지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
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This paper studies the performance of various forecasting models for Korean inflation rates. The models studied in this paper are the AR(p) model, the dynamic predictive regression model with such exogenous variables as the unemployment rate and the term spread, the inflation target model, the random walk model, and the dynamic predictive regression model using estimated factors along with the unemployment rate and the term spread. The sampling period studied in this paper is 2000M11--2011M06. Among the studied models, the dynamic predictive regression model using estimated factors along with the unemployment rate and the term spread tends to perform best when the factors are extracted from I(1) series and the variables for the factor extraction are selected by the criterion of the correlation of each variable with the inflation rate
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참고문헌 (28)
참고문헌 신청
Ang A / 2007 / Do macro varifables, asset markets, or surveys forecast inflation better / Journal of Monetary Economics 54 : 1163 ~ 1212
Atkeson A / 2001 / Are phillips curves useful for forecasting inflation / Federal Reserve Bank of Minneapolis Quarterly Review 25 (1) : 2 ~ 11
Bai J. / 2004 / Estimating cross-section common stochastic trends in nonstationary panel data / Journal of Econometrics 122 : 137 ~ 183
Boivin J / 2006 / Are more data always better for factor analysis / Journal of Econometrics 132 : 169 ~ 194
Breiman L. / 1996 / Bagging predictors / Machine Learning 24 : 123 ~ 140
Atkeson A / 2001 / Are phillips curves useful for forecasting inflation / Federal Reserve Bank of Minneapolis Quarterly Review 25 (1) : 2 ~ 11
Bai J. / 2004 / Estimating cross-section common stochastic trends in nonstationary panel data / Journal of Econometrics 122 : 137 ~ 183
Boivin J / 2006 / Are more data always better for factor analysis / Journal of Econometrics 132 : 169 ~ 194
Breiman L. / 1996 / Bagging predictors / Machine Learning 24 : 123 ~ 140
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