By Patrick Kent Watson, Sonja Sabita Teelucksingh
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Extra info for A Practical Introduction to Econometric Methods Classical and Modern
It can be shown that, if the general linear regression model contains a constant term,1 then n y= ∑ t =1 n n yt = ∑ yˆ t t =1 n That is, the average value of the observed y values ( y) is also the average of the fitted values. Therefore, if the regression fit is a reasonably good one, we can expect that the 1 From now on, unless it is specifically stated otherwise, the general linear regression model will be assumed to contain a constant term which, without loss of generality, may be taken to be the first exogenous variable, xlt.
It would appear that we did well to drop Cg from the equation. 2 above reveals that Cg is highly correlated with both Y and pm/pd. So multicollinearity is indeed a problem, and the importance of Cg to the explanation of imports may have been masked. 3. This time it is the income variable that is dropped and the results are astonishing. ) highly significant. 3 OLS Regression fit of Mt = β1 + β3 pmt/pdt + β4 Cgt + ut ============================================================== LS // Dependent Variable is IMPORTS Date: 08/12/95 Time: 18:44 Sample: 1967 1991 Included observations: 25 ============================================================== Variable Coefficient Std.
1. 1 The values in parentheses below the estimated slope coefficients are the corresponding t-ratios. Notice that the t-ratio associated with the constant term is not reported. This is a matter of taste and reflects the authors’ view that more attention should be paid to the value of the slope coefficients unless there is some compelling reason to do otherwise. The values of R 2 and the F-statistic are clearly shown, as well as another statistic – the Durbin–Watson statistic (DW). The use of the Durbin–Watson statistic will be discussed in chapter 4.
A Practical Introduction to Econometric Methods Classical and Modern by Patrick Kent Watson, Sonja Sabita Teelucksingh