Financial analysts assume that the reliability of predictions derived from regression analysis improves with sample size. This is generally true because larger samples tend to produce less noisy results than smaller samples. But this is not always the case. Some observations are more relevant...

It is common practice to forecast social, political, and economic outcomes by polling people about their intentions. This approach is direct, but it can be unreliable in settings where it is hard to identify a representative sample, or where subjects have an incentive to conceal their true...

Investors sometimes have strong convictions that a distinctive economic regime will prevail in the period ahead and therefore would like to form a portfolio that reflects the expected returns, standard deviations, and correlations of assets during such a regime. To do so, they typically isolate...

In this paper, we run regression analyses to explain voluntary turnover intentions with data from more than 5,000 employees and with about 250 explanatory variables. The findings of our multi-factor approach highlight the fact that previous empirical research might have over-estimated the impact...

Experimental researchers routinely use regression in order to control for pre-treatment covariates. This practice has become controversial in the wake of recent demonstrations showing that this type of regression is prone to bias in small samples. Bias may even remain when units are sampled from...

In this paper, we introduce a methodology based on zero-inflated long-term survival data in order to deal with fraud rate estimation in bank loan portfolios. Our approach enables us to accommodate three different types of loan borrowers, i.e., fraudsters, those who are susceptible to default...

Motivated by increment process modeling for two correlated random and non-random systems from a discrete-time asset pricing with both risk free asset and risky security, we propose a class of semi-parametric regressions for a combination of a non-random and a random system. Unlike classical...

Quantile regression is in the focus of many estimation techniques and is an important tool in data analysis. When it comes to nonparametric specifications of the conditional quantile (or more generally tail) curve one faces, as in mean regression, a dimensionality problem. We propose a...