being examined. For instance, if exports are being examined, a relatively high price level on the part of the importer would be expected to increase trade with that country. A non-linear system of equations are used by Anderson and van Wincoop (2003) to account for the endogenous change in these price terms from trade liberalization. A more simple method is to use a first order log-linearization of this system of equations (Baier and Bergstrand (2009)), or exporter-country-year and importer-country-year dummy variables. For counterfactual analysis, however, one would still need to account for the change in world prices. Econometric estimation of gravity equations Since the gravity model for trade does not hold exactly, in econometric applications it is customary to specify where represents volume of trade from country to country , and typically represent the GDPs for countries and , denotes the distance between the two countries, and represents an error term with expectation equal to 1. The traditional approach to estimating this equation consists in taking logs of both sides, leading to a log-log model of the form (note: constant G becomes part of ): However, this approach has two major problems. First, it obviously cannot be used when there are observations for which is equal to zero. Second, Santos Silva and Tenreyro (2006) argued that estimating the log-linearized equation by least squares (OLS) can lead to significant biases if the researcher believes the true model to be nonlinear in its parameters. As an alternative, these authors have suggested that the model should be estimated in its multiplicative form, i.e., using a Poisson pseudo-maximum likelihood (PPML) estimator based on the Poisson model usually used for count data. As shown by Santos Silva and Tenreyro (2006), PPML estimates of common gravity variables can be different from their OLS counterparts. In particular, they found that the trade-reducing effects of distance were smaller and that the effects of colonial ties were statistically insignificant. Though PPML does allow the inclusion of observations where , it is not necessarily a perfect solution to the "zeroes" problem. Martin and Pham (2008) argued that using PPML on gravity severely biases estimates when zero trade flows are frequent and reflect non-random selection. However, their results were challenged by Santos Silva and Tenreyro (2011), who argued that the simulation results of Martin and Pham (2008) are based on misspecified models and showed that the PPML estimator performs well even