Non-parametric gravity as defined in this paper encompasses previous parametric forms. The model generates non-parametric sufficient statistics for arbitrage gains from trade and terms of trade, and a non-parametric elasticity of terms of trade with respect to supply. For world manufacturing...

We propose a simple and flexible reduced-form econometric approach to estimate gravity models in the short and the long run. The theoretical lens for interpreting our methods amends the canonical Lucas-Prescott adjustment formulation to allow for time-interval-varying...

To provide sharp answers to basic questions in international trade, a standard approach is to focus on a small open economy (SOE). Whereas the classic tradition is to define a SOE as an economy that takes world prices as given, in the modern trade literature a SOE is defined instead as one that...

Causal inference is of central interests in many empirical applications yet often challenging because of the presence of endogenous regressors. The classical approach to the problem requires using instrumental variables that must satisfy the stringent condition of exclusion restriction. At the...

We develop a novel method for the identification of monetary policy shocks. By applying natural language processing techniques to documents that Federal Reserve staff prepare in advance of policy decisions, we capture the Fed's information set. Using machine learning techniques, we then predict...

We study how monetary policy affects subcomponents of the Personal Consumption Expenditures Price Index (PCEPI) using local projections. Following a monetary policy contraction, the response of aggregate PCEPI turns significantly negative after over three years. There are stark differences in...

With count-valued outcomes y in {0,1,...,M} identification and estimation of average treatment effects raise no special considerations beyond those involved in the continuous-outcome case. If partial identification of the distribution of treatment effects is of interest, however, count-valued...

We propose a methodology to take dynamic stochastic general equilibrium (DSGE) models to the data based on the combination of differentiable state-space models and the Hamiltonian Monte Carlo (HMC) sampler. First, we introduce a method for implicit automatic differentiation of perturbation...

Health expenditure data almost always include extreme values. Such heavy tails can be a threat to the commonly adopted least squares methods. To accommodate extreme values, we propose the use of an estimation method that recovers the often ignored right tail of health expenditure distributions....