Journal of the Korean Data and Information Science Society

한국데이터정보과학회 (The Korean Data and Information Science Society)
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소지역 실업률의 패널추정을 위한 일반화커널추정방정식

Generalized kernel estimating equation for panel estimation of small area unemployment rates

  • 심주용 (인제대학교 데이터정보학과) ;
  • 김영원 (숙명여자대학교 통계학과) ;
  • 황창하 (단국대학교 응용통계학과)
  • Shim, Jooyong (Department of Data Science, Inje University) ;
  • Kim, Youngwon (Department of Statistics, Sookmyung Women's University) ;
  • Hwang, Changha (Department of Applied Statistics, Dankook University)
  • 투고 : 2013.06.29
  • 심사 : 2013.08.23
  • 발행 : 2013.11.30
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초록

오늘날 높은 실업률은 대부분의 국가에서 중요한 문제 중의 하나이다. 한편 소지역의 노동 관련 통계에 대한 요구가 지난 몇년간 급속도로 증가하였다. 그러나 대부분의 공식통계를 생산하기 위한 표본설계는 대영역의 통계를 생산할 목적으로 설계되기 때문에 소지역의 경우 배정되는 표본조사단위수가 극히 적어 신뢰성 있는 통계 산출이 어렵다. 그리고 소지역 추정에 대한 대부분의 기존 연구들은 특정 시점에서의 추정에 국한되어 왔다. 그러나 대부분의 공식통계들은 월, 분기 또는 연 단위로 측정되는 패널자료이기 때문에 이를 고려한 추정방법이 필요하다. 본 논문에서는 패널자료의 분석을 위해 유용하게 사용되고 있는 일반화추정방정식의 비모수적 버전인 일반화커널추정방정식을 도출하여 조사시점을 고려한 소지역 실업률의 추정에 활용하는 방안을 제안한다. 모의실험을 통하여 일반화커널추정방정식 방법, 일반화추정방정식 방법 및 일반화선형모형과 비교한다. 그리고 2005년 1월부터 12월까지 경상남도 및 울산광역시의 25개 시군구의 경제활동인구조사의 패널자료에 위에서 언급한 세 가지 방법을 적용하여 해당 소지역의 월별 실업률을 추정한다.

The high unemployment rate is one of the major problems in most countries nowadays. Hence, the demand for small area labor statistics has rapidly increased over the past few years. However, since sample surveys for producing official statistics are mainly designed for large areas, it is difficult to produce reliable statistics at the small area level due to small sample sizes. Most of existing studies about the small area estimation are related with the estimation of parameters based on cross-sectional data. By the way, since many official statistics are repeatedly collected at a regular interval of time, for instance, monthly, quarterly, or yearly, we need an alternative model which can handle this type of panel data. In this paper, we derive the generalized kernel estimating equation which can model time-dependency among response variables and handle repeated measurement or panel data. We compare the proposed estimating equation with the generalized linear model and the generalized estimating equation through simulation, and apply it to estimating the unemployment rates of 25 areas in Gyeongsangnam-do and Ulsan for 2005.

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