The pollution-convergence hypothesis is formalized in a neoclassical growth model with optimal emissions reduction: pollution growth rates are positively correlated with output growth (scale effect) but negatively correlated with emission levels (defensive effect). This dynamic law is...

Although panel data have been used intensively by a wealth of studies investigating the GDP-pollution relationship, the poolability assumption used to model these data is almost never addressed. This paper applies a strategy to test the poolability assumption with methods robust to functional...

Ground-level ozone is an important pollutant regulated under the Clean Air Acts that affects respiratory morbidity, decreases lung function, and negatively affects those with existing respiratory conditions like asthma. This study examines the “Clean Air Works” program on ozone concentration...

This paper estimates the efficiency of the power generation sector in the USA by using Window Data Envelopment Analysis (W-DEA). We integrate radial and non-radial efficiency measurements in DEA using the hybrid measure while we extend the proposed model by considering inputs and good and bad...

The paper examines nitrous oxide (N₂O) emissions from an Environmental, Social, and Governance (ESG) standpoint with a combination of econometric and machine learning specifications to uncover global trends and policy implications. Results show the overwhelming effect of ESG factors on...

I study the identification of time preferences in dynamic discrete choice models. Time preferences play a crucial role in these models, as they affect inference and counterfactual analysis. Previous literature has shown that observed choice probabilities do not identify the exponential discount...

If the population of a given country evolves according to Gibrat'�s Law, its growth rate will be independent of its initial size. This short paper further investigates this empirical regularity by the application of a suitable panel unit root test and non-parametric methods. The evidence...

We propose a new methodology for estimating semiparametric panel data models, with a primary focus on the nonparametric component. We eliminate individual effects using first differencing transformation and estimate the unknown function by marginal integration. We extend our methodology to treat...

This pdf contains a do file that shows how to estimate a latent class discrete choice panel data model in Stata via Maximum Likelihood and an EM algorithm.

The relationship between calorie and nutrient (fat, protein, and carbohydrates) intake as a function of income is explored using data for 171 countries over two time periods 1990-1992 and 2003-2005. Three types of analysis are employed: i) nonparametric, ii) panel regressions, and iii) quantile...