With the introduction of a destination-based VAT in all but eight states starting April 2005, there is need for a good baseline indicator of tax buoyancies in states in the period immediately preceding. This paper attempts to provide such a base, with buoyancies estimated over a twenty-three...

We propose four different GMM estimators that allow almost consistent estimation of the structural parameters of panel probit models with fixed effects for the case of small T and large N. The moments used are derived for each period from a first order approximation of the mean of the dependent...

This paper is motivated by the US freight railroad industry, which is characterized by a major restructuring over the last 30 years. In particular, the number of active firms decreased from 26 in 1978 to seven in 2006 due to several takeover waves. The empirical focus concerns the estimation of...

Portfolio sorting is ubiquitous in the empirical finance literature, where it has been widely used to identify pricing anomalies in different asset classes. Despite the popularity of portfolio sorting, little attention has been paid to the statistical properties of the procedure or to the...

With the increased availability of longitudinal data, dynamic panel data models have become commonplace. Moreover, the properties of various estimators of such models are well known. However, we show that these estimators breakdown when the data are irregularly spaced along the time dimension....

This paper analyzes the impact of load factor, facility and generator types on the productivity of Korean electric power plants. In order to capture important differences in the effect of load policy on power output, we use a semiparametric smooth coefficient (SPSC) model that allows us to model...

The truncated bivariate normal stochastic frontier model (TBN-SFM), which is globally unidentifiable on its natural parameter space, has been recently shown to be locally near-identifiable even under the identifying parametric restrictions. Maximum Likelihood estimation of such a model is...

With the emergence of telematics car driving data, insurance companies start to boost classical actuarial regression models for claim frequency prediction. In this paper, we propose two data-driven neural network approaches that process telematics car driving data to construct driving behavior...

This paper presents a convenient shortcut method for implementing the Heckman estimator of the dynamic random effects probit model using standard software. It then compares the three estimators proposed by Heckman, Orme and Wooldridge based on three alternative approximations, first in an...