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摘要翻译：
本文给出了有限方差下未知形式（条件）异方差自回归模型的一个完整的推理过程。首先建立了该模型的加权最小绝对偏差估计(LADE)的渐近正态性。其次，我们发展了随机加权(RW)方法来估计其渐近协方差矩阵，从而实现了Wald检验。第三，构造了一个用于模型检验的portmanteau检验，并利用RW方法求出其临界值。作为一种特殊的加权自适应LADE，提出了可行自适应LADE(ALADE)，并证明了其与不可行自适应LADE具有相同的效率。通过对三个美国经济数据集的模拟结果和实际数据分析，说明了我们基于可行的ALADE的整个方法的重要性。
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英文标题：
《Statistical inference for autoregressive models under heteroscedasticity
of unknown form》
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作者：
Ke Zhu
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最新提交年份：
2018
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分类信息：
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
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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。
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英文摘要：
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.
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PDF链接：
https://arxiv.org/pdf/1804.02348
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