Juodis, Arturas and Sarafidis, Vasilis (2020): Online Supplement to An Incidental Parameters Free Inference Approach for Panels with Common Shocks.
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Abstract
This paper is an online supplementary appendix to "An Incidental Parameters Free Inference Approach for Panels with Common Shocks". Section S.1 of the present Supplementary Appendix studies the properties of the proposed GMM estimators under fixed T asymptotics. Section S.2 analyses the effect of transforming the model in terms of time-specific cross-sectional averages on the proposed estimating equations. Section S.3 considers identification-robust inference, building upon the idea of Anderson and Rubin (1949) and Stock and Wright (2000). Finally, Section S.4 discusses local and global identification for the panel AR(1) model and reports additional Monte Carlo results for this model.
Item Type: | MPRA Paper |
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Original Title: | Online Supplement to An Incidental Parameters Free Inference Approach for Panels with Common Shocks |
Language: | English |
Keywords: | Common Factors; GMM; Incidental Parameter Problem; Endogenous Regressors; U-statistic |
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 > C1 - Econometric and Statistical Methods and Methodology: General > C18 - Methodological Issues: General C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C33 - Panel Data Models ; Spatio-temporal Models |
Item ID: | 104908 |
Depositing User: | Vasilis Sarafidis |
Date Deposited: | 23 Dec 2020 15:05 |
Last Modified: | 23 Dec 2020 15:05 |
References: | Ahn, S. C., Y. H. Lee, and P. Schmidt (2013): “Panel Data Models with Multiple Time-varying Individual Effects,” Journal of Econometrics, 174, 1-14. Anderson, T. W. and C. Hsiao (1982): “Formulation and Estimation of Dynamic Models Using Panel Data,” Journal of Econometrics, 18, 47-82. Anderson, T. W. and H. Rubin (1949): “Estimation of the Parameters of a Single Equation in a Complete System of Stochastic Equations,” Annals of Mathematical Statistics, 20, 46-63. Andrews, D. W. K. (2005): “Cross-Section Regression with Common Shocks,” Econometrica, 73, 1551-1585. Andrews, I. (2016): “Conditional Linear Combination Tests for Weakly Identified Models,” Econometrica, 84, 2155-2182. Andrews, I. (2018): “Valid Two-Step Identification-Robust Confidence Sets for GMM,” The Review of Economics and Statistics, 100, 337-348. Andrews, I. and A. Mikusheva (2016): “Conditional Inference With a Functional Nuisance Parameter,” Econometrica, 84, 1571-1612. Bun, M. J. G. and F. R. Kleibergen (2016): “Identification and Inference in Moments Based Analysis of Linear Dynamic Panel Data Models,” UvA-Econometrics Working Paper Series. Bun, M. J. G. and V. Sarafidis (2015): “Dynamic Panel Data Models,” in The Oxford Handbook of Panel Data, ed. by B. H. Baltagi, Oxford: Oxford University Press, chap. 3. Chaudhuri, S. and E. Zivot (2011): “A New Method of Projection-based Inference in GMM with Weakly Identified Nuisance Parameters,” Journal of Econometrics, 164, 239 - 251. Dovonon, P., A. Hall, and F. R. Kleibergen (2020): “Inference in Second-Order Identified Models,” Journal of Econometrics, (forthcoming). Dufour, J.-M. and M. Taamouti (2005): “Projection-Based Statistical Inference in Linear Structural Models with Possibly Weak Instruments,” Econometrica, 73, 1351-1365. Hall, P. and C. C. Heyde (1980): Martingale Limit Theory and Its Application, Probability and Mathematical Statistics, Academic Press. Hayakawa, K. (2009): “On the effect of mean-nonstationarity in dynamic panel data models,” Journal of Econometrics, 153, 133-135. Juodis, A. and V. Sarafidis (2018): “Fixed T Dynamic Panel Data Estimators with Multi-Factor Errors,” Econometric Reviews, 37, 893-929. Juodis, A. and V. Sarafidis (2020): “A Linear Estimator for Factor-Augmented Fixed-T Panels With Endogenous Regressors,” Journal of Business & Economic Statistics, DOI: 10.1080/07350015.2020.1766469. Kleibergen, F. R. (2005): “Testing Parameters in GMM without Assuming that They are Identified,” Econometrica, 73, 1103-1123. Kuersteiner, G. and I. R. Prucha (2013): “Limit Theory for Panel Data Models with Cross Sectional Dependence and Sequential Exogeneity,” Journal of Econometrics, 174, 107-126. Kuersteiner, G. and I. R. Prucha (2020): “Dynamic Spatial Panel Models: Networks, Common Shocks, and Sequential Exogeneity,” Econometrica, (forthcoming). Moreira, M. J. (2003): “A Conditional Likelihood Ratio Test for Structural Models,” Econometrica, 71, 1027-1048. Newey, W. K. and D. McFadden (1994): “Large Sample Estimation and Hypothesis Testing,” in Handbook of Econometrics, ed. by J. Heckman and E. Leamer, Amsterdam: Elsevier, vol. 4, chap. 36, 2111-2245. Robertson, D. and V. Sarafidis (2015): “IV Estimation of Panels with Factor Residuals,” Journal of Econometrics, 185, 526-541. Robertson, D., V. Sarafidis, and J. Westerlund (2018): “Unit Root Inference in Generally Trending and Cross-Correlated Dynamic Panels,” Journal of Business & Economic Statistics, 36, 493-504. Stock, J. H. and J. H. Wright (2000): “GMM with Weak Identification,” Econometrica, 68, 1055-1096. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/104908 |