Bias-Corrected Common Correlated Effects Pooled Estimation in Dynamic Panels

Autor: Ignace De Vos, Gerdie Everaert
Přispěvatelé: Econometrics and Data Science
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: De Vos, I & Everaert, G 2021, ' Bias-Corrected Common Correlated Effects Pooled Estimation in Dynamic Panels ', Journal of Business and Economic Statistics, vol. 39, no. 1, pp. 294-306 . https://doi.org/10.1080/07350015.2019.1654879
Journal of Business and Economic Statistics, 39(1), 294-306. American Statistical Association
Journal of Business & Economic Statistics; 39(1), pp 294-306 (2021)
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
ISSN: 0735-0015
1537-2707
DOI: 10.1080/07350015.2019.1654879
Popis: This article extends the common correlated effects pooled (CCEP) estimator to homogenous dynamic panels. In this setting, CCEP suffers from a large bias when the time span (T) of the dataset is fixed. We develop a bias-corrected CCEP estimator that is consistent as the number of cross-sectional units (N) tends to infinity, for T fixed or growing large, provided that the specification is augmented with a sufficient number of cross-sectional averages, and lags thereof. Monte Carlo experiments show that the correction offers strong improvements in terms of bias and variance. We apply our approach to estimate the dynamic impact of temperature shocks on aggregate output growth.
Databáze: OpenAIRE
Nepřihlášeným uživatelům se plný text nezobrazuje