On the estimation of a fixed effects model with selective non-response

Economics;Statistical Methods;econometrics

Estimating and interpreting models with endogenous treatment effects: The relationship between competing estimators of the union impact on wages

Wages;Trade Unions;Estimation;Models

Estimation of time dependent parameters in linear models using cross sections, panels or both

Estimation;mathematische statistiek

Do Sophisticated Investors Believe in the Law of Small Numbers?

Believers in the law of small numbers tend to overinfer the outcome of a random process after a small series of observations. They believe that small samples replicate the probability distribution properties of the population. We provide empirical evidence indicating that investors are mistakenly driven by this psychological bias when hiring or firing a fund manager after a successful (or losing) performance streak. Using quarterly data between 1994 and 2000 of 752 hedge funds, we analyze actual money flows into and out of hedge funds and their relationship with the length of the streak. We first show that persistence patterns have a predictive ability of future relative performance of a manager: the longer the winner streak, the larger the probability for a fund to remain a winner. Investors, in turn, appear to be aware of quality dispersion across managers and respond by following a momentum strategy: the longer the winning (losing) streak, the more likely they will invest in (divest from) that fund. Yet, we find that investors place excessive weight in the managers’ track record as a criterion for decision. Our model shows that the length of the streak has an economically and statistically significant impact on money flows beyond rationally expected performance, which confirms a “hot-hand†bias driving to a large extent momentum investing. Apparently, even sophisticated investors exhibit psychological biases that may have adverse effects on their wealth.Performance Persistence;Overreaction;Hedge Fund Investors;Hot-Hand Bias;Law of Small Numbers

The Economic Value of Predicting Stock Index Returns and Volatility

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 genuine out-of-sample forecasts for the return on the S&P 500 index and its volatility. Using monthly data from 1954 to 2001, we test the statistical significance of return and volatility predictability and examine the economic value of a number of alternative trading strategies. While we find strong evidence for market timing in both returns and volatility, the success of market timing and volatility timing varies considerably over the sample period. Further, it appears easier to forecast returns at times when volatility is high. For a mean-variance investor, this predictability is economically profitable, even if short sales are not allowed and transaction costs are quite large. The economic value of trading strategies that employ market timing in returns and volatility exceeds that of strategies that only employ timing in returns.performance evaluation;market timing;investments;predictability of stock returns and volatility

Can cohort data be treated as genuine panel data?

Statistical Methods;Panel Data

Estimating and testing simultaneous equation panel data models with censored endogenous variables

Wages;Estimation;Testing;Models

The Economic Value of Predicting Stock Index Returns and Volatility

In this paper, we analyze the economic value of predicting index returns as well as volatility. On the basis of fairly simple linear models, estimated recursively, we produce genuine out-of-sample forecasts for the return on the S&P 500 index and its volatility. Using monthly data from 1954-1998, we test the statistical significance of return and volatility predictably and examine the economic value of a number of alternative trading strategies. We find strong evidence for market timing in both returns and volatility. Joint tests indicate no dependence between return and volatility timing, while it appears easier to forecast returns when volatility is high. For a mean-variance investor, this predictably is economically profitable, even if short sales are not allowed and transaction costs are quite large.Predicability of stock returns and volatility

Incomplete panels and selection bias: A survey

Sampling;Panel Data