Anton Yang Publications

The enormous gravity-model-of-trade literature has illustrated important advantages of estimation via Poisson Pseudo Maximum Likelihood (PPML). That literature’s prioritization of parameter estimation over hypothesis testing has left questions of testing in the PPML framework underexplored. In this paper we show analytically that scaling the dependent variable can affect the outcome of some joint hypothesis tests, but not others. Likelihood-ratio, model-based Wald, and model-based Lagrange Multiplier test statistics depend on scale, and therefore do not support scale-invariant inference. Wald and Lagrange Multiplier tests constructed with heteroskedasticity-robust sandwich adjustments are invariant to scale. We illustrate these points empirically with an application from the literature on Revealed Comparative Advantage.

We introduce a new methodology to detect and measure economic activity using geospatial data and apply it to steel production, a major industrial pollution source worldwide. Combining plant output data with geospatial data, such as ambient air pollutants, nighttime lights, and temperature, we train machine learning models to predict plant locations and output. We identify about 40% (70%) of plants missing from the training sample within a 1 km (5 km) radius and achieve R² above 0.8 for output prediction at a 1 km grid and at the plant level, as well as for both regional and time series validations. Our approach can be adapted to other industries and regions, and used by policymakers and researchers to track and measure industrial activity in near real time.