Econometric Diagnosis of Competitive Localization
Econometric Diagnosis of Competitive Localization
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In our eReader you can find the full English version of the book. Read Econometric Diagnosis of Competitive Localization Online - link to read the book on full screen. Our eReader also allows you to upload and read Pdf, Txt, ePub and fb2 books. In the Mini eReder on the page below you can quickly view all pages of the book - Read Book Econometric Diagnosis of Competitive Localization
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The residual covariance matrix, S, with typical element s. . , is thus treated in what follows as having been generated by observations from a multivariate normal distribution with covariance matrix l and zero mean vector.
Under this assumption, the (N - l)x(N - 1) matrix S has the Wishart distribution, and the log-likelihood function is the following: (4) L = C - (T/2)lnlZl + [ (T - N)/2]lnlSl - (T/2)tr( E"^S), where C is a constant and T is the number of observations. (See, for instance, Rao ...(1973, p. 398). Rao uses S to denote the dispersion matrix, which equals T times the sample covariance matrix. ) Given S, the unrestricted maximum of L is obtained by setting Z = S, and the maximum value is (5) L = C - (N/2)lnlSI - T(N - l)/2.
In order to maximize L subject to the restrictions given by (3) with z = 0, we make use of the fact that the following are valid if those restrictions - 8 - hold (Rao 1973, pp. 32-3): N-1 (6a) l[l = ( n v. )(l + V D), whert i = l N-1 (6b) D = E (1/v. ); and i = l (6c) [I ^].
What to read after Econometric Diagnosis of Competitive Localization?
You can find similar books in the "Read Also" column, or choose other free books by Richard Schmalensee to read onlineMoreLess
To quickly assess the difficulty of the text, read a short excerpt:
The residual covariance matrix, S, with typical element s. . , is thus treated in what follows as having been generated by observations from a multivariate normal distribution with covariance matrix l and zero mean vector.
Under this assumption, the (N - l)x(N - 1) matrix S has the Wishart distribution, and the log-likelihood function is the following: (4) L = C - (T/2)lnlZl + [ (T - N)/2]lnlSl - (T/2)tr( E"^S), where C is a constant and T is the number of observations. (See, for instance, Rao ...(1973, p. 398). Rao uses S to denote the dispersion matrix, which equals T times the sample covariance matrix. ) Given S, the unrestricted maximum of L is obtained by setting Z = S, and the maximum value is (5) L = C - (N/2)lnlSI - T(N - l)/2.
In order to maximize L subject to the restrictions given by (3) with z = 0, we make use of the fact that the following are valid if those restrictions - 8 - hold (Rao 1973, pp. 32-3): N-1 (6a) l[l = ( n v. )(l + V D), whert i = l N-1 (6b) D = E (1/v. ); and i = l (6c) [I ^].
What to read after Econometric Diagnosis of Competitive Localization?
You can find similar books in the "Read Also" column, or choose other free books by Richard Schmalensee to read onlineMoreLess
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