단일변수 시계열 모형들의 주택가격지수 예측력 비교
Comparing the Forecasting Performance of Univariate Time Series Models with Korean House Price Index
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This paper compares the forecasting performance of four types of univariate time series models: ARIMA, GARCH, regime-switching, and unobserved-component(trend-cycle decomposition) model. I carry out in-sample and out-of-sample forecasting exercise to Korean house price index in the recursive estimation scheme. For the comparison of forecast performances, I compute root mean squared forecast errors and mean absolute forecast errors for each forecasting horizon of 1 to 36 months. I also test the statistical significance of the forecast comparison results, applying the asymptotic test developed by Diebold and Mariano(1995). In case of in-sample forecasts, regime-switching and unobserved-component models show better performance than other two models, along with statistical significance. Regime-switching model is a compelling choice until 12 month forecasting steps, while the unobserved-component model is the best for 24 and 36 months. In case of out-of-sample forecasts, I find that the regime-switching model is particularly ill-suited for forecasting, showing the same result as Crawford and Fratantoni(2003) with US house price data. Also, I find that the unobserved-component model shows the best out-of-sample forecasting performance.
Abstract
This paper compares the forecasting performance of four types of univariate time series models: ARIMA, GARCH, regime-switching, and unobserved-component(trend-cycle decomposition) model. I carry out in-sample and out-of-sample forecasting exercise to Korean house price index in the recursive estimation scheme. For the comparison of forecast performances, I compute root mean squared forecast errors and mean absolute forecast errors for each forecasting horizon of 1 to 36 months. I also test the statistical significance of the forecast comparison results, applying the asymptotic test developed by Diebold and Mariano(1995). In case of in-sample forecasts, regime-switching and unobserved-component models show better performance than other two models, along with statistical significance. Regime-switching model is a compelling choice until 12 month forecasting steps, while the unobserved-component model is the best for 24 and 36 months. In case of out-of-sample forecasts, I find that the regime-switching model is particularly ill-suited for forecasting, showing the same result as Crawford and Fratantoni(2003) with US house price data. Also, I find that the unobserved-component model shows the best out-of-sample forecasting performance.
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