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# chap3 - Wooldridge Introductory Econometrics 4th ed Chapter...

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Wooldridge, Introductory Econometrics, 4th ed. Chapter 3: Multiple regression analysis: Estimation In multiple regression analysis, we extend the simple (two-variable) regression model to con- sider the possibility that there are additional explanatory factors that have a systematic ef- fect on the dependent variable. The simplest extension is the “three-variable” model, in which a second explanatory variable is added: y = β 0 + β 1 x 1 + β 2 x 2 + u (1) where each of the slope coefficients are now partial derivatives of y with respect to the x variable which they multiply: that is, hold- ing x 2 fixed, β 1 = ∂y/∂x 1 . This extension also allows us to consider nonlinear relationships, such as a polynomial in z, where x 1 = z and

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x 2 = z 2 . Then, the regression is linear in x 1 and x 2 , but nonlinear in z : ∂y/∂z = β 1 + 2 β 2 z. The key assumption for this model, analogous to that which we specified for the simple re- gression model, involves the independence of the error process u and both regressors, or ex- planatory variables: E ( u | x 1 , x 2 ) = 0 . (2) This assumption of a zero conditional mean for the error process implies that it does not systematically vary with the x 0 s nor with any linear combination of the x 0 s ; u is independent, in the statistical sense, from the distributions of the x 0 s. The model may now be generalized to the case of k regressors: y = β 0 + β 1 x 1 + β 2 x 2 + ... + β k x k + u (3) where the β coefficients have the same inter- pretation: each is the partial derivative of y
with respect to that x, holding all other x 0 s constant ( ceteris paribus ), and the u term is that nonsystematic part of y not linearly re- lated to any of the x 0 s. The dependent variable y is taken to be linearly related to the x 0 s, which may bear any relation to each other (e.g. poly- nomials or other transformations) as long as there are no exact linear dependencies among the regressors. That is, no x variable can be an exact linear transformation of another, or the regression estimates cannot be calculated. The independence assumption now becomes: E ( u | x 1 , x 2 , ..., x k ) = 0 . (4) Mechanics and interpretation of OLS Consider first the “three-variable model” given above in (1). The estimated OLS equation contains the parameters of interest: ˆ y = b 0 + b 1 x 1 + b 2 x 2 (5)

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and we may define the ordinary least squares criterion in terms of the OLS residuals, calcu- lated from a sample of size n, from this expres- sion: min S = n X i =1 ( y i - b 0 - b 1 x i 1 - b 2 x i 2 ) 2 (6) where the minimization of this expression is performed with respect to each of the three parameters, { b 0 , b 1 , b 2 } . In the case of k regres- sors, these expressions include terms in b k , and the minimization is performed with respect to the ( k +1) parameters { b 0 , b 1 , b 2 , ...b k } . For this to be feasible, n > ( k + 1) : that is, we must have a sample larger than the number of pa- rameters to be estimated from that sample.
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