Revisiting the trade effects of the euro: data sources and various samples

New evidence in the literature on trade effects of the euro often reports different estimates. In this paper, I investigate the impact of trade data, instead of methodology, on the estimation of the key coefficient. In particular, I apply both the log-linearized least squares (OLS) estimator and the Poisson pseudo-maximum likelihood (PPML) estimator to the structural gravity model and compare these estimates by using trade data from two of the most widely used sources (IMF DOTS and UN Comtrade) and by varying samples. One surprising result is that the OLS estimator yields coefficients of the euro with opposite signs for the two data sources, when a sample covering all countries is applied. It is as expected that the PPML estimator is much less sensitive to sample size than the OLS estimator, taking a data source as given. However, the variation in estimates caused by data sources and sampling is consistent for both estimators. It indicates that both estimators are not free from the measurement error issue. More findings include: (1) the discrepancy in OLS estimates derived for the two datasets persists across samples, but the magnitude varies; (2) the magnitude of the discrepancy in PPML estimates from the two datasets is more stable to sampling; (3) both OLS and PPML estimators are sensitive to sample compositions for a given sample size.