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New results on asymptotic properties of likelihood estimators with persistent data for small and large T

Author

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  • Artūras Juodis

    (University of Amsterdam
    Tinbergen Institute)

  • Vasilis Sarafidis

    (Brunel University London
    BI Norwegian Business School)

Abstract

This paper revisits the panel autoregressive model, with a primary emphasis on the unit-root case. We study a class of misspecified Random effects Maximum Likelihood (mRML) estimators when T is either fixed or large, and N tends to infinity. We show that in the unit-root case, for any fixed value of T, the log-likelihood function of the mRML estimator has a single mode at unity as $$N\rightarrow \infty $$ N → ∞ . Furthermore, the Hessian matrix of the corresponding log-likelihood function is non-singular, unless the scaled variance of the initial condition is exactly zero. As a result, mRML is consistent and asymptotically normally distributed as N tends to infinity. In the large-T setup, it is shown that mRML is asymptotically equivalent to the bias-corrected FE estimator of Hahn and Kuersteiner (Econometrica 70(4):1639–1657, 2002). Moreover, under certain conditions, its Hessian matrix remains non-singular.

Suggested Citation

  • Artūras Juodis & Vasilis Sarafidis, 2023. "New results on asymptotic properties of likelihood estimators with persistent data for small and large T," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 14(3), pages 435-461, December.
    • Handle: RePEc:spr:series:v:14:y:2023:i:3:d:10.1007_s13209-023-00286-y
    DOI: 10.1007/s13209-023-00286-y
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    References listed on IDEAS

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    1. Jushan Bai, 2013. "Fixed‐Effects Dynamic Panel Models, a Factor Analytical Method," Econometrica, Econometric Society, vol. 81(1), pages 285-314, January.
    2. Binder, Michael & Hsiao, Cheng & Pesaran, M. Hashem, 2005. "Estimation And Inference In Short Panel Vector Autoregressions With Unit Roots And Cointegration," Econometric Theory, Cambridge University Press, vol. 21(4), pages 795-837, August.
    3. Hsiao, Cheng & Hashem Pesaran, M. & Kamil Tahmiscioglu, A., 2002. "Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods," Journal of Econometrics, Elsevier, vol. 109(1), pages 107-150, July.
    4. Bun, Maurice J.G. & Carree, Martin A., 2005. "Bias-Corrected Estimation in Dynamic Panel Data Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 200-210, April.
    5. Alvarez, Javier & Arellano, Manuel, 2022. "Robust likelihood estimation of dynamic panel data models," Journal of Econometrics, Elsevier, vol. 226(1), pages 21-61.
    6. Botosaru, Irene & Sasaki, Yuya, 2018. "Nonparametric heteroskedasticity in persistent panel processes: An application to earnings dynamics," Journal of Econometrics, Elsevier, vol. 203(2), pages 283-296.
    7. Arellano, Manuel, 2016. "Modelling optimal instrumental variables for dynamic panel data models," Research in Economics, Elsevier, vol. 70(2), pages 238-261.
    8. Juodis, Artūras & Sarafidis, Vasilis, 2022. "An incidental parameters free inference approach for panels with common shocks," Journal of Econometrics, Elsevier, vol. 229(1), pages 19-54.
    9. Moon, Hyungsik Roger & Perron, Benoit & Phillips, Peter C.B., 2007. "Incidental trends and the power of panel unit root tests," Journal of Econometrics, Elsevier, vol. 141(2), pages 416-459, December.
    10. Joakim Westerlund & Milda Norkutė & Ovidijus Stauskas, 2022. "The factor analytical approach in trending near unit root panels," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(3), pages 501-508, May.
    11. Maddala, G S, 1971. "The Use of Variance Components Models in Pooling Cross Section and Time Series Data," Econometrica, Econometric Society, vol. 39(2), pages 341-358, March.
    12. Hayakawa, Kazuhiko & Pesaran, M. Hashem, 2015. "Robust standard errors in transformed likelihood estimation of dynamic panel data models with cross-sectional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 188(1), pages 111-134.
    13. Javier Alvarez & Manuel Arellano, 2003. "The Time Series and Cross-Section Asymptotics of Dynamic Panel Data Estimators," Econometrica, Econometric Society, vol. 71(4), pages 1121-1159, July.
    14. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    15. Kruiniger, Hugo, 2018. "A further look at Modified ML estimation of the panel AR(1) model with fixed effects and arbitrary initial conditions," MPRA Paper 88623, University Library of Munich, Germany.
    16. Artūras Juodis, 2018. "First difference transformation in panel VAR models: Robustness, estimation, and inference," Econometric Reviews, Taylor & Francis Journals, vol. 37(6), pages 650-693, July.
    17. 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.
    18. Manuel Arellano & Stéphane Bonhomme, 2009. "Robust Priors in Nonlinear Panel Data Models," Econometrica, Econometric Society, vol. 77(2), pages 489-536, March.
    19. Joakim Westerlund, 2016. "Pooled Panel Unit Root Tests and the Effect of Past Initialization," Econometric Reviews, Taylor & Francis Journals, vol. 35(3), pages 396-427, March.
    20. Manuel Arellano & Richard Blundell & Stéphane Bonhomme, 2017. "Earnings and Consumption Dynamics: A Nonlinear Panel Data Framework," Econometrica, Econometric Society, vol. 85, pages 693-734, May.
    21. Artūras Juodis & Joakim Westerlund, 2019. "Optimal panel unit root testing with covariates," The Econometrics Journal, Royal Economic Society, vol. 22(1), pages 57-72.
    22. Kruiniger, Hugo, 2013. "Quasi ML estimation of the panel AR(1) model with arbitrary initial conditions," Journal of Econometrics, Elsevier, vol. 173(2), pages 175-188.
    23. Chamberlain, Gary, 1982. "Multivariate regression models for panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 5-46, January.
    24. Gerdie Everaert, 2013. "Orthogonal to backward mean transformation for dynamic panel data models," Econometrics Journal, Royal Economic Society, vol. 16(2), pages 179-221, June.
    25. Norkutė, Milda & Westerlund, Joakim, 2021. "The factor analytical approach in near unit root interactive effects panels," Journal of Econometrics, Elsevier, vol. 221(2), pages 569-590.
    26. Tony Lancaster, 2002. "Orthogonal Parameters and Panel Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 69(3), pages 647-666.
    27. Enrique Moral-Benito, 2013. "Likelihood-Based Estimation of Dynamic Panels With Predetermined Regressors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(4), pages 451-472, October.
    28. Hahn, Jinyong & Kuersteiner, Guido & Cho, Myeong Hyeon, 2004. "Asymptotic distribution of misspecified random effects estimator for a dynamic panel model with fixed effects when both n and T are large," Economics Letters, Elsevier, vol. 84(1), pages 117-125, July.
    29. Anderson, T. W. & Hsiao, Cheng, 1982. "Formulation and estimation of dynamic models using panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 47-82, January.
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    More about this item

    Keywords

    Dynamic panel data; Maximum likelihood; Monte Carlo simulation;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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