Download A Companion to Theoretical Econometrics by Badi H. Baltagi PDF
By Badi H. Baltagi
A better half to Theoretical Econometrics offers a entire connection with the fundamentals of econometrics. This better half specializes in the rules of the sector and even as integrates well known subject matters frequently encountered by way of practitioners. The chapters are written by way of foreign specialists and supply updated study in parts now not frequently coated via ordinary econometric texts.
- Focuses at the foundations of econometrics.
- Integrates real-world themes encountered via execs and practitioners.
- Draws on up to date study in parts no longer lined by means of ordinary econometrics texts.
- Organized to supply transparent, available details and element to extra readings.
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Extra info for A Companion to Theoretical Econometrics
3). Finally, consider condition (2). Since X(β) plays the role of R(θ), we see that 1 n R(ׅθ)R(θ) = 1 n X(ׅβ)X(β). 14) is evaluated at any root-n consistent estimator T, it must tend to the same probability limit as n−1X ׅ0 X0. 15) n →∞ where σ 20 is the true variance of the error terms; see, for example, Davidson and MacKinnon (1993, ch. 5). Thus the GNR would satisfy condition (2) except that there is a factor of σ 20 missing. However, this factor is automatically supplied by the regression package.
19). Proof. To prove this theorem, we need to show two things. 21). This result follows by standard asymptotic arguments based on the one-step property. The second is that the vector n−1/2qׅ2 51s = n−1/2Rׅ2 (θ0)M1(θ0)r(θ0) + op(1) is asymptotically normally distributed. The equality here also follows by standard asymptotic arguments. The asymptotic normality of P implies that c is asymptotically normally distributed. 22), n−1/2qׅ2 51s must also be asymptotically normally distributed. 23) is a quadratic form in a normally distributed r-vector, the mean of which is zero, and the inverse of its covariance matrix.
56) in various ways. For example, it has been extended to tests of the functional form of F(x) by Thomas (1993) and to tests of ordered logit models by Murphy (1996). 36 R. G. MACKINNON 11 CONCLUSION In this chapter, we have introduced the concept of an artificial regression and discussed several examples. We have seen that artificial regressions can be useful for minimizing criterion functions, computing one-step estimates, calculating covariance matrix estimates, and computing test statistics.