We extend range-based volatility estimation to the multivariate case. In particular, we propose a range-based covariance estimator motivated by a key financial economic consideration, the absence of arbitrage, in addition to statistical considerations. We show that this estimator is highly...

Following the work by White (1980ab; 1982) it is common in empirical work in economics to report standard errors that are robust against general misspecification. In a regression setting these standard errors are valid for the parameter that in the population minimizes the squared difference between...

In this paper we study identification and estimation of a correlated random coefficients (CRC) panel data model. The outcome of interest varies linearly with a vector of endogenous regressors. The coefficients on these regressors are heterogenous across units and may covary with them. We...

We develop estimation methods that use the amount of selection on the observables in a model as a guide to the amount of selection on the unobservables. We show that if the observed variables are a random subset of a large number of factors that influence the endogenous variable and the outcome...

We perform maximum likelihood estimation of a model of international asset pricing based on CAPM. We test the restrictions imposed by CAPM against a more general asset pricing model. The "betas" in our CAPM vary over time from two sources -- the supplies of the assets (government obligations of...

Data on human height can provide an index that may measure more accurately changes in the standard of living than the more conventional real wage index. Height data, like those on real wages, are relatively abundant and extend back to the seventeenth century. In a previous paper, we developed...

We propose a novel time-series econometric framework to forecast U.S. Presidential election outcomes in real time by combining polling data, economic fundamentals, and political prediction market prices. Our model estimates the joint dynamics of voter preferences across states. Applying our...

We investigate the economic consequences of statistical learning for arbitrage pricing in a high-dimensional setting. Arbitrageurs learn about alphas from historical data. When alphas are weak and rare, estimation errors hinder arbitrageurs--even those employing optimal machine learning...

Revealed preference arguments are commonly used when identifying models of both single-agent decisions and non-cooperative games. We develop general identification results for a large class of models that have a linearly separable payoff structure. Our model allows for both discrete and...