Standard regression technique uses Ordinary Least Square estimator (OLS) for model fitting. In the presence of outliers OLS fits the model vary sharply with respect to actual regression curve. For model fitting, this paper applies robust estimation approach as a substitute for OLS. This...

This study focuses on the impact of model estimation methods on earnings forecast accuracy. Compared with an ordinary least squares (OLS) regression combined with winsorization, robust regression MM-estimation improves the earnings forecast accuracy of all the models examined, especially for...

This paper develops a novel wild bootstrap procedure to construct robust bias-corrected (RBC) valid confidence intervals (CIs) for fuzzy regression discontinuity designs, providing an intuitive complement to existing RBC methods. The CIs generated by this procedure are valid under conditions...

This paper addresses the issue of outliers in finance-growth literature and provides a robust sensitivity analysis of some past studies and an updated data set. We employ the robust regression methods of median quantile regression and least trimmed squares. It shows that the findings of past...

Sparse partial robust M regression is introduced as a new regression method. It is the first dimension reduction and regression algorithm that yields estimates with a partial least squares alike interpretability that are sparse and robust with respect to both vertical outliers and leverage...

This paper evaluates the robustness of UK bond term premia from affine term structure models. We show that this approach is able to match standard specification tests. In addition, term premia display countercyclical behaviour and are positively related to uncertainty about future inflation,...

In regression discontinuity design (RD), for a given bandwidth, researchers can estimate standard errors based on different variance formulas obtained under different asymptotic frameworks. In the traditional approach the bandwidth shrinks to zero as sample size increases; alternatively, the...

In this analysis of the risk and return of stocks in global markets, we build a reasonably large number of stock selection models and create optimized portfolios to outperform a global benchmark. We apply robust regression techniques include variable selection method like LASSO and LAR...