Abstract
This paper considers a linear panel data model with time varying heterogeneity. Bayesian inference techniques organized around Markov chain Monte Carlo (MCMC) are applied to implement new estimators that combine smoothness priors on unobserved heterogeneity and priors on the factor structure of unobserved effects. The latter have been addressed in a non-Bayesian framework by Bai (2009) and Kneip et al. (2012), among others. Monte Carlo experiments are used to examine the finite-sample performance of our estimators. An empirical study of efficiency trends in the largest banks operating in the U.S. from 1990 to 2009 illustrates our new estimators. The study concludes that scale economies in intermediation services have been largely exploited by these large U.S. banks.
Highlights
In this paper, we consider two panel data models with unobserved heterogeneous time-varying effects; one with individual effects treated as random functions of time, and the other with common factors whose number is unknown and whose effects are firm-specific
This paper has proposed a Bayesian approach to treat time-varying heterogeneity in a panel data stochastic frontier model setting
We introduce two new models: one with nonparametric time effects and one with effects that are driven by a number of unknown common factors
Summary
We consider two panel data models with unobserved heterogeneous time-varying effects; one with individual effects treated as random functions of time, and the other with common factors whose number is unknown and whose effects are firm-specific. We develop methods that allow us to interpret the effects as measures of technical efficiency in the spirit of the structural productivity approaches of Olley and Pakes (1996) and non-structural approaches from the stochastic frontier literature (Kumbhakar and Lovell 2000; Fried et al 2008). The general factor structure we utilize can pick up potential nonlinear selection effects that may be introduced when using a balanced panel of firms. Our dynamic heterogeneity estimators could be interpreted as general controls for any mis-specified factors, such as selectivity due to entry/exit, that are correlated with the regressors and could bias slope.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
