| Автор | Jensen, Mark J. |
| Дата выпуска | 1995 |
| dc.description | Econometric techniques to estimate output supply systems, factor demand systems and consumer demand systems have often required estimating a nonlinear system of equations that have an additive error structure when written in reduced form. To calculate the ML estimate's covariance matrix of this nonlinear system one can either invert the Hessian of the concentrated log likelihood function, or invert the matrix calculated by pre-multiplying and post multiplying the inverted MLE of the disturbance covariance matrix by the Jacobian of the reduced form model. Malinvaud has shown that the latter of these methods is the actual limiting distribution's covariance matrix, while Barnett has shown that the former is only an approximation.In this paper, we use a Monte Carlo simulation study to determine how these two covariance matrices differ with respect to the nonlinearity of the model, the number of observations in the dataet, and the residual process. We find that the covariance matrix calculated from the Hessian of the concentrated likelihood function produces Wald statistics that are distributed above those calculated with the other covariance matrix. This difference becomes insignificant as the sample size increases to one-hundred or more observations, suggesting that the asymptotics of the two covariance matrices are quickly reached. |
| Формат | application.pdf |
| Издатель | Marcel Dekker, Inc. |
| Копирайт | Copyright Taylor and Francis Group, LLC |
| Тема | Nonlinear Sure Models |
| Тема | Covariance Estimates |
| Тема | Hypothesis Testing Of Neoclassical Theory |
| Название | A monte carlo study on two methods of calculating the mle's covariance matrix in a seemingly unrelated nonlinear regression. |
| Тип | research-article |
| DOI | 10.1080/07474939508800323 |
| Electronic ISSN | 1532-4168 |
| Print ISSN | 0747-4938 |
| Журнал | Econometric Reviews |
| Том | 14 |
| Первая страница | 315 |
| Последняя страница | 330 |
| Аффилиация | Jensen, Mark J.; Department of Economics, Southern Illinois University |
| Выпуск | 3 |
| Библиографическая ссылка | William Barnett, A. 1976. Maximum Likelihood and Iterated .Aitken Estimation of Nonlinear System of Equations. Journal of the American Statistical Association, 71: 354–360. |
| Библиографическая ссылка | Barten, A. P. 1969. Maximum Likelihood Estimation of a Complete Demand System. European Economic Review, 1: 7–73. |
| Библиографическая ссылка | Belsley and David, A. 1980. On the Efficient Computation of the Nonlinear Full-Information Maximum-Likelihood Estimator. Journal of Econometrics, 14: 203–225. |
| Библиографическая ссылка | Berndt, E. K., Hall, B. H., Hall, R. E. and Hausman, J. A. 1974. Estimation and Inference in Nonlinear Structural Models. Annals of Economics and Social Measurement, Oct: 653–665. |
| Библиографическая ссылка | Fletcher, R. 1963. A Rapidly Convergent Descent Method for Minimization. Computer Journal, 6: 163–168. |
| Библиографическая ссылка | Gallant, A. and Ronald. 1975. Seemingly Unrelated Nonlinear Regressions. Journal of Econometrics, 3: 35–50. |
| Библиографическая ссылка | Laitinen, K. 1978. Why is Demand Homogeneity So Often Rejected?. Economics Letters, 1: 187–191. |
| Библиографическая ссылка | Malinvaud, E. 1970. Statistical Methods of Econometrics, Amsterdam: North-Holland. |
| Библиографическая ссылка | Meisner, J. F. 1979. The Sad Fate of the Asymptotic Slutsky Symmetry Test for Large Systems. Economics Letters, 2: 231–233. |
| Библиографическая ссылка | Rosalsky Mercede, C., Renate, Finke. and Henri, Theil. 1984. The Downward Bias of Asymptotic Standard Errors of Maximum Likelihood Estimates of Non- Linear Systems. Economics Letters, 14: 207–211. |
| Библиографическая ссылка | Theil, Henri, Kennethl, W. and Clements, W. 1987. Cambridge, Mass: Ballinger. |
| Библиографическая ссылка | Theil, Henri and Mercedo Rosalsky, C. 1985. Least Squares and Maximum Likelihood Estimation of Non-Linear Systems. Economics Letters, 17: 119–122. |
| Библиографическая ссылка | Zellner, Arnold. 1962. An Efficient Method of Estimating Seemingly Unrelated Regressions and Test for Aggregation Bias. Journal of the American Statistical Association, 57: 348–368. |
