Robust Covariance Matrix Estimation in Time Series: A Review

被引：1
|
作者：
Hirukawa, Masayuki [1 ]
机构：
[1] Ryukoku Univ, Fac Econ, 67 Tsukamoto cho,Fushimi Ku, Kyoto 6128577, Japan
来源：
ECONOMETRICS AND STATISTICS | 2023年 / 27卷
基金：
日本学术振兴会;
关键词：
Bandwidth; Generalized method of moments; Heteroskedasticity and autocorrelation; robust inference; Kernel; Long -run variance; Positive semi -definite; LONG-RUN VARIANCE; UNIT-ROOT TESTS; GENERALIZED-METHOD; POSITIVE SEMIDEFINITE; PARAMETER INSTABILITY; BANDWIDTH SELECTION; ORIGIN KERNELS; HETEROSKEDASTICITY; CONSISTENT; INFERENCE;
D O I：
10.1016/j.ecosta.2021.12.001
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
In the analysis of economic, financial and other time series, long-run variance estimators play an important role in estimating model parameters more efficiently and drawing more accurate statistical inference on the parameters. A non-technical review of long-run variance estimation is provided. Both parametric and nonparametric estimators are discussed. Kernel methods are dominant among all estimation procedures, and therefore recent developments in kernel-smoothed estimators and related inference are presented. The information given can help practitioners decide on a suitable long-run variance estimator. & COPY; 2021 EcoSta Econometrics and Statistics. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码：36 / 61
页数：26
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