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미시자료를 이용한 총소비함수의 추정(Estimating aggregate consumption with household panel data) : 가구패널자료를 이용하여

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Frame of Image  것은 대단히 고무적이다. 점차 경 제․시회 관련 미시자료의 획득이 용이해지고 있는 상황임을 감안할 때, 이러한 분석법의 유용성은 더욱 커져갈 것이며, 특히 고령화와 같이 인구구조의 근본적인 변화가 예상되는 환경하에서 총소비의 흐름이 어 떠할 것인지를 예측하는 데 많은 도움이 될 것으로 기대한다. 저자는 보고서의 집필부터 탈고에 이르기까지 귀중한 조언과 비판을 아끼지 않은 박창균 박사를 비롯한 본원의 동료 연구위원들, 보고서에 대해 유익한 논평을 해 준 익명의 검토자들, 그리고 연구자료 및 원고의 취합과 정리에 많은 수고를 해 준 김현아 연구원 및 조미경 연구행정원 에게 깊이 감사하고 있다. 마지막으로 본 보고서에 제시된 견해는 집필자 개인의 것이며, 본원 의 공식 견해가 아님을 밝힌다.
2005년 12월
한국개발연구원 원장
현정택
목
발간사
차
요 약 ·································································································· 1 제1장 서론: 연구의 필요성 및 목적 ······ ················ ················ ·········· 2 제2장 미시자료를 이용한 총소비모형 ·· ··· ·· ·· ··· ·· ··· ·· ··· ·· ··· ·· ··· ·· ·· ··· ·· ··· · 7
제1절 개별 가구의 소비함수 ··························································· 8 제2절 개별 가구소비함수의 총량화 ·· ····· ···· ····· ···· ····· ···· ····· ···· ····· ···· 10
제3장 분 석 ···························· ···························· ···························· 14
제1절 가구소비함수의 추정 ··························································· 14 제2절 총소비함수 ·········································································· 15
제4장 맺음말 ············ ···································· ···································· 23 참고문헌 ··········· ·········· ·········· ··········· ·········· ·········· ··········· ·········· ·········· 24 [부록 Ⅰ] 사용된 변수의 정의 및 기초통계량 ···························· 27 [부록 Ⅱ] KLIPS를 이용한 가구소비함수 추정결과 ········· ············· 33 [부록 Ⅲ] 패널분석 및 KLIPS를 사용한 총소비증가율 추정 · · · · · · · 38 [부록 Ⅳ] 도시가계조사자


Full Text
Title 미시자료를 이용한 총소비함수의 추정(Estimating aggregate consumption with household panel data)
Similar Titles
Sub Title

가구패널자료를 이용하여

Material Type Reports
Author(Korean)

허석균

Publisher

서울:한국개발연구원

Date 2005
Series Title; No 정책포럼 / 2005-10
Pages 46
Subject Country South Korea(Asia and Pacific)
Language Korean
File Type Documents
Original Format pdf
Subject Economy < Macroeconomics
Holding KDI; KDI School

Abstract

This study suggests a way to estimate the aggregate consumption function by estimating and then aggregating individual household consumption functions with micro household data rather than using macro aggregate variables. The primary purpose of this paper lies in setting up a model that uses micro data to estimate and predict aggregate consumption, which is one of the pillars of a national economy. This approach approximates individual household consumption functions to an aggregate consumption function when they exhibit non-linearity with regard to particular household characteristics. This method is considered more adequate for mid-/long-term analyses than for short-term predictions because the analysis time period of the estimated aggregate consumption function needs to be linked with the time period when the micro data are acquired. Also, micro data mainly reflect volatile socio-economic distributions of population.
When individual household characteristics including earnings influence consumptions in a non-linear way, the cross-section distribution of the explaining variables determines aggregate consumption. Along with this, this study adopts the methodology proposed by Hildenbrand and Kneip (2005), which describes with statistical figures such as averages and co-variances the distributional characteristics of socio-economic variables including the age of the head of household, level of education, wealth, family composition and income, and captures changes in the aggregate consumption through variations of the statistically described variables. This approach can heighten the model’s explanatory power especially by taking into account co-variances among variables in estimating the aggregate consumption, while most previous methods depend only on the averages of the variables.
An analysis of data on 3,087 households (from the Korea Labor Income Panel Study in its 1-6th survey sessions) entered into the suggested model, however, failed to show the full advantages of this approach, mainly due to the short time period of the surveys. Despite its limitations, the aggregate consumption function estimation with micro data assists in understanding and predicting more precisely how the changes in the population’s distributional characteristics, for example, income distribution, affect aggregate consumption under stable economic circumstances, which makes it necessary to refine and improve the methodology.