To model international trade of forest products we use a gravity model of trade. In modeling trade, we estimate the impact of importer gross domestic product (GDP), exporter GDP, and distance between trading partners using Poisson pseudo-maximum likelihood (PPML). When estimating the log-linearized gravity model (ordinary least squares [OLS]), two issues arise. First, potential bias associated with truncation of all zero-trade observations due to the nonexistence of the natural log of zero. Second, heteroscedasticity can bias results from the log-linearized gravity model because of the multiplicative error term of the stochastic gravity model. To address these two issues, we propose avoiding the log-linearized gravity model and instead estimate the nonlinear gravity model via PPML. To estimate the model, trade data are compiled from the Food and Agriculture Organization of the United Nations. The observation window is from 1997 to 2014 and covers 13 product categories at a country-pair level. In our estimation, we find systematic differences in estimates from OLS in comparison with estimates from PPML. Using the estimated elasticities, in combination with estimates of future GDP from shared socioeconomic pathways, we project future US exports to the year 2030 for each item category in addition to total exports for Brazilian wood pulp, New Zealand industrial roundwood, and Canadian coniferous sawnwood. Using our approach, we provide a tool for policy makers and industry leaders alike to make informed decisions over prior estimates of forest product trade.