Daily Gravity

We estimate trade costs under large zero trade by using daily data on agricultural goods trade within a country. Because of the nature of daily data, there is prominent zero daily trade between regions, and daily delivery is subject to noisy demand and supply shocks, which tend to create heteroskedasticity in the data. Hence, we use Poisson Pseudo Maximum Likelihood (PPML) to estimate the gravity model and investigate the nonlinear nature of trade costs. Empirical analysis shows a statistically significant and economically large nonlinearity in trade costs. We also aggregate daily data to the monthly level to examine whether shocks are smoothed which result in dampened impacts. Our estimation shows that the difference is minor. Comparisons of the results with other estimation methods such as the least squares of linear-in-log model and various Tobit procedures are also conducted. There is a large difference in the results between simple least squares and PPML, suggesting significant heteroskedasticity. We also calculate outward and inward multilateral resistance terms to derive the incidence of trade costs and find that a large portion is the buyer's burden.