Dynamic Simultaneous Equations with Panel Data: Small Sample Properties and Application to Regional Econometric Modelling

Efficiency of full information approaches for the estimation of a system of equations is well known for large samples. However, less is known about the estimators’ small sample properties. This is especially true for panel data settings, although the latter are by now a standard approach to model static and dynamic economic processes. Here extensive finite sample evidence based on Monte Carlo (MC) simulations is solely given for the single equation case. In this paper, we provide new results for the multiple equation case by testing multiple equations extensions for the standard fixed effects type models (FEM) as well as familiar IV/GMM-style estimators, which have recently been proposed in the literature. Building upon the MC simulation evidence, the paper then applies dynamic simultaneous equation estimation to a small-scale economic model for regional output growth, private and public capital investment among German states. The model is used to identify the likely two-way effects among the variables and to perform policy analysis. The latter is done by augmenting the baseline model by variables measuring interregional spillover effects from public capital as well as transfer payments from regional equalization schemes. We find positive but insignificant effects from interregional spillovers in transport infrastructure, while spillovers from science infrastructure even tend to be negative. The latter result is likely to originate from specific locational advantages of science infrastructure, which allows regions to poach production factors from their neighborhood. For regional equalization transfers we find mixed results, crucially depending on the specific policy programme.