시계열 자료에서 불변하는 인과성 탐색: 원-달러 환율 데이터에 적용
Invariant causal prediction for time series data: Application to won dollar exchange rate data
- 주제(키워드) Chow 검정 , Granger 인과 방법 , 원 달러 환율 , 인과추론 , causal prediction , Chow test , Granger causality , won-dollar exchange rate
- 주제(기타) 통계학
- 설명문(URI) https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002771565
- 등재 KCI등재
- 발행기관 한국통계학회
- 발행년도 2021
- URI http://www.dcollection.net/handler/ewha/000000184015
- 본문언어 한국어
초록/요약
Evaluating or predicting the effectiveness of economic policies is an important issue, but it is difficult to find an economic variable which causes a significant result because there are numerous variables that cannot be taken into account. A randomized controlled experiment is the best way to investigate causality, but it is not realistically possible to control through randomization and intervention in time series data such as macroeconomic data. Although some analysis methods have been proposed to find causality, the methods such as Granger causality method and Chow test are insufficient to explain causality. Recently, Pfister {\em et al.} (2019) proposed invariant causal prediction methods which can be applicable in time series data. In this paper, we introduce the method of Pfister {\em et al.} (2019) and use the method to find macroeconomic variables invariantly affecting the won-dollar exchange rate.
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