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...

In this paper we estimate the skewness of the unconditional distribution of energy returns and test its statistical significance. We compare the performance of traditional and robust tests for symmetry with those based on the implied unconditional skewness in a TGARCH model with Gram-Charlier...

This paper considers the interpretation of "Mincer rates of return." We test and reject the Mincer model. It fails to track the time series of true returns. We show how repeated cross section and panel data improves the ability of analysts to estimate the ex ante and ex post marginal rate of...

We examine the efficacy with which the CBOE Volatility Index (VIX) predicts future (30 day forward) S&P realized volatility. We find that the VIX’s accuracy is about 63%. An alternative framework that we present, built on asymptotic distribution theory, predicts this volatility with an...

We extend LeSage and Pace's (2008) spatial autoregressive model for origin-destination flows by accommodating two-way fixed effects. Those fixed effects represent unobserved characteristics of origin and destination units. A partial likelihood approach is used to remove fixed effects in the...

For linear panel data models with fixed effects and sample selection, we correct for selectivity bias in the within-group estimator. The proposed procedure is equivalent to a pooled weighted least squares based on pairwise differences augmented with correction terms. A computationally affordable...