We propose a new empirical framework to estimate sectoral uncertainty from data-rich environments. We jointly decompose the conditional variance of economic time series i[...]

Forecast combinations, also known as ensemble models, routinely require practitioners to select a model from a massive number of potential candidates. Ten explanatory var[...]

In this paper, we study how potential characteristics of the new payment instrument may affect consumer adoption and usage decisions at the point of sale. We do this by d[...]

This paper studies identification, estimation, and inference of a weighted average treatment effect (W-ATE) parameter in a class of switching regime models, where the age[...]

This paper addresses an existing gap in the developing literature on conditional skewness. We develop a simple procedure to evaluate parametric conditional skewness model[...]

In this paper, we propose a flexible, parametric class of switching regime models allowing for both skewed and fat-tailed outcome and selection errors. Specifically, we m[...]

Observed high-frequency prices are contaminated with liquidity costs or market microstructure noise. Using such data, we derive a new asset return variance estimator insp[...]

This paper addresses an existing gap in the developing literature on conditional skewness. We develop a simple procedure to evaluate parametric conditional skewness model[...]

This paper explores the volatility forecasting implications of a model in which the friction in high-frequency prices is related to the true underlying volatility. The co[...]

This paper proposes new measures of the integrated variance, measures which use high-frequency bid-ask spreads and quoted depths. The traditional approach assumes that th[...]