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Statistical inference in a random coefficient panel model

Horv�th, Lajos; Trapani, Lorenzo

Authors

Lajos Horv�th

Lorenzo Trapani



Abstract

This paper studies the asymptotics of the Weighted Least Squares (WLS) estimator of the autoregressive root in a panel Random Coefficient Autoregression (RCA). We show that, in an RCA context, there is no “unit root problem” : the WLS estimator is always asymptotically normal, irrespective of the average value of the autoregressive root, of whether the autoregressive coefficient is random or not, and of the presence and degree of cross dependence. Our simulations indicate that the estimator has good properties, and that confidence intervals have the correct coverage even for sample sizes as small as (N,T)=(10,25)(N,T)=(10,25). We illustrate our findings through two applications to macroeconomic and financial variables.

Citation

Horváth, L., & Trapani, L. (2016). Statistical inference in a random coefficient panel model. Journal of Econometrics, 193(1), https://doi.org/10.1016/j.jeconom.2016.01.006

Journal Article Type Article
Acceptance Date Jan 25, 2016
Online Publication Date Feb 16, 2016
Publication Date Jul 1, 2016
Deposit Date Oct 3, 2017
Publicly Available Date Mar 28, 2024
Journal Journal of Econometrics
Print ISSN 0304-4076
Electronic ISSN 0304-4076
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 193
Issue 1
DOI https://doi.org/10.1016/j.jeconom.2016.01.006
Keywords Random Coefficient Autoregression; Panel data; WLS estimator; Common factors
Public URL https://nottingham-repository.worktribe.com/output/976071
Publisher URL http://www.sciencedirect.com/science/article/pii/S0304407616300203

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