Econometric Applications of Maximum Likelihood Methods - download pdf or read online

By Jan Salomon Cramer

ISBN-10: 0511572050

ISBN-13: 9780511572050

ISBN-10: 0521253179

ISBN-13: 9780521253178

ISBN-10: 0521378575

ISBN-13: 9780521378574

The arrival of digital computing allows the empirical research of financial types of some distance higher subtlety and rigour than ahead of, while many fascinating rules weren't up as the calculations concerned made this impracticable. The estimation and checking out of those extra difficult versions is mostly in keeping with the tactic of extreme chance, that's a well-established department of mathematical data. Its use in econometrics has resulted in the advance of a few designated strategies; the explicit stipulations of econometric study in addition call for sure alterations within the interpretation of the fundamental argument. This ebook is a self-contained creation to this box. It includes 3 components. the 1st bargains with normal gains of extreme probability equipment; the second one with linear and nonlinear regression; and the 3rd with discrete selection and similar micro-economic versions. Readers should still already be acquainted with straight forward statistical concept, with utilized econometric learn papers, or with the literature at the mathematical foundation of utmost probability thought. they could additionally try out their hand at a few complicated econometric learn in their personal.

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Extra resources for Econometric Applications of Maximum Likelihood Methods

Sample text

The constant of proportionality c depends on /, the number of dimensions, and on the right-hand side of the quadratic equation concerned; this is different for (26), (27), and (28). It follows from (31) that \H-*\*\Vt\ (32) This sets a lower limit to the determinant of Vt, always positive, which is also known as the generalized variance of the vector t and used as a scalar measure of its dispersion. We now know why. Secondly, any linear combination of the elements of 6° with known coefficients, say aT6°, is estimated without bias by aTt.

In statistical writings the standard explanation is that asymptotic results are approximately valid in large samples, and that we can make the approximation as close as we wish by increasing the sample size. This presupposes that the observations are generated by a well-defined statistical experiment, and that increasing their number is a practical proposition. In many cases the latter condition is not met, if only for reasons of costs or of time. We may then still derive substantial intellectual satisfaction from considering the hypothetical case.

Clearly the two versions of the model can only be fully equivalent if we can retrieve the first from the second as well as we can construct the second from the first. There must therefore be a one-to-one correspondence between the two (IX I) parameter vectors 9 and t] by the vector functions After the transformation from 6 to t] the true parameter vector is of course r\° = g(0°), and the parameter space is transformed from G to 9*; when we retain the same symbol we use an asterisk to distinguish the transformed model from the original.

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Econometric Applications of Maximum Likelihood Methods by Jan Salomon Cramer

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