Cui, Guowei and Norkute, Milda and Sarafidis, Vasilis and Yamagata, Takashi (2020): Two-Stage Instrumental Variable Estimation of Linear Panel Data Models with Interactive Effects.
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Abstract
This paper puts forward a new instrumental variables (IV) approach for linear panel data models with interactive effects in the error term and regressors. The instruments are transformed regressors and so it is not necessary to search for external instruments. The proposed method asymptotically eliminates the interactive effects in the error term and in the regressors separately in two stages. We propose a two-stage IV (2SIV) and a mean-group IV (MGIV) estimator for homogeneous and heterogeneous slope models, respectively. The asymptotic analysis for the models with homogeneous slopes reveals that: (i) the \sqrt{NT}-consistent 2SIV estimator is free from asymptotic bias that could arise due to the correlation between the regressors and the estimation error of the interactive effects; (ii) under the same set of assumptions, existing popular estimators, which eliminate interactive effects either jointly in the regressors and the error term, or only in the error term, can suffer from asymptotic bias; (iii) the proposed 2SIV estimator is asymptotically as efficient as the bias-corrected version of estimators that eliminate interactive effects jointly in the regressors and the error, whilst; (iv) the relative efficiency of the estimators that eliminate interactive effects only in the error term is indeterminate. A Monte Carlo study confirms good approximation quality of our asymptotic results and competent performance of 2SIV and MGIV in comparison with existing estimators. Furthermore, it demonstrates that the bias-corrections can be imprecise and noticeably inflate the dispersion of the estimators in finite samples.
Item Type: | MPRA Paper |
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Original Title: | Two-Stage Instrumental Variable Estimation of Linear Panel Data Models with Interactive Effects |
Language: | English |
Keywords: | Large panel data; interactive effects; common factors; principal components analysis; instrumental variables. |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C23 - Panel Data Models ; Spatio-temporal Models C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C26 - Instrumental Variables (IV) Estimation |
Item ID: | 102827 |
Depositing User: | Vasilis Sarafidis |
Date Deposited: | 15 Sep 2020 14:36 |
Last Modified: | 15 Sep 2020 14:37 |
References: | Ahn, S., Horenstein, A., 2013. Eigenvalue ratio test for the number of factors. Econometrica 81, 1203–1227. Bai, J., 2003. Inferential theory for factor models of large dimensions. Econometrica 71, 135–173. Bai, J., 2009a. Panel data models with interactive fixed effects. Econometrica 77, 1229–1279. Bai, J., 2009b. Supplement to “panel data models with interactive fixed effects”: technical details and proofs. Econometrica Supplemental Material. Bai, J., Li, K., 2014. Theory and methods of panel data models with interactive effects. Annals of Statistics 42, 142–170. Bai, J., Ng, S., 2002. Determining the number of factors in approximate factor models. Econometrica 70, 191–221. Bun, M., Kiviet, J., 2006. The effects of dynamic feedbacks on LS and MM estimator accuracy in panel data models. Journal of Econometrics 132, 409–444. Cui, G., Hayakawa, K., Nagata, S., Yamagata, T., 2020. A robust approach to heteroskedasticity, error serial correlation and slope heterogeneity for large linear panel data models with interactive effects. Mimeo. Hansen, C., 2007. Asymptotic properties of a robust variance matrix estimator for panel data when T is large. Journal of Econometrics 141, 597–620. Jiang, B., Yang, Y., Gao, J., Hsiao, C., 2017. Recursive estimation in large panel data models: Theory and practice. Working Paper 05/17. Monash University. Juodis, A., Karabiyik, H., Westerlund, J., 2020. On the robustness of the pooled cce estimator. Journal of Econometrics 00, 1–24. Juodis, A., Sarafidis, V., 2018. Fixed t dynamic panel data estimators with multifactor errors. Econometric Reviews 37, 893–929. Juodis, A., Sarafidis, V., 2020. A linear estimator for factor-augmented fixed-t panels with endogenous regressors. Journal of Business & Economic Statistics 00, 1–15. Kapetanios, G., 2010. A testing procedure for determining the number of factors in approximate factor models with large datasets. Journal of Business & Economic Statistics 28, 397 – 409. Kapetanios, G., Pesaran, H.M., 2005. Alternative approaches to estimation and inference in large multifactor panels: Small sample results with an application to modelling of asset returns. CESIFO WORKING PAPER NO. 1416. Kripfganz, S., Sarafidis, V., 2020. Instrumental variable estimation of large panel data models with common factors. Available at SSRN. Moon, H.R., Weidner, M., 2015. Linear regression for panel with unknown number of factors as interactive fixed effects. Econometrica 83, 1543–1579. Moon, H.R., Weidner, M., 2017. Dynamic linear panel regression models with interactive fixed effects. Econometric Theory 33, 158–195. Norkute, M., Sarafidis, V., Yamagata, T., Cui, G., 2020. Instrumental variable estimation of dynamic linear panel data models with defactored regressors and a multifactor error structure. Journal of Econometrics forthcoming, 1–31. Pesaran, M.H., 2006. Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica 74, 967–1012. Reese, S., Westerlund, J., 2018. Estimation of factor-augmented panel regressions with weakly influential factors. Econometric Reviews 37, 401–465. Sarafidis, V., Wansbeek, T., 2020. Celebrating 40 years of panel data analysis: Past, present and future. Journal of Econometrics 00, 1–12. Westerlund, J., Urbain, J.P., 2015. Cross-sectional averages versus principal components. Journal of Econometrics 185, 372 – 377. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/102827 |