Highly accurate likelihood analysis for the seemingly unrelated regression problem [An article from: Journal of Econometrics]

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Description:
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.