Frontmatter -- Preface -- Acknowledgments -- Contents -- Acronyms -- 1 Introduction -- 2 Linear static models -- 3 Dealing with heterogeneity and endogeneity: fixed effects, IV and GMM -- 4 Outliers, missing values and other data issues -- 5 Linear dynamic models -- 6 Models with limited...

A wide range of empirical biases hampers hedge fund databases. In this paper we focus upon survival-related biases and disentangle look-ahead biases due to self-selection of funds and due to fund termination. Self-selection arises because funds voluntarily report their information to data...

Conventional short-term reversal strategies exhibit dynamic exposures to the Fama and French (1993) factors. We develop a novel reversal strategy based on residual stock returns that does not exhibit these exposures and consequently earns risk-adjusted returns that are twice as large as those of...

If missing observations in a panel data set are not missing at random, many widely applied estimators may be inconsistent. In this paper the authors examine empirically several ways to reveal the nature and severity of the selectivity problem due to nonresponse, as well as a number of methods to...

The authors discuss several tests to check for the presence of selectivity bias in estimators based on panel data. One approach to test for selectivity bias is to specify the selection mechanism explicitly and estimate it jointly with the model of interest. Alternatively, one can derive the...

This paper analyzes the properties of a number of estimators that can be used to estimate short-run persistence in mutual fund returns. When data for different funds are pooled, it is advisable to correct for cross-sectional differences in expected returns. However, these adjustments may induce...