发帖
雷达卡
摘要翻译：
本文考虑了一个增广的双自回归(DAR)模型，该模型允许零波动率系数来规避DAR模型中的过参数化问题。由于波动系数可能在边界上，基于高斯拟最大似然估计(GQMLE)的统计推断方法变得非标准，其渐近性要求数据具有有限的六阶矩，从而缩小了研究重尾数据的适用范围。针对这一不足，本文提出了一种基于自加权GQMLE的系统的DAR模型统计推理方法。除Lagrange乘子检验统计量外，Wald、拟似然比和portmanteau检验统计量均具有非标准渐近性。只要数据是平稳的，整个过程都是有效的，仿真研究和一个实例说明了它的有效性。
---
英文标题：
《Non-standard inference for augmented double autoregressive models with
null volatility coefficients》
---
作者：
Feiyu Jiang, Dong Li, Ke Zhu
---
最新提交年份：
2019
---
分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
--
一级分类：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
--
---
英文摘要：
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.
---
PDF链接：
https://arxiv.org/pdf/1905.01798
京公网安备 11010802022788号
