A Robust Modified Cox Test for Time Series Models

• 발행기관: 한국자료분석학회
• 저자명: 김동근(Donggeun Kim)
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.7 No.4, 1103~1115쪽, 전체 13쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2005.08.30

영문 초록

We reexamine the modified Cox test by Kim(2005) and propose a robust modified Cox test that does not require normality assumption. This approach can extend its applicability to more various time series models from asymmetric conditional distributions or thicker tail distributions than normal distribution. Some small finite sample simulation experiments show that this proposed test performs well in different sample sizes. The empirical applications to two time series data sets are also performed.

목차

1. Introduction

2. A Robust Modified Cox Test

3. Simulation Experiments

4. Empirical Applications under Nonnormality

5. Conclusions

References

키워드

Robust modified Cox test Conditional means Conditional variance Normality