Asymptotic Inference for the Weak Stationary Double AR(1) Model

• 作者: Augustine C. M. Wong Fang Chang Yanyan Wu
• 来源期刊: 统计学期刊(英文)
• 学科: 数学
• 关键词: CANONICAL Parameter DOUBLE AUTOREGRESSIVE Model P-VALUE Function Signed Log-Likelihood Ratio Statistic
• 年，卷（期）: 2012,（2）
• 页码范围: 141-152
• 页数: 12页
• 分类号: O1

摘要

An AR(1) model with ARCH(1) error structure is known as the first-order double autoregressive (DAR(1)) model. In this paper, a conditional likelihood based method is proposed to obtain inference for the two scalar parameters of interest of the DAR(1) model. Theoretically, the proposed method has rate of convergence O(n-3/2). Applying the proposed method to a real-life data set shows that the results obtained by the proposed method can be quite different from the results obtained by the existing methods. Results from Monte Carlo simulation studies illustrate the supreme accuracy of the proposed method even when the sample size is small.