The Annals of Statistics
- Ann. Statist.
- Volume 47, Number 6 (2019), 3185-3215.
Statistical inference for autoregressive models under heteroscedasticity of unknown form
This paper provides an entire inference procedure for the autoregressive model under (conditional) heteroscedasticity of unknown form with a finite variance. We first establish the asymptotic normality of the weighted least absolute deviations estimator (LADE) for the model. Second, we develop the random weighting (RW) method to estimate its asymptotic covariance matrix, leading to the implementation of the Wald test. Third, we construct a portmanteau test for model checking, and use the RW method to obtain its critical values. As a special weighted LADE, the feasible adaptive LADE (ALADE) is proposed and proved to have the same efficiency as its infeasible counterpart. The importance of our entire methodology based on the feasible ALADE is illustrated by simulation results and the real data analysis on three U.S. economic data sets.
Ann. Statist., Volume 47, Number 6 (2019), 3185-3215.
Received: April 2018
Revised: August 2018
First available in Project Euclid: 31 October 2019
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Primary: 62F03: Hypothesis testing 62F12: Asymptotic properties of estimators 62F35: Robustness and adaptive procedures 62M10: Time series, auto-correlation, regression, etc. [See also 91B84]
Zhu, Ke. Statistical inference for autoregressive models under heteroscedasticity of unknown form. Ann. Statist. 47 (2019), no. 6, 3185--3215. doi:10.1214/18-AOS1775. https://projecteuclid.org/euclid.aos/1572487390
- Supplement to “Statistical inference for autoregressive models under heteroscedasticity of unknown form”. The supplement includes additional simulation results, applications, some technical lemmas and the remaining proofs.