Despite its paramount importance in the empirical growth literature, productivity convergence analysis has three problems that have yet to be resolved: (1) little attempt has been made to explore the hierarchical structure of industry-level datasets; (2) industry-level technology heterogeneity...

In this paper, we propose a variable selection procedure based on the shrinkage estimation technique for a categorical varying-coefficient model. We apply the method to identify the relevant determinants for body mass index (BMI) from a large amount of potential factors proposed in the...

A novel and unified approach is proposed in sieve estimation to tackle the issue of unbounded support of variables in nonparametric regression models. The model em- braces time trend and both stationary and nonstationary variables that are allowed to be correlated. This approach is introduced...

In this paper, we consider a partially linear panel data model with cross-sectional dependence and non-stationarity. Meanwhile, we allow fixed effects to be correlated with the regressors to capture unobservable heterogeneity. Under a general spatial error dependence structure, we then establish...

In this paper, we propose a single-index panel data model with unobserved multiple interactive fixed effects. This model has the advantages of being flexible and of being able to allow for common shocks and their heterogeneous impacts on cross sections, thus making it suitable for the...

This paper investigates the productivity and efficiency of large bank holding companies (BHCs) in the United States over the period 2004–2013, by estimating a translog stochastic distance frontier (SDF) model with time-varying heterogeneity. The main feature of this model is that a...

Empirical growth analysis is plagued with three problems — variable selection, parameter heterogeneity and cross-sectional dependence — which are addressed independently from each other in most studies. The purpose of this study is to pro- pose an integrated framework that extends the...