우리나라 소비자전망지수의 경제예측력 제고 방안에 관한 연구
Forecasting Economic Variables Using Disaggregated Data for Consumer Expectations: A Comparison of Forecast Combination Methods
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본 연구는 우리나라 소비자전망지수의 세분화된 자료에 다양한 예측기법을 적용하여 그 중 어떠한 방법이 가장 우수한 성과를 보이는지를 분석한다. 예측대상 변수로는 산출, 소비, 고용, 물가, 금리 변수를 고려한다. 정보변수로는 다양한 기준에 따라 26개 그룹으로 세분화된 소비자전망지수를 고려한다. 예측기법으로는 전통적인 ARDL 방법뿐만 아니라, 단순 예측결합방법, 할인MSFE 가중평균법, 베이지안 모형평균법, 정보기준 가중평균법, 클러스터 결합방법, 주성분기법 등을 고려한다. 분석 결과 소비자전망지수가 거시경제 변수들을 예측하는 데 도움이 될 수 있으며, 클러스터 기법이 해당 지표의 활용도를 높이는 최선의 방법인 것으로 나타났다. 아울러, 클러스터 기법 중에서는 클러스터별 가중치를 OLS로 추정하는 방법과 이를 베이지안 기법으로 수축하는 방법이 가장 우수한 성과를 보이는 것으로 나타났다.
Abstract
Different consumers may form different expectations about future economic conditions. Therefore we need to combine individual forecasts rather than relying a single forecast from a particular consumer group. In this paper we investigate the relative performances of several combination methods in predicting economic variables such as output, consumption spending, employment, unemployment rate, price level, and interest rate, using disagregated data for the consumer expectations. In addition to the principal component method, we consider forecast combination methods based on simple averaging, discount MSFE, Bayesian shrinkage, information creteria, and clustering. We find that consumers' expectation indexes are useful to predict most of the economic variables we consider. We also find that the combination methods based on clustering outperform other forecasting methods, especially when the weight of each cluster is determined using ordinary least squares estimation or Bayesian shrinkage techniques.
참고문헌 (34)
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