This paper introduces an estimation procedure for a random effects probit model in presence of heteroskedasticity and a likelihood ratio test for homoskedasticity. The cases where the heteroskedasticity is due to individual effects or idiosyncratic errors or both are analyzed. Monte Carlo...

The problem of instrument proliferation and its consequences (overfitting of the endogenous explanatory variables, biased IV and GMM estimators, weakening of the power of the overidentification tests) are well known. This paper introduces a statistical method to reduce the instrument count. The...

Panel data of our interest consist of a moderate number of panels, while the panels contain a small number of observations. An estimator of common breaks in panel means without a boundary issue for this kind of scenario is proposed. In particular, the novel estimator is able to detect a common...

A new empirical reduced-form model for credit rating transitions is introduced. It is a parametric intensity-based duration model with multiple states and driven by exogenous covariates and latent dynamic factors. The model has a generalized semi-Markov structure designed to accommodate many of...

This paper focuses on the estimation of fiscal response functions for advanced economies and on the performance of alternative specifications of the Generalized Method of Moments (GMM) estimator for the rule's parameters. We first estimate the parameters on simulated data through Monte Carlo...

This paper focuses on the estimation of fiscal response functions for advanced economies and on the performance of alternative specifications of the Generalized Method of Moments (GMM) estimator for the rule's parameters. We first estimate the parameters on simulated data through Monte Carlo...

In this paper we propose a simple procedure for data dependent determination of the number of lags and leads to use in feasible estimation of cointegrated panel regressions. Results from Monte Carlo simulations suggests that the feasible estimators considered enjoys excellent precision in terms...

This paper examines the panel data models when the regression coefficients are fixed, random, and mixed, and proposed the different estimators for this model. We used the Mote Carlo simulation for making comparisons between the behavior of several estimation methods, such as Random Coefficient...

This paper gives a brief survey of forecasting with panel data. Starting with a simple error component regression and surveying best linear unbiased prediction under various assumptions of the disturbance term. This includes various ARMA models as well as spatial autoregressive models. The paper...

This paper focusses on the estimation of error components models in the presence of a correlation of the disturbances across equations and AR(1) of the remainder disturbances for panel data with endogenous unobserved effects. Additionally, the set-up allows for unequally spaced panel data and...