کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5096204 | 1478578 | 2012 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Inference in regression models with many regressors
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
آمار و احتمال
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
We investigate the behavior of various standard and modified F, likelihood ratio (LR), and Lagrange multiplier (LM) tests in linear homoskedastic regressions, adapting an alternative asymptotic framework in which the number of regressors and possibly restrictions grows proportionately to the sample size. When the restrictions are not numerous, the rescaled classical test statistics are asymptotically chi-squared, irrespective of whether there are many or few regressors. However, when the restrictions are numerous, standard asymptotic versions of classical tests are invalid. We propose and analyze asymptotically valid versions of the classical tests, including those that are robust to the numerosity of regressors and restrictions. The local power of all asymptotically valid tests under consideration turns out to be equal. The “exact” F test that appeals to critical values of the F distribution is also asymptotically valid and robust to the numerosity of regressors and restrictions.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Econometrics - Volume 170, Issue 2, October 2012, Pages 368-382
Journal: Journal of Econometrics - Volume 170, Issue 2, October 2012, Pages 368-382
نویسندگان
Stanislav Anatolyev,