This paper shows that a multiple regression with two highly correlated explanatory variables, both of them with a near zero correlation with the dependent variable may correspond to a spurious regression or to a homeostatic model, with estimates highly sensible to outliers. The regression method...

This companion paper to Chatelain and Ralf (2012), “Spurious regressions with near-multicollinearity” put their results into the contexts of the history of statistics, of the current publication bias in applied sciences and of the substantive versus statistical significance debate. This...

In multiple regressions, explanatory variables with simple correlation coefficients with the dependent variable below 0.1 in absolute value (such as aid with economic growth) may have very large and statistically significant estimated parameters which are unfortunately "outliers driven" and...

This companion paper to Chatelain and Ralf (2012), 'Spurious regressions with near-multicollinearity' put their results into the contexts of the history of statistics, of the current publication bias in applied sciences and of the substantive versus statistical significance debate. This article...

The paper proposes a new algorithm for finding the confidence set of a collection of forecasts or prediction models. Existing numerical implementations for finding the confidence set use an elimination approach where one starts with the full collection of models and successively eliminates the...

We extend the results of De Luca et al. (2021) to inference for linear regression models based on weighted-average least squares (WALS), a frequentist model averaging approach with a Bayesian flavor. We concentrate on inference about a single focus parameter, interpreted as the causal effect of...

Generalized Information Matrix Tests (GIMTs) have recently been used for detecting the presence of misspecification in regression models in both randomized controlled trials and observational studies. In this paper, a unified GIMT framework is developed for the purpose of identifying,...

The paper proposes a new algorithm for finding the confidence set of a collection of forecasts or prediction models. Existing numerical implementations for finding the confidence set use an elimination approach where one starts with the full collection of models and successively eliminates the...

We develop a method of testing linearity using power transforms of regressors, allowing for stationary processes and time trends. The linear model is a simplifying hypothesis that derives from the power transform model in three different ways, each producing its own identification problem. We...

This paper discusses how real-life statistical analysis/inference deviates from ideal environments. More specifically, there often exist models that have equal statistical power as the actual data-generating model, given only limited information and information processing/computation capacity....