Li and You (this volume) study public firms’ common stock return reactions to two events: when analysts’ initiate coverage of the firm and when they terminate coverage. They test the returns for evidence of three sources of value added by analysts: (1) more monitoring of the firm, (2) reduced information asymmetry about the firm, and (3) greater demand for the firm’s common stock. They find consistent support for analysts adding value by increasing demand, but not monitoring or by reducing information asymmetry. Their findings also indicate that analysts’ initiations supply little new information. I review these findings, put them in perspective with related research, and note research directions.

I test the effect of harsher punishments and sanctions on driving under the influence (DUI). In this setting, punishments are determined by strict rules on blood alcohol content (BAC) and previous offenses. Regression discontinuity derived estimates suggest that having a BAC above the DUI threshold reduces recidivism by up to 2 percentage points (17 percent). Likewise having a BAC over the aggravated DUI threshold reduces recidivism by an additional percentage point (9 percent). The results suggest that the additional sanctions experienced by drunk drivers at BAC thresholds are effective in reducing repeat drunk driving. (JEL I12, K42, R41)

We demonstrate the asymptotic equivalence between commonly used test statistics for out-of-sample forecasting performance and conventional Wald statistics. This equivalence greatly simplifies the computational burden of calculating recursive out-of-sample test statistics and their critical values. For the case with nested models, we show that the limit distribution, which has previously been expressed through stochastic integrals, has a simple representation in terms of -distributed random variables and we derive its density. We also generalize the limit theory to cover local alternatives and characterize the power properties of the test.

This paper studies the performance and persistence of Norwegian mutual funds utilizing a new data set of daily returns. Daily data allow us to evaluate the performance over short time horizons in a reliable manner, which is important because the risk exposure of funds can change over time. We complement the existing literature by providing the first study based on daily data outside of the US. Our results show that the performance of top and bottom funds cannot be explained by luck. The performance of these top and bottom funds persists for short horizons, of only up to one year. The mutual fund industry as a whole underperforms the benchmark by approximately the fund fees.

We consider estimation of and inference about coefficients on endogenous variables in a linear instrumental variables model where the number of instruments and exogenous control variables are each allowed to be larger than the sample size. We work within an approximately sparse framework that maintains that the signal available in the instruments and control variables may be effectively captured by a small number of the available variables. We provide a LASSO-based method for this setting which provides uniformly valid inference about the coefficients on endogenous variables. We illustrate the method through an application to demand estimation.