This paper investigates the relationship between urbanization and CO2 emissions in a sample of 20 emerging countries over the period 1992–2008 using the semi-parametric panel data model with fixed effects, proposed by Baltagi and Li (2002). We find little evidence in support of an inverted-U...

This paper constructs the simultaneous confidence band for the nonparametric function in nonparametric fixed effects panel data models. We first transform the nonparametric fixed effects panel data models into the partially linear models. We then obtain the estimator of the nonparametric...

We propose an empirical likelihood method for application to a partially linear panel data model with fixed effects. The empirical log-likelihood ratio statistic is proved to be asymptotically chi-squared distributed, and the asymptotic properties of estimators for both the parametric and...

I examine the distribution dynamics of incomes across Indian states using the entire income distribution. Unlike standard regression approaches, this approach allows us to identify specific distributional characteristics such as polarisation and stratification. The period between 1965 to 1997...

We examine the (potentially nonlinear) relationship between inequality and growth using a method which does not require an a priori assumption on the underlying functional form. This approach reveals a plateau completely missed by commonly used (nonlinear) parametric approaches—the economy...

This paper is concerned with maximum likelihood based inference in random effects models with serial correlation. Allowing for individual effects we introduce serial correlation of general form in the time effects as well as the idiosyncratic errors. A straightforward maximum likelihood...

This paper considers the large sample behavior of the maximum likelihood estimator of random effects models with serial correlation in the form of AR(1) for the idiosyncratic or time-specific error component. Consistent estimation and asymptotic normality as N and/or T grows large is established...

This paper is concerned with maximum likelihood based inference in random effects models with serial correlation. Allowing for individual effects we introduce serial correlation of general form in the time effects as well as the idiosyncratic errors. A straightforward maximum likelihood...

This paper considers the large sample behavior of the maximum likelihood estimator of random effects models with serial correlation in the form of AR(1) for the idiosyncratic or time-specific error component. Consistent estimation and asymptotic normality as N and/or T grows large is established...

We obtain an LM test for the random effects probit model. In the natural parameterization of the model the necessary derivatives are identically zero under the null hypothesis. After a reparameterization, the feasible LM test is based on generalized residuals.