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Unformatted text preview: Applied Econometrics William Greene Department of Economics Stern School of Business Applied Econometrics 5. Regression Algebra and a Fit Measure The Sum of Squared Residuals b minimizes e e = ( y  Xb ) ( y  Xb ). Algebraic equivalences, at the solution b = ( X X )1 X y ee = y e (why? e = y bX ) e e = y y  yXb = y y  b X y = e y as e X = (This is the F.O.C. for least squares.) Minimizing e e Any other coefficient vector has a larger sum of squares. A quick proof: d = the vector, not b u = y  Xd . Then, u u = ( y  Xd ) ( y Xd ) = [ y  Xb  X ( d  b )] [ y  Xb  X ( d  b )] = [ e  X ( d  b )] [ e  X ( d  b )] Expand to find u u = e e + ( d b ) X X ( d b ) > e e Dropping a Variable An important special case. Suppose [ b ,c]=the regression coefficients in a regression of y on [ X , z ]and d is the same, but computed to force the coefficient on z to be 0. This removes z from the regression. (Well discuss how this is done shortly.) So, we are comparing the results that we get with and without the variable z in the equation. Results which we can show: Dropping a variable(s) cannot improve the fit  that is, reduce the sum of squares. Adding a variable(s) cannot degrade the fit  that is, increase the sum of squares. The algebraic result is on text page 31. Where u = the residual in the regression of y on [ X,z ] and e = the residual in the regression of y on X alone, uu = e e c 2 ( z * z *) e e where z * = M X z . This result forms the basis of the NeymanPearson class of tests of the regression model. The Fit of the Regression Variation: In the context of the model we speak of variation of a variable as movement of the variable, usually associated with (not necessarily caused by) movement of another variable. Total variation = = y M y . M = I i(ii)1 i = the M matrix for X = a column of ones....
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 Spring '11
 PP
 Econometrics

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