacturing industries to illustrate the benefits of our procedure.

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Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (https://creativecommons.org/licenses/by-nc-nd/4.0), which permits non-commercial re-use, distribution, and reproduction in any medium, provided that no alterations are made and the original article is properly cited. The written permission of Cambridge University Press must be obtained prior to any commercial use and/or adaptation of the article.
Copyright
© The Author(s), 2025. Published by Cambridge University Press

Footnotes

I would like to thank the Co-Editor Guido Kuersteiner and three anonymous referees for numerous suggestions that greatly improved this article. I would also like to thank Stephane Bonhomme, Tom Boot, Jorg Breitung, Denis Chetverikov, Ivan Fernandez-Val, Yi He, Aureo de Paula, Anders Bredahl Kock, Frank Kleibergen, Ryo Okui, Andres Santos, Vasilis Sarafidis, Gabriela Szini, Gautam Tripathi, Martin Weidner, Joakim Westerlund, as well as the participants of the 2021 International Panel Data Conference, 2023 Annual IAAE Conference (Oslo), and seminar participants at U Amsterdam, Oxford, UCLA, U Duisburg-Essen, U Lund, U Luxembourg, U Cologne, and Tinbergen Econometrics Workshop for helpful comments. Preliminary version of this article was completed while I enjoyed the hospitality of the Department of Economics at the University College London in May 2019. Financial support from the Netherlands Organization for Scientific Research (NWO) under research grant number 451-17-002 is gratefully acknowledged.

