Consider a k-variables linear regression model, i.e.,
Y = X 1β1 + X 2 β2 + ε,
Where, X1 is (N  k1 ) , X 2 is (N  k2 ) and k = k1 + k2 . As you may recall, adding columns to the X matrix (including additional regressors in the model) gives positive definite increase in R2. The adjusted R2 ( R 2 ) attempts to avoid this phenomenon of ever increase in R2. Show that the additional k2 number of variables (regressors) in this model increases R 2 if the calculated F-statistic in testing the joint statistical significance of coefficients of these additional
regressors (β2 ) is larger than one.
"\\begin{bmatrix}\n y_1 \\\\\n y_2\\\\.\\\\.\\\\. \\\\y_T\n\\end{bmatrix}=\\begin{bmatrix}\n 1 & x_{11}&...&Ax_{1k}\\\\\n 1 & x_{21}&...&x_{2k}\\\\.&.&&.\\\\.&.&&.\\\\.&.&&.\\\\1&x_{T1}&...&x_{Tk}\n\\end{bmatrix}\\begin{bmatrix}\n \\beta _0 \\\\\n \\beta _1 \\\\.\\\\.\\\\.\\\\\\beta _k\n\\end{bmatrix}+\\begin{bmatrix}\n \\in _1 \\\\\n \\in _2\\\\.\\\\.\\\\.\\\\\\in_T,\n\\end{bmatrix}."
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