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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2015.12
- 수록면
- 379 - 417 (39page)
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This study examines the predictive ability of a wide set of variables to predict Korean recessions based on probit models. In doing so, we extend probit-based recession forecasting models in several important and novel ways. First, in addition to commonly used financial variables, we incorporate several macro leading variables as potential predictors of recessions. Second, our forecasts use the algorithm of dynamic model selection/averaging (DMS/DMA), which allows for specific predictors to switch over time in a data-based manner.
Our main findings are as follows. First, in terms of both in-sample fits and out-of-sample forecasts, while financial indicators (such as interest rate spreads) are good predictors over short-horizons (i.e., one or three months ahead), some macro leading variables (such as commodity price index and job opening-to-application ratio) turn out to be useful over longer horizons. Second, forecasting using time-varying predictors (i.e., DMS/DMA models) performs well, beating individual best predictors for each forecast horizon. In addition, we show that forecasting using switching predictors outperforms the models that employ fixed predictors and the composite leading index, in most cases, especially in terms of the mean squared error (MSE). Third, we find strong evidence for predictor switching and illustrate how the performance of the key predictors has evolved over time.
Our main findings are as follows. First, in terms of both in-sample fits and out-of-sample forecasts, while financial indicators (such as interest rate spreads) are good predictors over short-horizons (i.e., one or three months ahead), some macro leading variables (such as commodity price index and job opening-to-application ratio) turn out to be useful over longer horizons. Second, forecasting using time-varying predictors (i.e., DMS/DMA models) performs well, beating individual best predictors for each forecast horizon. In addition, we show that forecasting using switching predictors outperforms the models that employ fixed predictors and the composite leading index, in most cases, especially in terms of the mean squared error (MSE). Third, we find strong evidence for predictor switching and illustrate how the performance of the key predictors has evolved over time.
목차
1. INTRODUCTION
2. PROBIT MODEL FOR FORECASTING RECESSIONS
3. RESULTS OF THE INDIVIDUAL PREDICTORS
4. FORECASTING WITH TIME-VARYING PREDICTORS
5. CONCLUDING REMARKS
REFERENCES
참고문헌 (35)
참고문헌 신청
[학술지(정기간행물)] - Bessec, M. / 2005 / What Causes the Forecasting Failure of Markov-switching Models? A Monte Carlo Study / Studies in Nonlinear Dynamics and Econometrics 9 (2)
[학술지(정기간행물)] - Bork, L / 2015 / Forecasting House Prices in the 50 States Using Dynamic Model Averaging and Dynamic Model Selection / International Journal of Forecasting 31 (1) : 63 ~ 78
[보고서] - Camacho, M / 2012 / Markov-Switching Dynamic Factor Models in Real Time
[단행본] - Chauvet, M / 2006 / Nonlinear Time Series Analysis of Business Cycles / Elsevier : 1 ~ 53
[학술지(정기간행물)] - Chauvet, M. / 2008 / A Comparison of the Real-Time Performance of Business Cycle Dating Methods / Journal of Business and Economic Statistics 26 (1) : 42 ~ 49
[학술지(정기간행물)] - Bork, L / 2015 / Forecasting House Prices in the 50 States Using Dynamic Model Averaging and Dynamic Model Selection / International Journal of Forecasting 31 (1) : 63 ~ 78
[보고서] - Camacho, M / 2012 / Markov-Switching Dynamic Factor Models in Real Time
[단행본] - Chauvet, M / 2006 / Nonlinear Time Series Analysis of Business Cycles / Elsevier : 1 ~ 53
[학술지(정기간행물)] - Chauvet, M. / 2008 / A Comparison of the Real-Time Performance of Business Cycle Dating Methods / Journal of Business and Economic Statistics 26 (1) : 42 ~ 49
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UCI(KEPA) : I410-ECN-0101-2016-320-002317344