References

REFERENCES

Alvarez, J., & Arellano, M. (2003). The time series and cross-section asymptotics of dynamic panel data estimators. Econometrica , 71(4), 11211159.10.1111/1468-0262.00441CrossRefGoogle Scholar
Andrews, D. W. K. (2005). Cross-section regression with common shocks. Econometrica , 73, 15511585.CrossRefGoogle Scholar
Arellano, M. (1987). Computing robust standard errors for within-groups estimators. Oxford Bulletin of Economics and Statistics , 49, 431434.10.1111/j.1468-0084.1987.mp49004006.xCrossRefGoogle Scholar
Bai, J. (2009). Panel data models with interactive fixed effects. Econometrica , 77, 12291279.Google Scholar
Berger, D., Easterly, W., Nunn, N., & Satyanath, S. (2013). Commercial imperialism? Political influence and trade during the cold war. American Economic Review , 103, 863896.10.1257/aer.103.2.863CrossRefGoogle Scholar
Bücher, A., & Kojadinovic, I. (2019). A note on conditional versus joint unconditional weak convergence in bootstrap consistency results. Journal of Theoretical Probability , 32, 11451165.10.1007/s10959-018-0823-3CrossRefGoogle Scholar
Callaway, B., & Sant’Anna, P. H. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics , 225, 200230.CrossRefGoogle Scholar
Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2011). Robust inference with multiway clustering. Journal of Business & Economic Statistics , 29, 238249.10.1198/jbes.2010.07136CrossRefGoogle Scholar
Chambers, M. J. (2013). Jackknife estimation of stationary autoregressive models. Journal of Econometrics , 172, 142157.10.1016/j.jeconom.2012.09.003CrossRefGoogle Scholar
Chen, L., Dolado, J. J., & Gonzalo, J. (2021a). Quantile factor models. Econometrica , 89, 875910.10.3982/ECTA15746CrossRefGoogle Scholar
Chen, M., Fernández-Val, I., & Weidner, M. (2021b). Nonlinear factor models for network and panel data,” Journal of Econometrics , 220, 296324.Google Scholar
Chiang, H. D., Hansen, B. E., & Sasaki, Y. (2024). Standard Errors for Two-Way Clustering with Serially Correlated Time Effects, The Review of Economics and Statistics , 140.Google Scholar
Chudik, A., Pesaran, M. H., & Yang, J.-C. (2018). Half-panel jackknife fixed effects estimation of panels with weakly exogenous regressor. Journal of Applied Econometrics , 33, 816836.10.1002/jae.2623CrossRefGoogle Scholar
Ciccone, A., & Peri, G. (2005). Long-run substitutability between more and less educated workers: evidence from U.S. States, 1950–1990. The Review of Economics and Statistics , 87, 652663.10.1162/003465305775098233CrossRefGoogle Scholar
Davezies, L., D’Haultfœuille, X., & Guyonvarch, Y. (2021). Empirical process results for exchangeable arrays. Annals of Statistics , 49, 845862.10.1214/20-AOS1981CrossRefGoogle Scholar
de Chaisemartin, C., & D’Haultfœuille, X. (2020). Two-way fixed effects estimators with heterogeneous treatment effects. American Economic Review , 110, 29642996.10.1257/aer.20181169CrossRefGoogle Scholar
Dhaene, G., & Jochmans, K. (2015). Split-panel jackknife estimation of fixed-effect models. Review of Economic Studies , 82, 9911030.CrossRefGoogle Scholar
Doukhan, P., Lang, G., Leucht, A., & Neumann, M. H. (2015). Dependent wild bootstrap for the empirical process. Journal of Time Series Analysis , 36, 290314.10.1111/jtsa.12106CrossRefGoogle Scholar
Driscoll, J. C., & Kraay, A. C. (1998). Consistent covariance matrix estimation with spatially dependent panel data. The Review of Economics and Statistics , 80, 549560.Google Scholar
Fernández-Val, I., Freeman, H., & Weidner, M. (2021). Low-rank approximations of nonseparable panel models. Econometrics Journal , 24(2), C40C77.10.1093/ectj/utab007CrossRefGoogle Scholar
Fernández-Val, I., & Lee, J. (2013). Panel data models with nonadditive unobserved heterogeneity: estimation and inference. Quantitative Economics , 4, 453481.10.3982/QE75CrossRefGoogle Scholar
Fernández-Val, I., & Weidner, M. (2016). Individual and time effects in nonlinear panel models with large N, T. Journal of Econometrics , 192, 291312.10.1016/j.jeconom.2015.12.014CrossRefGoogle Scholar
Friedrich, M., Smeekes, S., & Urbain, J.-P. (2020). Autoregressive wild bootstrap inference for nonparametric trends. Journal of Econometrics , 214, 81109.10.1016/j.jeconom.2019.05.006CrossRefGoogle Scholar
Galvao, A. F., & Kato, K. (2014). Estimation and inference for linear panel data models under misspecification when both n and T are large. Journal of Business & Economic Statistics , 32, 285309.10.1080/07350015.2013.875473CrossRefGoogle Scholar
Goodman-Bacon, A. (2021). Difference-in-differences with variation in treatment timing. Journal of Econometrics , 225, 254277.10.1016/j.jeconom.2021.03.014CrossRefGoogle Scholar
Hahn, J., Hughes, D. W., Kuersteiner, G., and Newey, W. K. (2024). Efficient bias correction for cross-section and panel data. Quantitative Economics , 15, 783816.10.3982/QE2350CrossRefGoogle Scholar
Hahn, J., & Kuersteiner, G. (2002). Asymptotically unbiased inference for a dynamic panel model with fixed effects when both n and T are large. Econometrica , 70(4), 16391657.10.1111/1468-0262.00344CrossRefGoogle Scholar
