This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating DSGE model posterior distributions. We show how the tempering schedule can be chosen adaptively, document the accuracy and runtime benefits of generalized data tempering for “online” estimation...

This paper develops a forecasting procedure based on a Bayesian method for estimating vector autoregressions. The procedure is applied to ten macroeconomic variables and is shown to improve out-of-sample forecasts relative to univariate equations. Although cross-variables responses are damped by...

One of the perceived advantages of difference-in-differences (DiD) methods is that they do not explicitly restrict how units select into treatment. However, when justifying DiD, researchers often argue that the treatment is "quasi-randomly" assigned. We investigate what selection mechanisms are...

Linear panel models, and the "event-study plots" that often accompany them, are popular tools for learning about policy effects. We discuss the construction of event-study plots and suggest ways to make them more informative. We examine the economic content of different possible identifying...

There has been an extraordinary decrease in order execution time on stock exchanges in the past two decades. A related question is whether there has been a similar reduction in orders of magnitude for the lengths of the lead lag time between stocks. If the answer is affirmative, and the lengths...

Although typically overlooked, many purchase datasets exhibit a high incidence of products with zero sales. We propose a new estimator for the Random-Coefficients Logit demand system for purchase datasets with zero-valued market shares. The identification of the demand parameters is based on a...

This paper proposes an adjusted-range based self-normalization method to construct confidence intervals for censored dependent data, which helps to circumvent the long-run variance estimation and tuning parameter selection problems. Simulation studies confirm the validity of this new approach

This paper considers the model averaging prediction in a quasi-likelihood framework that allows for parameter uncertainty and model misspecification. We propose an averaging prediction that selects the data-driven weights by minimizing a K-fold cross-validation. We provide two theoretical...

We present a general framework for optimal nonparametric spot volatility estimation based on intraday range data, comprised of the first, highest, lowest, and last price over a given time interval. We rely on a decision-theoretic approach together with a coupling-type argument to directly tailor...