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Non-Spherical Disturbances in Regression by Prateek Naik is a document available to read on EtoBox.

1. The document introduces the concept of non-spherical disturbances in linear models, where the variance matrix of the disturbances is not simply a scaled identity matrix. 2. It shows that the ordinary least squares (OLS) estimator remains unbiased even with non-spherical disturbances, but its variance depends on the unknown variance matrix of the disturbances. 3. The document proposes using a generalized least squares (GLS) estimator, which transforms the model variables using the square root of the di

Author

Prateek Naik

Language

EN