Trading Partners and Trading Volumes: Implementing the Helpman–Melitz–Rubinstein Model Empirically

Helpman, Melitz and Rubinstein [Quarterly Journal of Economics (2008) Vol. 123, pp. 441–487] (HMR) present a rich theoretical model to study the determinants of bilateral trade flows across countries. The model is then empirically implemented through a two‐stage estimation procedure. We argue that this estimation procedure is only valid under the strong distributional assumptions maintained in the article. Statistical tests using the HMR sample, however, clearly reject such assumptions. Moreover, we perform numerical experiments which show that the HMR two‐stage estimator is very sensitive to departures from the assumption of homoskedasticity. These findings cast doubts on any inference drawn from the empirical implementation of the HMR model.     

The Log of Gravity at 15

Portuguese Economic Journal - We review the contribution of “The Log of Gravity” (Santos Silva and Tenreyro, Rev Econ Stat 88:641–658, 2006), summarize the main results in the...     

Estimating the extensive margin of trade

Understanding and quantifying the determinants of the number of sectors or firms exporting in a given country is of relevance for the assessment of trade policies. Estimation of models for the number of exporting sectors, however, poses a challenge because the dependent variable has both a lower and an upper bound, implying that the partial effects of the explanatory variables on the conditional mean of the dependent variable cannot be constant. We argue that ignoring these bounds can lead to erroneous conclusions and propose a flexible specification that accounts for the doubly-bounded nature of the dependent variable. We empirically investigate the problem and the proposed solution, finding significant differences between estimates obtained with the proposed estimator and those obtained with standard approaches.     

Further simulation evidence on the performance of the Poisson pseudo-maximum likelihood estimator

We extend the simulation results in Santos Silva and Tenreyro (2006, The log of gravity, The Review of Economics and Statistics, 88, 641-658) by considering a novel data-generating process. Our results confirm that the Poisson pseudo-maximum likelihood estimator is generally well behaved, even when the proportion of zeros in the sample is very large.