By Badi H. Baltagi
A significant other to Theoretical Econometrics offers a complete connection with the fundamentals of econometrics. This significant other specializes in the principles of the sphere and while integrates well known subject matters usually encountered by way of practitioners. The chapters are written through foreign specialists and supply updated learn in components no longer often coated by way of usual econometric texts.
- Focuses at the foundations of econometrics.
- Integrates real-world issues encountered by way of execs and practitioners.
- Draws on updated examine in parts no longer coated by means of ordinary econometrics texts.
- Organized to supply transparent, obtainable info and aspect to extra readings.
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Additional info for A Companion to Theoretical Econometrics
37) provides an estimator of var(S), which can be used in place of the usual estimator, s2(tׅt)−1. Like the latter, this heteroskedasticity-consistent covariance matrix estimator, or HCCME, can be computed by means of an artificial regression. We will refer to this regression as the heteroskedasticity-robust Gauss–Newton regression, or HRGNR. 30 R. G. MACKINNON In order to derive the HRGNR, it is convenient to begin with a linear regression model y = Xβ + u, and to consider the criterion function Q(β) = 1 2 (y − Xβ)ׅX(XׅΩX)−1X(ׅy − Xβ).
Such a quadratic form follows the χ2(r) distribution. n ARTIFICIAL REGRESSIONS 25 Remarks. 24) s = q1b1 + q2b2 + residuals. 25): 51s = 51q2b2 + residuals. 23), or statistics asymptotically equivalent to it. 26) which works because the denominator tends to a probability limit of 1 as n → ∞. This statistic is, of course, in F rather than χ2 form. Another frequently used test statistic is available if Q is actually the vector of restricted estimates, that is, the estimator that minimizes the criterion function when the restriction that θ2 = 0 is imposed.
Journal of the American Statistical Association 85, 220–7. G. MacKinnon (1992). A new form of the information matrix test. Econometrica 60, 145–57. G. MacKinnon (1993). Estimation and Inference in Econometrics. New York: Oxford University Press. G. MacKinnon (1999). Bootstrap testing in nonlinear models. International Economic Review 40, 487–508. , and T. Stengos (1994). Semiparametric specification testing of non-nested econometric models. Review of Economic Studies 61, 291–303. F. (1984). Wald, Likelihood Ratio and Lagrange Multiplier Tests in Econometrics.
A Companion to Theoretical Econometrics by Badi H. Baltagi