Gravity type models are widely used in international economics. In these models the inclusion of time-fi0xed regressors like geographical or cultural distance, language and institutional (dummy) variables is often of vital importance e.g. to analyse the impact of trade costs on internationalization activity. This paper assesses the problem of parameter inconsistency due to a correlation of the time-fixed regressors with the combined error term in panel data settings. A common solution is to use Instrumental-Variable (IV) estimation in the spirit of Hausman-Taylor (1981) since a standard Fixed Effect Model (FEM) estimation is not applicable. However, some potential shortcomings of the latter approach recently gave rise to the use of non-IV two-step estimators. Given their growing number of empirical applications, we aim to compare the performance of IV and non-IV approaches in the presence of time-fixed variables and right hand side endogeneity using Monte Carlo simulations, where we explicitly control for the problem of IV selection in the Hausman-Taylor case. The simulation results show that the Hausman-Taylor model with perfect-knowledge about the underlying data structure (instrument orthogonality) has on average the smallest bias. However, compared to the empirically relevant specification with imperfect-knowledge and instruments chosen by statistical criteria, simple non-IV rival estimators performs equally well or even better. We illustrate these findings by estimating gravity type models for German regional export activity within the EU. The results show that the HT specification is likely to overestimate the role of trade costs proxied by geographical distance.
Gravity model Exports Instrumental variables two-step estimators Monte Carlo