Several frequentist and Bayesian model averaging schemes, including a new one that simultaneously allows for parameter uncertainty, model uncertainty and time varying model weights, are compared in terms of forecast accuracy over a set of simulation experiments. Artificial data are generated,...

Poor performing mutual funds are less likely to be observed in the data sets that are typically available. This so-called survivor problem can induce a substantial bias in measures of the performance of the funds and the persistence of this performance. Many studies have recently argued that...

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...

We analyze the performance persistence in hedge funds taking into account look-ahead bias (multi-period sampling bias). We model liquidation of hedge funds by analyzing how it depends upon historical performance. Next, we use a weighting procedure that eliminates look-ahead bias in measures for...

In this paper, we analyze the economic value of predicting stock index returns as well as volatility. On the basis of simple linear models, estimated recursively, we produce out-of-sample forecasts for the return on the S&P 500 index and its volatility. Using monthly data, we examine the...

This article examines the relationship between two alternative approaches, instrumental variables and control function procedures, for estimating the impact of endogenous treatment effects. Although it is well known that the two approaches generate comparable estimates, the relationship between...