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....

Least squares regression with heteroskedasticity consistent standard errors ("OLS-HC regression") has proved very useful in cross section environments. However, several major difficulties, which are generally overlooked, must be confronted when transferring the HC technology to time series...

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...

Econometric software packages typically report a fixed number of decimal digits for coefficient estimates and their associated standard errors. This practice misses the opportunity to use rounding rules that convey statistical precision. Using insights from the testing statistical hypotheses of...

This paper examines the econometric causal model and the interpretation of empirical evidence based on thought experiments that was developed by Ragnar Frisch and Trygve Haavelmo. We compare the econometric causal model with two currently popular causal frameworks: the Neyman-Rubin causal model...