Research and development-based growth models aim to explain the role of technological progress in the growth process. The role of knowledge production and intertemporal spillover effects are investigated using a panel data set covering 49 US states over the period 1994-2004. The aim is to estimate knowledge flows in the context of a space-time dynamic suggested by the knowledge production funct...

We consider consistent estimation of partially linear panel data models with fixed effects. We propose profile-likelihood-based estimators for both the parametric and nonparametric components in the models and establish convergence rates and asymptotic normality for both estimators.

A panel data regression model with heteroskedastic as well as spatially correlated disturbancesis considered, and a joint LM test for homoskedasticity and no spatial correlation is derived. In addition, a conditional LM test for no spatial correlation given heteroskedasticity, as well as a conditional LM test for homoskedasticity given spatial correlation, are also derived. These LM tests are c...

This paper provides an overview of recent econometric advances on testing of functional form with semiparametric methods and their application to the estimation of the environmental Kuznets curve (EKC). The EKC in environmental economics is estimated using panel data models and provides a fertile ground for the adoption of recent panel data developments. The paper provides a discussion of recen...

Recent years have seen countries introducing reform of their utility industries with a view to promoting private ownership and competition. This paper studies the effect of the sequencing of privatisation, competition and regulation reforms in electricity generation using data from 25 developing countries for the period 1985 to 2001. A fixed effects panel data model is used. The study finds tha...

Forecasting with Spatial Panel Data This paper compares various forecasts using panel data with spatial error correlation. The true data generating process is assumed to be a simple error component regression model with spatial remainder disturbances of the autoregressive or moving average type. The best linear unbiased predictor is compared with other forecasts ignoring spatial correlation, or...