以混合頻率誤差修正模型預測台灣通貨膨脹

金俊洲

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以混合頻率誤差修正模型預測台灣通貨膨脹

Forecasting Taiwan’s Inflation with Mixed Frequency Error Correction Model

金俊洲 , Masters　　Advisor：李慶男　　

繁體中文

預測；通貨膨脹；共整合；混合頻率誤差修正模型；混和數據抽樣； prediction ； mixed data sampling ； inflation ； cointegration ； Mixed frequency error correction model

Abstract │ Reference (31) │ Altmetrics

Abstract 〈TOP〉

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The main topic of this paper is to forecast the inflation of Taiwan. As a most common prediction model, Error Correction Model (ECM) uses data that must be processed at the same frequency. The omission of high-frequency variables for observation may lead to loss of information. Therefore, in this paper, the Mixed Frequency Error Correction Model (MF- ECM) presented by Götz et al. (2014) is adopted along with Mixed Data Sampling. Sampling at different frequencies can obtain information at the low-frequency and high-frequency as well as explore the short and long-term relationship between the non-stationary data. It not only has the advantages of cointegration and error correction, but also improves both the problem of the high-frequency data loss and the performance of predictability.
The period for data collection was from January 2006 to December 2016, with 132 pieces of the monthly data and 528 pieces of the weekly data, the main variables being the Wholesale Price Index (WPI), Consumer Price Index (CPI) and crude oil prices. The Mixed Frequency Error Correction Model is used to predict WPI and CPI, and whether MF-ECM is better than ECM in predictability is also explored. According to the assessment at the three indexes of power of prediction, the empirical result of the study shows that MF-ECM is better than ECM in predictability.

Reference ( 31 ) 〈TOP〉

李見發、洪振義、林益倍 (2012),〈國際原油價格上漲對台灣產業生產成本與物價水準的影響〉,《應用經濟論叢》,92,163-197。
連結：
張呈徽、李仁耀、呂軒宇 (2012),〈國際油價對消費者物價指數之影響分析〉,《修平學報》,25,79-90。
連結：
羅佑傑 (2016),《油價、利率與通貨膨脹》,(臺北:國立台北大學經濟研究所碩士論文)。
連結：
Akaike, H. (1974). ‘A new look at the statistical model identification’, Automatic Control, IEEE Transactions on, Vol. 19(6), pp. 716–723.
連結：
Clements, M. and Galvão, A. B. (2008). ‘Macroeconomic forecasting with mixed- frequency data’, Journal of Business and Economic Statistics, Vol. 26, pp. 546–554.
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