A0217
Title: Efficient estimation of a semiparametric panel data model with common factors and spatial dependence
Authors: Alexandra Soberon - Universidad de Cantabria (Spain) [presenting]
Antonio Musolesi - University of Ferrara (Italy)
Juan Manuel Rodriguez-Poo - Universidad de Cantabria (Spain)
Abstract: International carbon markets are an appealing and increasingly popular tool for countries to regulate carbon emissions. By putting a price on carbon, carbon markets make pollution less attractive for regulated firms. However, many observers remain skeptical of initiatives such as the European Union Emissions Trading System (EUETS), whose price remained low (compared to the social cost of carbon). The aim is to shed light on this dilemma by analyzing the effect of the EU ETS on CO2 emissions with a semiparametric panel data model where several types of cross-sectional dependence (CSD) and heteroscedasticity are allowed. A new estimator that extends the common correlated effect (CCE) approach to this framework is proposed. However, the initial estimator ignores the CSD and heteroscedasticity, which will lead to a loss of efficiency. Thus, Generalized Least Squares (GLS)-type estimators are proposed. Under rather standard conditions, the parametric estimators are shown to be root-NT consistent, and the asymptotic normality of the nonparametric estimators is also established. Further, the GLS-type estimators are shown to dominate the other. Small sample properties of the estimators are investigated by Monte Carlo experiments and an empirical application on the effect of the EU ETS is conducted.
