| Автор | Mcdonald, James B. |
| Автор | White, Steven B. |
| Дата выпуска | 1993 |
| dc.description | Numerous estimation techniques for regression models have been proposed. These procedures differ in how sample information is used in the estimation procedure. The efficiency of least squares (OLS) estimators implicity assumes normally distributed residuals and is very sensitive to departures from normality, particularly to "outliers" and thick-tailed distributions. Lead absolute deviation (LAD) estimators are less sensitive to outliers and are optimal for laplace random disturbances, but not for normal errors. This paper reports monte carlo comparisons of OLS,LAD, two robust estimators discussed by huber, three partially adaptiveestimators, newey's generalized method of moments estimator, and an adaptive maximum likelihood estimator based on a normal kernal studied by manski. This paper is the first to compare the relative performance of some adaptive robust estimators (partially adaptive and adaptive procedures) with some common nonadaptive robust estimators. The partially adaptive estimators are based on three flxible parametric distributions for the errors. These include the power exponential (Box-Tiao) and generalized t distributions, as well as a distribution for the errors, which is not necessarily symmetric. The adaptive procedures are "fully iterative" rather than one step estimators. The adaptive estimators have desirable large sample properties, but these properties do not necessarily carry over to the small sample case.The monte carlo comparisons of the alternative estimators are based on four different specifications for the error distribution: a normal, a mixture of normals (or variance-contaminated normal), a bimodal mixture of normals, and a lognormal. Five hundred samples of 50 are used. The adaptive and partially adaptive estimators perform very well relative to the other estimation procedures considered, and preliminary results suggest that in some important cases they can perform much better than OLS with 50 to 80% reductions in standard errors. |
| Формат | application.pdf |
| Издатель | Marcel Dekker, Inc. |
| Копирайт | Copyright Taylor and Francis Group, LLC |
| Тема | regression models |
| Тема | adaptive maximum likelihood estimation |
| Тема | generalized method of moments estimation |
| Тема | M-estimation |
| Тема | partially adaptive estimation |
| Тема | monte carlo simulation |
| Тема | C13 |
| Тема | C14 |
| Название | A comparison of some robust, adaptive, and partially adaptive estimators of regression models |
| Тип | research-article |
| DOI | 10.1080/07474939308800255 |
| Electronic ISSN | 1532-4168 |
| Print ISSN | 0747-4938 |
| Журнал | Econometric Reviews |
| Том | 12 |
| Первая страница | 103 |
| Последняя страница | 124 |
| Аффилиация | Mcdonald, James B.; Brigham young university |
| Аффилиация | White, Steven B.; Allstate Insurance Co. |
| Выпуск | 1 |
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