Saturday, May 4, 2013

All About Spherically Distributed Regression Errors

In our list of standard assumptions about the error term in linear multiple regression model, we include one that incorporates both homoskedasticity and the absence of autocorrelation. That is, the individual values of the errors are assumed to be generated by a random process whose variance is constant, and all possible distinct pairs of these values are uncorrelated. This implies that the full error vector has a scalar covariance matrix.
This overall situation is referred to as one in which the values of the error term follow a “Spherical Distribution”. Let's take a look at the origin of this terminology.

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