We present a fundamentally unique method of nonparametric regression using clusters and test it against classically established methods. We compare two nonlinear regression estimation packages called ‘NNS', Viole (NNS: nonlinear nonparametric statistics, 2016), and ‘np', Hayfield and Racine...

Advanced tree-based estimation methods, such as random forest, are ensembles of regression trees that are built using random subsets of explanatory variables. However, because of the random selection process, relevant variables may not be considered in some regression trees, thereby reducing...

It is well known that volatility is time-varying and clustered. However, few studies have explored the information content of volatility clustering and its implications for investors’ risk aversion. This information is particularly important in turbulent periods, such as financial crisis. We...

In many sampling surveys, the use of auxiliary information at either the design or estimation stage, or at both these stages is usual practice. Auxiliary information is commonly used to obtain improved designs and to achieve a high level of precision in the estimation of population density....

We study cluster-robust inference for binary response models. Inference based on the most commonly-used cluster-robust variance matrix estimator (CRVE) can be very unreliable. We study several alternatives. Conceptually the simplest of these, but also the most computationally demanding, involves...