Download A Companion to Theoretical Econometrics by Badi H. Baltagi PDF

By Badi H. Baltagi

A spouse to Theoretical Econometrics presents a entire connection with the fundamentals of econometrics. This better half specializes in the rules of the sphere and even as integrates renowned subject matters frequently encountered by way of practitioners. The chapters are written through foreign specialists and supply up to date examine in components no longer often coated by way of commonplace econometric texts.
Focuses at the foundations of econometrics.
Integrates real-world issues encountered through execs and practitioners.
Draws on up to date examine in components no longer coated by means of usual econometrics texts.
- geared up to supply transparent, obtainable details and element to extra readings.

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Example text

4) related to the sum of squared residuals, but rather Q(β) ≡ 1 2 (y − x(β))‫ׅ‬PW (y − x(β)), where W is an n × l matrix of instrumental variables. 32), with β instead of θ, and with f(β) = y − x(β). In addition, J(β) = X(β), where X(β) is defined, exactly as for the GNR, to have the tith element ∂xt/∂βi(β). 7): See Davidson and MacKinnon (1993, ch. 7). 36), are not asymptotically valid when the assumption that the error terms are iid is violated. 3), in which E(uu‫ = )ׅ‬Ω, where Ω is an n × n diagonal matrix with tth diagonal element ω 2t .

17) provides an easy way to calculate what is arguably the best estimate of var(S). 2) be independent and identically distributed, as we have assumed so far. We will discuss ways to drop this assumption in Section 7. ARTIFICIAL REGRESSIONS 23 Since the GNR satisfies the one-step property, it and other artificial regressions can evidently be used to obtain one-step efficient estimates. However, although one-step estimation is of considerable theoretical interest, it is generally of modest practical interest, for two reasons.

6) where we write X(m) = X(β(m)) and x(m) = x(β(m)). 7) where the variables r(β) ≡ y − x(β) and R(β) ≡ X(β) are evaluated at β(m). 7) corresponding to the parameter σ 2, because the criterion function Q(β) does not depend on σ 2. This is one of the features of the GNR that makes it a nonstandard artificial regression. 3) around the point β. In the special case in which the original model is linear, x(β) = Xβ, where X is the matrix of independent variables. Since X(β) is equal to X for all β in this special case, the GNR will simply be a regression of the vector y − Xβ on the matrix X.

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