臺灣博碩士論文加值系統

本文主要探討的議題是預測台灣通貨膨脹,常見的預測模型 為誤差修正模型 (Error Correction Model, ECM),資料必須在相同 頻率下進行,由於省略了高頻變數觀察,可能導致訊息丟失,因 此,本文採用 Götz et al. (2014) 所提出的混合頻率誤差修正模型 (mixed frequency error correction model, MF-ECM),引入混合數據抽 樣 (mixed data sampling),用於不同頻率下抽樣,能同時保有低頻資 料和高頻資料的訊息且能探討非定態資料間的長短期關係,不僅擁 有共整合 (cointegration) 與誤差修正的優點,也改善了喪失高頻資料 的缺點,提高預測的能力。
研究資料期間為 2006 年 1 月至 2016 年 12 月,月資料 132 筆、 週資料 528 筆,主要變數為躉售物價指數 (WPI)、消費者物價指數 (CPI) 及原油價格,採用混合頻率誤差修正模型來預測 WPI 及 CPI, 並探討 MF-ECM 是否會比 ECM 的預測力好,而研究的實證結果根 據三項預測指標的評估顯示 MF-ECM 預測力優於 ECM。

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.

口試委員會審定書................................................................................. . i
摘要........................................................................................................ ii
ABSTRACT............................................................................................ iii
圖目錄.................................................................................................... vi
表目錄 ................................................................................................... vii
第一章 緒論............................................................................................ 1
1.1 研究動機與目的................................................................................ 1
1.2 研究架構........................................................................................... 2
第二章 文獻回顧..................................................................................... 3
2.1 通貨膨脹相關理論回顧 .................................................................... 3
2.2 計量模型文獻回顧............................................................................ 6
2.3 實證文獻回顧 ................................................................................... 7
第三章 研究方法..................................................................................... 9
3.1 單根檢定........................................................................................... 9
3.2 共整合檢定........................................................................................ 11
3.2.1 Engle-Granger兩階段共整合檢定 .................................................. 11
3.2.2 Johansen共整合檢定 ..................................................................... 12
3.3 最適落後期數 .................................................................................... 15
3.4 混合頻率誤差修正模型 ...................................................................... 16
3.4.1 混合頻率自迴歸分佈滯後模型
(Mixed frequency autoregression distributed lag model, MF-ADL) ......... 16
3.4.2 混合頻率動態共整合 ....................................................................... 17
3.4.3 非限制(Unrestricted)之短期動態預測模型 ....................................... 19
3.5 評估預測表現 ..................................................................................... 19
3.6 Diebold-Mariano檢定 .......................................................................... 20
第四章 實證分析與結果.............................................................................. 21
4.1 資料來源與處理.................................................................................... 21
4.2 決定落後期數 ....................................................................................... 22
4.3 共整合檢定............................................................................................ 23
4.4 混合頻率誤差修正模型 ......................................................................... 24
4.4.1 估計共整合關係誤差修正項 ............................................................... 25
4.4.2 估計混合頻率誤差修正模型 ............................................................... 25
4.5 樣本內預測力比較................................................................................. 26
4.6 樣本外預測............................................................................................ 28
第五章 結論................................................................................................. 30
參考文獻..................................................................................................... 32
附錄A.......................................................................................................... 35

中文部分
李慶男 (2005),《時間序列講義》(高雄:國立中山大學),《國立中山大學經濟學研究所》,〈http://econ.nsysu.edu.tw/files/11-1124-1342.php〉。
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。
陳旭昇 (2013),《時間序列分析: 總體經濟與財務金融之應用》,(臺北:東華書局,二版)。
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。
張萃貞、鄭雅綺 (2008),〈油價上漲對國內物價及人民生活負擔的影響簡析〉,《經濟研究》,8 卷,33-50。
張筱嵐 (2009),《原物料指數與總經物價指數關聯性分析》,(臺北:國立政治大學金融研究所碩士論文)。
郭宗憲 (2008),《世界主要原物料價格指數與台灣消費者物價指數的關聯 性》,(臺北:國立交通大學經營管理研究所碩士班碩士論文)。
郭柱延 (2011),《石油危機時期之物價問題與相關經濟政策分析》,(嘉義: 國立中正大學國際經濟學研究所碩士論文)。
彭曄 (2015),《國際油價與消費者分類物價指數之關聯分析》,(桃園:國立 中央大學產業經濟研究所在職專班碩士論文)。
楊奕農 (2009),《時間序列分析: 經濟與財務上之應用》,(臺北:雙葉書局, 二版)。
賴景昌 (2011),《總體經濟學》,(臺北:雙葉書局,三版)。
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)。

英文部分
Akaike, H. (1974). ‘A new look at the statistical model identification’, Automatic Control, IEEE Transactions on, Vol. 19(6), pp. 716–723.
Armesto, M. T., Engemann, K. M. and Owyang, M. T. (2010). ‘Forecasting with Mixed Frequencies’, Federal Reserve Bank of St. Louis Review, Vol. 92(6), pp. 521-36.
Clements, M. and Galvão, A. B. (2008). ‘Macroeconomic forecasting with mixed- frequency data’, Journal of Business and Economic Statistics, Vol. 26, pp. 546–554.
Diebold, F. X. and Mariano, R. S. (1995). ‘Comparing predictive accuracy’, Journal of Business & Economic Statistics, Vol. 13, pp. 253-263.
Engle, R. F. and Granger, C. W. (1987). ‘Cointegration and error correction: representation, estimation, and testing’, Econometrica: journal of the Econometric Society, pp. 251–276.
Granger, C. W. and Newbold, P. (1974). ‘Spurious regressions in econometrics’, Journal of econometrics, Vol. 2(2), pp. 111–120.
Götz, T. B., Hecq, A. and Urbain, J. P. (2014). ‘Forecasting mixed frequency time series with ecm-midas models’, Journal of Forecasting, Vol. 33, pp. 198–213.
Ghysels, E., Santa-Clara, P. and Valkanov, R. (2004). The Midas touch: mixed data sampling regression models, Working Paper 2004s-20, CIRANO.
Ghysels, E., Sinko, A. and Valkanov, R. (2007). ‘Midas regressions: further results and new directions’, Econometric Reviews, Vol. 26(1), pp. 53–90.
Johansen, S. (1988). ‘Statistical analysis of cointegration vectors’, Journal of eco nomic dynamics and control, Vol. 12(2), pp. 231–254.
Johansen, S. (1995). ‘Likelihood-based inference in cointegrated vector autoregres sive models’, Oxford University Press on Demand.
Johansen, S. and Juselius, K. (1990). ‘Maximum likelihood estimation and inference on cointegration—with applications to the demand for money’, Oxford Bulletin of Economics and statistics, Vol. 52(2), pp. 169–210.
Miller, J. I. (2012). Mixed-frequency cointegrating regressions with parsimonious distributed lag structures, Working Paper 1211, Department of Economics, University of Missouri.
Phillips, P. C. and Ouliaris, S. (1990). ‘Asymptotic properties of residual based tests for cointegration’, Econometrica: Journal of the Econometric Society, pp. 165– 193.
Said, S. E. and Dickey, D. A. (1984). ‘Testing for unit roots in autoregressive moving average models of unknown order’, Biometrika, Vol. 71(3), pp. 599– 607.
Schwarz, G. (1978) ‘Estimating the dimension of a model’, The annals of statistics, Vol. 6(2), pp. 461–464.
Totonchi, J. (2011). ‘Macroeconomic Theories of Inflation’, International Confer ence on Economics and Finance Research, Vol. 4(2011)