We propose an estimation procedure for (semiparametric) panel data censored regression models in which the error terms may be subject to general forms of non-stationarity, thus permitting heteroscedasticity over time. The proposed estimator exploits a weak structural form imposed on the individual speci ̄c e®ect. This is in contrast to the estimators introduced in Honor¶e(1992) where the individ...

In this paper we provide a new methodology to analyze the (Gaussian) profile quasi likelihood function for panel regression models with interactive fixed effects, also called factor models. The number of factors is assumed to be known. Employing the perturbation theory of linear operators, we derive a power series expansion of the likelihood function in the regression parameters. Using this exp...

This work is related to the studying of poverty dynamics and the analysis of the socio-demographic factors influencing it. We have chosen a fuzzy and multidimmensional approach in order to define two different poverty measures. A panel regression model has been estimated and particular attention has been paid to the treatment of the unobservable heterogeneity among longitudial units. The specif...

This paper extends the panel threshold regression of Hansen (1999) and Kourtellos, Stengos and Tan (2007) to allow for endogeneity of the threshold variable. We consider the static linear panels with fixed effect. The modified concentrated two-stage least square methods that are based on inverse Mills ratio bias correction terms are proposed to estimate threshold parameters. Our estimators are ...

This paper derives several Lagrange Multiplier tests for the panel data regression model wih spatial error correlation. These tests draw upon two strands of earlier work. The Þrst is the LM tests for the spatial error correlation model discussed in Anselin (1988, 1999) and Anselin, Bera, Florax and Yoon (1996), and the second is the LM tests for the error component panel data model discussed in...

Linear models with error components are widely used to analyze panel data. Some applications of these models require knowledge of the probability densities of the error components. Existing methods handle this requirement by assuming that the densities belong to known parametric families of distributions (typically the normal distribution). This paper shows how to carry out nonparametric estima...