Bayesian Linear Regression Model
In this entry, we lay the foundations of Bayesian linear regression estimation. We start with a univariate model with Gaussian innovations and consider two cases for prior distributional assumptions—diffuse and informative. Then, we show how one could incorporate knowledge that the sample is not homogeneous with respect to the variance, for example, due to a structural break. Finally, multivariate regression estimation is discussed.
THE UNIVARIATE LINEAR REGRESSION MODEL
The univariate linear regression model attempts to explain the variability in one variable ...
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