Seminario "Common Correlated Effects Estimation of Heterogeneous Dynamic Panel Quantile Regression Models”
Miércoles 28 de junio, 17h | Sala 4 (Piso 4)
Presentado por Carlos Lamarche
“Common Correlated Effects Estimation of Heterogeneous Dynamic Panel Quantile Regression Models”
por Matthew Harding (University of California at Irvine), Carlos Lamarche (University of Kentucky) y Hashem Pesaran (University of Southern California)
Abstract
This paper proposes a quantile regression estimator for a heterogeneous panel model with lagged dependent variables and interactive effects. The paper adopts the Common Correlated Effects (CCE) approach proposed by Pesaran (2006) and Chudik and Pesaran (2015) and demonstrates that the extension to the estimation of dynamic quantile regression models is feasible under similar conditions to the ones used in the literature. We establish consistency and derive the asymptotic distribution of the new quantile regression estimator. Monte Carlo studies are carried out to study the small sample behavior of the proposed approach. The evidence shows that the estimator can significantly improve the performance of existing estimators as long as the time series dimension of the panel is large. We present an application to the evaluation of Time-of-Use pricing using a large randomized control trial.
Carlos Lamarche is Gatton Endowed Associate Professor of Economics at the University of Kentucky. Prior to assuming his position at the University of Kentucky, Professor Lamarche was Assistant Professor and Chong K. Liew Associate Professor of Economics at the University of Oklahoma. He is an econometrician who conducts research on theoretical and applied econometrics with a focus on labor economics, microeconomic development, and empirical industrial organization. His research interest includes theoretical and empirical aspects of quantile regression for panel data. His work has been published in the Journal of Econometrics, the Journal of Applied Econometrics, the Journal of Public Economics, the Journal of Development Economics, and the Journal of the American Statistical Association, among others. He is Research Fellow of the Institute for Labor Studies in Germany, Associate Editor of Empirical Economics, and received several research awards for his work on Quantile Regression. Lamarche received his MS. in Statistics in 2003 and his Ph.D. in Economics from the University of Illinois at Urbana-Champaign in 2006.
por Matthew Harding (University of California at Irvine), Carlos Lamarche (University of Kentucky) y Hashem Pesaran (University of Southern California)
Abstract
This paper proposes a quantile regression estimator for a heterogeneous panel model with lagged dependent variables and interactive effects. The paper adopts the Common Correlated Effects (CCE) approach proposed by Pesaran (2006) and Chudik and Pesaran (2015) and demonstrates that the extension to the estimation of dynamic quantile regression models is feasible under similar conditions to the ones used in the literature. We establish consistency and derive the asymptotic distribution of the new quantile regression estimator. Monte Carlo studies are carried out to study the small sample behavior of the proposed approach. The evidence shows that the estimator can significantly improve the performance of existing estimators as long as the time series dimension of the panel is large. We present an application to the evaluation of Time-of-Use pricing using a large randomized control trial.
Carlos Lamarche is Gatton Endowed Associate Professor of Economics at the University of Kentucky. Prior to assuming his position at the University of Kentucky, Professor Lamarche was Assistant Professor and Chong K. Liew Associate Professor of Economics at the University of Oklahoma. He is an econometrician who conducts research on theoretical and applied econometrics with a focus on labor economics, microeconomic development, and empirical industrial organization. His research interest includes theoretical and empirical aspects of quantile regression for panel data. His work has been published in the Journal of Econometrics, the Journal of Applied Econometrics, the Journal of Public Economics, the Journal of Development Economics, and the Journal of the American Statistical Association, among others. He is Research Fellow of the Institute for Labor Studies in Germany, Associate Editor of Empirical Economics, and received several research awards for his work on Quantile Regression. Lamarche received his MS. in Statistics in 2003 and his Ph.D. in Economics from the University of Illinois at Urbana-Champaign in 2006.