Hahn, J., & Kuersteiner, G. (2011). Bias reduction for dynamic nonlinear panel data models with fixed effects. Econometric Theory , 27, 11521191.10.1017/S0266466611000028CrossRefGoogle Scholar
Hahn, J., Kuersteiner, G., & Mazzocco, M. (2022). Joint time series and cross-section limit theory under mixingale assumptions. Econometric Theory , 38, 942958.CrossRefGoogle Scholar
Hahn, J., & Moon, H. R. (2006). Reducing bias of MLE in a dynamic panel model,” Econometric Theory , 22, 499512.10.1017/S0266466606060245CrossRefGoogle Scholar
Hahn, J., & Newey, W. (2004). Jackknife and analytical bias reduction for nonlinear panel models. Econometrica , 72(4), 12951319.10.1111/j.1468-0262.2004.00533.xCrossRefGoogle Scholar
Hoeffding, W. (1948). A class of statistics with asymptotically normal distribution. Annals of Mathematical Statistics , 19, 293325.CrossRefGoogle Scholar
Imai, K., & Kim, I. S. (2021). On the use of two-way fixed effects regression models for causal inference with panel data. Political Analysis , 29, 405415.10.1017/pan.2020.33CrossRefGoogle Scholar
Juodis, A. (2022). A regularization approach to common correlated effects estimation. Journal of Applied Econometrics , 37, 788810.10.1002/jae.2899CrossRefGoogle Scholar
Juodis, A., Karabiyik, H., & Westerlund, J. (2021). On the robustness of the pooled CCE estimator. Journal of Econometrics , 220, 325348.CrossRefGoogle Scholar
Juodis, A., & Reese, S. (2022). The incidental parameters problem in testing for remaining cross-section correlation. Journal of Business and Economic Statistics , 40, 11911203.10.1080/07350015.2021.1906687CrossRefGoogle Scholar
Juodis, A., & Sarafidis, V. (2022a). An incidental parameters free inference approach for panels with common factors. Journal of Econometrics , 229, 1954.10.1016/j.jeconom.2021.03.011CrossRefGoogle Scholar
Juodis, A., & Sarafidis, V. (2022b). A linear estimator for factor-augmented fixed-T panels with endogenous regressors. Journal of Business and Economic Statistics , 40, 115.CrossRefGoogle Scholar
Kapetanios, G., Serlenga, L., & Shin, Y. (2024). An LM test for the conditional independence between regressors and factor loadings in panel data models with interactive effects. Journal of Business and Economic Statistics , 42, 743761.10.1080/07350015.2023.2238774CrossRefGoogle Scholar
Menzel, K. (2021). Bootstrap with cluster-dependence in two or more dimensions. Econometrica , 89, 21432188.10.3982/ECTA15383CrossRefGoogle Scholar
Nickell, S. (1981). Biases in dynamic models with fixed effects. Econometrica , 49, 14171426.10.2307/1911408CrossRefGoogle Scholar
Okui, R. (2014). Asymptotically unbiased estimation of autocovariances and autocorrelations with panel data in the presence of individual and time effects. Journal of Time Series Econometrics , 6, 129181.10.1515/jtse-2013-0017CrossRefGoogle Scholar
Okui, R. (2017). Misspecification in dynamic panel data models and model-free inferences. Japanese Economic Review , 68, Edition 3, 283304.10.1111/jere.12080CrossRefGoogle Scholar
Okui, R., & Yanagi, T. (2019). Panel data analysis with heterogeneous dynamics. Journal of Econometrics , 212, 451475.CrossRefGoogle Scholar
Pesaran, M. H. (2006). Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica , 74, 9671012.10.1111/j.1468-0262.2006.00692.xCrossRefGoogle Scholar
Petrova, Y., & Westerlund, J. (2020). Fixed effects demeaning in the presence of interactive effects in treatment effects regressions and elsewhere. Journal of Applied Econometrics , 35, 960964.10.1002/jae.2790CrossRefGoogle Scholar
Phillips, P. C. B., & Moon, H. R. (1999). Linear regression limit theory for nonstationary panel data. Econometrica , 67, 10571111.10.1111/1468-0262.00070CrossRefGoogle Scholar
Shao, X. (2010). The dependent wild bootstrap. Journal of the American Statistical Association , 105, 218235.CrossRefGoogle Scholar
Su, L., Jin, S., & Zhang, Y. (2015). Specification test for panel data models with interactive fixed effects. Journal of Econometrics , 186, 222244.10.1016/j.jeconom.2014.06.018CrossRefGoogle Scholar
Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics , 225, 175199.10.1016/j.jeconom.2020.09.006CrossRefGoogle Scholar
Thompson, S. B. (2011). Simple formulas for standard errors that cluster by both firm and time. Journal of Financial Economics , 99, 110.10.1016/j.jfineco.2010.08.016CrossRefGoogle Scholar
Voigtländer, N. (2014). Skill bias magnified: Intersectoral linkages and white-collar labor demand in U.S. manufacturing. The Review of Economics and Statistics , 96, 495513.10.1162/REST_a_00390CrossRefGoogle Scholar
White, H. (1982). Maximum likelihood estimation of misspecified models. Econometrica , 50, 125.10.2307/1912526CrossRefGoogle Scholar
Yin, S.-Y., Liu, C.-A., & Lin, C.-C. (2021). Focused information criterion and model averaging for large panels with a multifactor error structure. Journal of Business & Economic Statistics , 39, 5468.10.1080/07350015.2019.1623044CrossRefGoogle Scholar
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