Economic theory often provides us with qualitative information on the properties of the functions in a model but rarely indicates their explicit functional form. Among these properties one can find monotonicity, concavity and supermodularity, which involve restricting the sign of the...

This paper evaluates the role of various volatility specifications, such as multiple stochastic volatility (SV) factors and jump components, in appropriate modeling of equity return distributions. We use estimation technology that facilitates non-nested model comparisons and use a long data set...

This paper explores the uniformity of inference for parameters of interest in nonlinear models with endogeneity. The notion of uniformity is fundamental in these models because due to potential endogeneity, the behavior of standard estimators of these parameters is shown to vary with where they...

We show that the compensation for rare events accounts for a large fraction of the average equity and variance risk premia. Exploiting the special structure of the jump tails and the pricing thereof we identify and estimate a new Investor Fears index. The index suggests both large and...

We propose a new and flexible non-parametric framework for estimating the jump tails of Itô semimartingale processes. The approach is based on a relatively simple-to-implement set of estimating equations associated with the compensator for the jump measure, or its "intensity", that only...

Discrete response models are of high interest in economics and econometrics as they encompass treatment effects, social interaction and peer effect models, and discrete games. We study the impact of the structure of information sets of economic agents on the Fisher information of (strategic)...

It is often believed that without instrument, endogenous sample selection models are identified only if a covariate with a large support is available (see, e.g., Chamberlain, 1986, and Lewbel, 2007). We propose a new identification strategy mainly based on the condition that the selection...

This paper provides a general framework that enables many existing inference methods for predictive accuracy to be used in applications that involve forecasts of latent target variables. Such applications include the forecasting of volatility, correlation, beta, quadratic variation, jump...

We evaluate various economic modelsí relative performance in forecasting future US output growth and inflation on a monthly basis. Our approach takes into account the possibility that the modelsí relative performance can be varying over time. We show that the modelsí relative performance has,...

The objective of this paper is to identify which parameters of a model are stable over time. Existing procedures can only be used to test whether a given subset of parameters is stable, and cannot be used to find which subset of parameters is stable. We propose a new procedure that is...