Estimation of Correlated Random Coefficient Models for Short Panels with a Multi-Factor Error Structure


3

Day & Time
7th August 2015, 15:00 ~
Venue
Room C816, Building of the Faculty of Science Graduate school of Science
Lecturer
Professor. Takashi YAMAGATA
(Department of Economics, University of York)
Presentation Title
Estimation of Correlated Random Coefficient Models for Short Panels with a Multi-Factor Error Structure
Abstract
In this paper we develop a consistent and asymptotically efficient GMM estimator of average partial effects in correlated random coefficients panel data models with a small number of time series observations, T.The problem of identification and estimation is studied without imposing the restriction that T is larger than the number of ovariates, which is a necessary condition for mean group type estimators.
In addition, our approach allows for a rich form of correlated nobserved heterogeneity in the residuals, based on a multi-factor error structure.Finite sample evidence shows that the proposed estimator performs well, both in terms of bias and RMSE,as well as size.The methodology is applied to a large sample of bank holding companies, operating in the US, over the period 2004-2012.We find constant returns to scale in the production of value added bank services.By contrast, inference based on standard methods indicates decreasing returns to scale.