We develop new semiparametric methods for estimating treatment effects. We focus on a setting where the outcome distributions may be thick tailed, where treatment effects are small, where sample sizes are large and where assignment is completely random. This setting is of particular interest in...

Time series data are widely used to explore causal relationships, typically in a regression framework with lagged dependent variables. Regression-based causality tests rely on an array of functional form and distributional assumptions for valid causal inference. This paper develops a...

This paper studies asymmetry in economic activity over the business cycle. It develops a tractable multisector model of the economy in which complementarity across inputs causes aggregate activity to be left skewed with countercyclical volatility. We then examine implications of the model...

We propose methods for constructing regularized mixtures of density forecasts. We explore a variety of objectives and regularization penalties, and we use them in a substantive exploration of Eurozone inflation and real interest rate density forecasts. All individual inflation forecasters (even...

We find that among stocks dominated by retail investors, the lottery anomaly is amplified by high investor attention (proxied by high analyst coverage, salient earnings surprises, or recency of extreme positive returns) and intense social interactions (proxied by Facebook social connectedness or...

This paper suggests analytical strategies for obtaining informative parameter bounds when multivariate health-outcome data are partially observed in a particular yet common manner. One familiar context is where M1 health outcomes' respective totals across N1 time periods are observed but where...

Sea-level rise and ensuing permanent coastal inundation will cause spatial shifts in population and economic activity over the next 200 years. Using a highly spatially disaggregated, dynamic model of the world economy that accounts for the dynamics of migration, trade, and innovation, this paper...

Errors in probabilistic reasoning have been the focus of much psychology research and are among the original topics of modern behavioral economics. This chapter reviews theory and evidence on this topic, with the goal of facilitating more systematic study of belief biases and their integration...

We propose a Bayesian procedure for exploiting small, possibly long-lag linear predictability in the innovations of a finite order autoregression. We model the innovations as having a log-spectral density that is a continuous mean-zero Gaussian process of order 1/√T. This local embedding makes...

Climate policy is complicated by the considerable compounded uncertainties over the costs and benefits of abatement. We don't even know the probability distributions for future temperatures and impacts, making cost-benefit analysis based on expected values challenging to say the least. There are...