Non-standard inference for augmented double autoregressive models with null volatility coefficients

Feiyu Jiang, Dong Li and Ke Zhu ()

Journal of Econometrics, 2020, vol. 215, issue 1, 165-183

Abstract: This paper considers an augmented double autoregressive (DAR) model, which allows null volatility coefficients to circumvent the over-parameterization problem in the DAR model. Since the volatility coefficients might be on the boundary, the statistical inference methods based on the Gaussian quasi-maximum likelihood estimation (GQMLE) become non-standard, and their asymptotics require the data to have a finite sixth moment, which narrows the applicable scope in studying heavy-tailed data. To overcome this deficiency, this paper develops a systematic statistical inference procedure based on the self-weighted GQMLE for the augmented DAR model. Except for the Lagrange multiplier test statistic, the Wald, quasi-likelihood ratio and portmanteau test statistics are all shown to have non-standard asymptotics. The entire procedure is valid as long as the data are stationary, and its usefulness is illustrated by simulation studies and one real example.

Keywords: Augmented DAR model; DAR model; Heavy-tailedness; Non-standard asymptotics; Parameter on the boundary; Portmanteau test; Self-weighted QMLE (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:215:y:2020:i:1:p:165-183

DOI: 10.1016/j.jeconom.2019.08.009

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