Thesis “Influential Observations in Cross-Sectional Tests of Linear Factor Models” I investigate the link between the variations in the cross-section of average returns and macroeconomic conditions. Instead of examining a specific factor, I ask which properties are needed for a factors to capture a large part of the cross-sectional variation of average return. I show that the return data of com...

The local influence method has proven to be a useful and powerful tool for detecting influential observations on the estimation of model parameters. This method has been widely applied in different studies related to econometric and statistical modelling. We propose a methodology based on the Lagrange multiplier method with a linear penalty function to assess local influence in the possibly het...

A statistical methodology for detecting influential observations in long-memory models is proposed. The identification of these influential points is carried out by case-deletion techniques. In particular, a Kullback–Leibler divergence is considered to measure the effect of a subset of observations on predictors and smoothers. These techniques are illustrated with an analysis of the River Nile ...

Jackknife-after-Bootstrap (JaB) has first been proposed by [1] then used by [2] and [3] to detect influential observations in linear regression models. In this study, we propose using JaB to detect influential observations in logistic regression model. Performance of the proposed method will be compared with the traditional method for standardized Pearson residuals, Cook’s distance, change in t...

Abstract: Identifying influential observations is an important part of the model building process in linear regression. There are numerous diagnostic measures based on different approaches in linear regression analysis. However, the problem of multicollinearity and influential observations may occur simultaneously. Therefore, we propose new diagnostic measures based on the two parameter ridge e...