There are several approaches to dealing with heteroscedasticity. If the error variance at different times is known, weighted regression is a good method. If, as is ...

This repository is maintained by the KNIME Team Rakete. It provides a template for creating KNIME Python extensions. This repository contains a template KNIME Python Extensions. The code is organized ...

Previous time series applications of qualitative response models have ignored features of the data, such as conditional heteroscedasticity, that are routinely addressed in time series econometrics of ...

In this paper, we introduce a new identification and estimation strategy for partially linear regression models with a general form of unknown heteroscedasticity, that is, Y = X'β₀ + m(Z) + U and U = ...

One of the key assumptions of the ordinary regression model is that the errors have the same variance throughout the sample. This is also called the homoscedasticity ...