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Highly accurate likelihood analysis for the seemingly unrelated regression problem
(with D.A.S. Fraser and M. Rekkas)
Journal of Econometrics , 127(1), 2005, pp. 17-33
Abstract: The linear and nonlinear seemingly unrelated regression problem with general error distribution is analyzed using recent likelihood theory that arguably provides the definitive distribution for assessing a scalar parameter; this involves implicit but well defined conditioning and marginalization for determining intrinsic measures of departure. Highly accurate p-values are obtained for the key difference between two regression coefficients of central interest. The p-value gives the statistical position of the data with respect to the key parameter. The theory and the results indicate that this methodology provides substantial improvement on first-order likelihood procedures, both in distributional accuracy, and in precise measurement of the key parameter.
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