While partial moments like semivariance, lower and upper partial moments have seen acceptance by both academics and investment professionals, there are some who consider these measures to be ad hoc measures of investment performance. This paper seeks to provide the academic foundation for the...

Using component series from a given time series, we are able to demonstrate forecasting ability with none of the requirements of the traditional ARMA method, while strictly adhering to the definition of an autoregressive model. We also propose a new test for seasonality using coefficient of...

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...

Function approximation is at the heart of machine learning. Given a dataset comprised of inputs and outputs, we assume that there is an unknown underlying function that is consistent in mapping inputs to outputs in the target domain and resulted in the dataset. We then use supervised learning...

Demonstration of nonlinear nonparametric regression technique using R-package "NNS" and comparison to kernel based regression methods in goodness of fit, partial derivative estimation, and out-of-sample extrapolation

Partial derivatives have a special place in economics since the marginal revolution of the 1850s. We present results from multivariate partial derivative estimates using nonlinear non-parametric regressions in a finite difference method, accessible via the R-package NNS. Numerical partial...