The Generalized Method of Moments (GMM) is discussed for handling the joint occurrence of fixed effects and random measurement errors in an autoregressive panel data model. Finite memory of disturbances, latent regressors and measurement errors is assumed. Two specializations of GMM are...

The Generalized Method of Moments (GMM) is discussed for handling the joint occurrence of fixed effects and random measurement errors in an autoregressive panel data model. Finite memory of disturbances, latent regressors and measurement errors is assumed. Two specializations of GMM are...

An autoregressive fixed effects panel data equation in error-ridden endogenous and exogenous variables, with finite memory of disturbances, latent regressors and measurement errors is considered. Finite sample properties of GMM estimators are explored by Monte Carlo (MC) simulations. Two kinds...

GMM estimation of autoregressive panel data equations in error-ridden variables when the noise has memory, is considered. The impact of variation in the memory length in signal and noise spread and in the degree of individual heterogeneity are discussed with respect to finite sample bias, using...

This study uses Monte Carlo experiments to produce new evidence on the performance of a wide range of panel data estimators. It focuses on estimators that are readily available in statistical software packages such as Stata and Eviews, and for which the number of cross-sectional units (N) and...

The Generalized Method of Moments (GMM) is discussed for handling the joint occurrence of fixed effects and random measurement errors in an autoregressive panel data model. Finite memory of disturbances, latent regressors and measurement errors is assumed. Two specializations of GMM are...

This paper, using the Bewley (1979) transformation of the autoregressive distributed lag model, proposes a novel pooled Bewley (PB) estimator of long-run coefficients for dynamic panels with heterogeneous short-run dynamics, in the same setting as the widely used Pooled Mean Group (PMG)...

In this paper we consider estimation and inference of common breaks in panel data models via adaptive group fused lasso. We consider two approaches -- penalized least squares (PLS) for first-differenced models without endogenous regressors, and penalized GMM (PGMM) for first-differenced models...

Using data for most of the year 2020, we analysed the impact of COVID-19 deaths on a given country regarding the financial market returns of neighbouring countries. Our empirical evidence show that in the first weeks of the COVID-19 outbreak, until mid-March 2020, the spatial effect of COVID-19...

In this paper, we perform an extensive Monte Carlo study of the finite sample properties of different estimators for panel data sample selection models. The estimators investigated are various two-step estimators and maximum likelihood estimators with simultaneous equations for the...