کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5095763 | 1376483 | 2015 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Asymptotic inference in multiple-threshold double autoregressive models
ترجمه فارسی عنوان
استنتاج همبستگی در چند مدل آسترگی دو طرفه
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
آمار و احتمال
چکیده انگلیسی
This paper investigates a class of multiple-threshold models, called Multiple Threshold Double AR (MTDAR) models. A sufficient condition is obtained for the existence and uniqueness of a strictly stationary and ergodic solution to the first-order MTDAR model. We study the Quasi-Maximum Likelihood Estimator (QMLE) of the MTDAR model. The estimated thresholds are shown to be n-consistent, asymptotically independent, and to converge weakly to the smallest minimizer of a two-sided compound Poisson process. The remaining parameters are n-consistent and asymptotically multivariate normal. In particular, these results apply to the multiple threshold ARCH model, with or without AR part, and to the multiple threshold AR models with ARCH errors. A score-based test is also presented to determine the number of thresholds in MTDAR models. The limiting distribution is shown to be distribution-free and is easy to implement in practice. Simulation studies are conducted to assess the performance of the QMLE and our score-based test in finite samples. The results are illustrated with an application to the quarterly US real GNP data over the period 1947-2013.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Econometrics - Volume 189, Issue 2, December 2015, Pages 415-427
Journal: Journal of Econometrics - Volume 189, Issue 2, December 2015, Pages 415-427
نویسندگان
Dong Li, Shiqing Ling, Jean-Michel Zakoïan,