Adaptive estimators and tests of stationary and nonstationary short- and long-memory ARFIMA-GARCH models

成果类型:
Article
署名作者:
Ling, SQ
署名单位:
Hong Kong University of Science & Technology
刊物名称:
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
ISSN/ISSBN:
0162-1459
DOI:
10.1198/016214503000000918
发表日期:
2003
页码:
955-967
关键词:
maximum-likelihood-estimation autoregressive time-series efficient estimation unit-root differencing parameter Asymptotic Normality regression inflation inference range
摘要:
This article considers the fractionally autoregressive integrated moving average [ARFIMA(p, d, q)] models with GARCH errors. The process generated by this model is short memory, long memory, stationary, and nonstationary, respectively, when d is an element of (- 1/2, 0), d is an element of (0, 1/2), d is an element of (- 1/2, 1/2), and d is an element of (1/2, infinity). Using a unified approach, the local asymptotic normality of the model is established for d is an element of U-j=0(infinity)(J - 1/2, J + 1/2). The adaptivity and efficiency of the estimating parameters are discussed. In a class of loss functions, the asymptotic minimax bound of the estimators for the model is given when the density f of rescaled residuals is unknown. An adaptive estimator is constructed for the parameters in the ARFIMA part when f is symmetric, and a general form of the efficient estimator is also constructed for all the parameters in the ARFIMA and GARCH parts. When the density f is unknown, Wald tests are constructed for testing the unit root +1 against the class of fractional unit roots. It is shown that these tests asymptotically follow the chi-squared distribution and are locally most powerful. These results are new contributions to the literature, even for the ARFIMA model with iid errors, except for the adaptive estimator in this case with d e (0, 1/2). The performance of the asymptotic results in finite samples is examined through Monte Carlo experiments. An application to the U.S. Consumer Price Index inflation series is given, and a clear conclusion from this is that the series is neither an I(0) nor an I(I), but rather than an I(d) process with d approximate to 0.288.