Estimating panel VARs from macroeconomic data: Some Monte Carlo evidence and an application to OECD public spending shocks

Abstract

This paper compares the performance of six widely applied techniques to estimate panel VARs from macroeconomic (large T) data. We show that the bias of the popular least squares dummy variable estimator remains substantial even when the time dimension of the dataset is relatively large. Adopting a bias correction to the simple fixed-effects estimator is strongly recommended to obtain consistent estimates of the implied impulse response functions. Multivariate extensions of the GMM-type estimators usually applied for estimating single-equation dynamic panel data models perform reasonably well in terms of bias, but poorly in terms of root mean square error, in particular if the variance of the fixed effects is large relative to the variance of the innovations. To illustrate the methodological arguments we present an application in which we use annual OECD country data to estimate the effects of changes in government consumption on aggregate output, private consumption, investment, and real wages. JEL classification: C13, C33, E62, E00