arXiv 6 May 2019 · Econometrics · publishedJournal of Econometrics (2019) · 2 citations (OpenAlex)
arXiv:1905.01798 · PDF · DOI · OpenAlex · Extracted main text
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 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 is stationary, and its usefulness is illustrated by simulation studies and one real example.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Pedersen, R.S (2017) Inference and testing on the boundary in extended constant conditional correlation GARCH models | 1.000 | 6 | 3 | 100% |
| 2 | Ling, S (2007) Self-weighted and local quasi-maximum likelihood estimators for ARMA-GARCH/IGARCH models | 1.000 | 5 | 3 | 100% |
| 3 | Francq, C., Zakoïan, J.-M (2009) Testing the nullity of GARCH coeffcients: correction of the standard tests and relative effciency comparisons | 0.950 | 7 | 4 | 86% |
| 4 | Francq, C., Zakoïan, J.-M (2007) Quasi-maximum likelihood estimation in GARCH processes when some coefficients are equal to zero | 0.894 | 7 | 3 | 71% |
| 5 | Ling, S (2007) A double AR($p$) model: structure and estimation | 0.843 | 4 | 3 | 75% |
| 6 | Ling, S (2005) Self-weighted least absolute deviation estimation for infinite variance autoregressive models | 0.811 | 4 | 2 | 100% |
| 7 | Andrews, D.W.K (1999) Estimation when a parameter is on a boundary | 0.737 | 3 | 2 | 100% |
| 8 | Ling, S (2004) Estimation and testing stationarity for double-autoregressive models | 0.737 | 3 | 2 | 100% |
| 9 | Zhu, K., Ling, S (2015) LADE-based inference for ARMA models with unspecified and heavy-tailed heteroscedastic noises self | 0.644 | 2 | 2 | 100% |
| 10 | Jiang, F., Li, D., Zhu, K (2019) Non-standard inference for augmented double autoregressive models with null volatility coefficients self | 0.585 | 4 | 3 | 25% |
Showing the top 10 of 72 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 2509.13492 | 0.843 | 4 | 4 |
| 2 | Adaptive inference for a semiparametric generalized autoregressive conditional heteroskedasticity model | 0.405 | 1 | 1 |