Maximum likelihood estimation has been the workhorse of statistics for decades, but alternative methods, going under the name “regularization,” are proving to have lower predictive variance. Regularization shrinks fitted values towards the overall mean, much like credibility does. There is...

Despite their effectiveness, linear models for realized variance neglect measurement errors on integrated variance and exhibit several forms of misspecification due to the inherent nonlinear dynamics of volatility. We propose new extensions of the popular approximate long-memory HAR model apt to...

A new method for computing the standard errors of returns-based risk and performance estimators for serially correlated returns is developed. The method uses the fact that any such estimator can be represented as the sum of returns that are transformed using the estimator's influence function,...

While classical measurement error in the dependent variable in a linear regression framework results only in a loss of precision, non-classical measurement error can lead to estimates which are biased and inference which lacks power. Here, we consider a particular type of non-classical...

We investigate the effect of estimation error on backtests of (multi-period) expected shortfall (ES) forecasts. These backtests are based on first order conditions of a recently introduced family of jointly consistent loss functions for Value-at-Risk (VaR) and ES. We provide explicit expressions...

This research brief is a cautionary note on the dangers of using p-values as empirical evidence for crossover effects in consumer research. If you rely on experimental manipulation to produce between-subjects variation on a moderator construct, and then test for interaction effects on the...

Since dividend imputation was introduced to Australia 32 years ago, researchers and corporate finance practitioners have debated the extent to which imputation credits are incorporated into share prices. One reason for divergence of opinions is the selective interpretation of coefficient...

I backcast expectation errors of credit spreads via machine learning. I use newspapers over the past century to construct text-based expectations of credit spreads and study the relationship between expectation errors and business cycles. The main result is that overoptimism about future credit...

When the running variable in a regression discontinuity (RD) design is measured with error, identification of the local average treatment effect of interest will typically fail. While the form of this measurement error varies across applications, in many cases the measurement error structure is...

A large literature on persistence finds that many modern outcomes strongly reflect characteristics of the same places in the distant past. However, alongside unusually high t statistics, these regressions display severe spatial autocorrelation in residuals, and the purpose of this paper is to...