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    Title: 台灣消費者物價指數的預測評估與比較
    The evaluations and comparisons of consumer price index`s forecasts in Taiwan
    Authors: 張慈恬
    Chang, Ci Tian
    Contributors: 徐士勛
    Hsu, Shih Hsun
    張慈恬
    Chang, Ci Tian
    Keywords: 通貨膨脹率預測
    樣本外預測
    貨幣模型
    成本加成模型
    菲力浦曲線
    期限結構
    隨機漫步模型
    ARIMA 模型
    VAR 模型
    Forecasting inflation
    out-of-sample forecast
    monetary model
    mark-up model
    Phillips curve
    term structure
    random walk model
    ARIMA model
    VAR model
    Date: 2010
    Issue Date: 2011-10-05 14:52:15 (UTC+8)
    Abstract: 本篇論文擴充Ang et al. (2007)之基本架構,分別建構台灣各式月資料與季資料的物價指數預測模型,並進行預測以及實證分析。我們用以衡量通貨膨脹率的指標為 CPI 年增率與核心CPI 年增率。我們比較貨幣模型、成本加成模型、6 種不同設定的菲力浦曲線模型、3 種期限結構模型、隨機漫步模型、 AO 模型、ARIMA 模型、VAR 模型、主計處(DGBAS)、中經院(CIER) 及台經院(TIER) 之預測。藉由此研究,我們可以完整評估出文獻上常用之各式月資料及季資料預測模型的優劣。

    我們實證結果顯示,在月資料預測模型樣本外預測績效表現方面, ARIMA 模
    型對 2 種通貨膨脹率指標的樣本外預測能力表現最好。至於季資料預測模型樣本外預測績效表現, ARIMA 模型對未來核心 CPI 年增率的樣本外預測能力表現最好; 然而,對於 CPI 年增率為預測目標的預測模型則不存在最佳的模型。此外,實證分析中我們也發現本研究所建構的模型預測表現仍遜於主計處的預測,但部份模型的樣本外預測能力表現則比中經院與台經院的預測為佳。
    This paper compares the forecasting performance of inflation in Taiwan. We conduct various inflation forecasting methods (models) for two inflation measures(CPI growth rate and core-CPI growth rate) by using monthly and quarterly data. Besides the models of Ang et al. (2007), we also consider some macroeconomic models for comparison. We compare some Monetary models, Mark-up models, six variants of Phillips curve models, three variants of term structure models, a Random walk model, an AO model, an ARIMA model, and a VAR model. We also compare the forecast ability of these model with three different survey forecasts (the DGBAS, CIER, and TIER surveys).

    We summarized our findings as follows. The best monthly forecasting model for both inflation measures is ARIMA model. For quarterly core-CPI inflation, ARIMA model is also the best model; however, when comparing the quarterly forecasts for CPI inflation, there does not exist the best one. Besides, we also found that the DGBAS survey outperforms all of our forecasting methods/models, but some of our forecasting models are better than the CIER and TIER surveys in terms of MAE.
    Reference: 黃朝熙 (2007), 台灣通貨膨脹預測, 《中央銀行季刊》, 29(1), 5-29.
    侯德潛和徐千婷 (2000), 我國通貨膨脹預測模型之建立, 《中央銀行季刊》, 24(3), 9-40.
    徐士勛、管中閔和羅雅惠 (2005), 以擴散指標為基礎之總體經濟預測, 《臺灣經濟預測與政策》, 36(1), 1-28.
    陳柏琪 (1996), 物價膨脹與失業率之抵換關係-菲力普曲線分析之檢討, 自由中國之工業, 19-28.
    陳宜廷、徐士勛、劉瑞文和莊額嘉 (2011), 經濟成長率預測之評估與更新,《經濟論文叢刊》, 39(1), 1-44.
    葉盛和田慧琦 (2004), 台灣的物價情勢:影響因素探析與計量實證模型應用, 《中央銀行季刊》, 26(4), 69-116.
    Altimari, S. N. (2001), Does Money Lead Inflation in the Euro Area?, working paper.
    Ang, A., G. Bekaert, and M. Wei (2007), Do Macro Variables, Asset Markets, or Surveys Forecast Inflation Better?, Journal of Monetary Economics, 54, 1163-1212.
    Atkeson, A. and L.E. Ohanian (2001), Are Phillips Curves Useful for Forecasting Inflation?, Federal Reserve Bank of Minneapolis Quarterly Review, 25, 2-11.
    Bailliu, J. D. G., M. Kruger, and M. Messmacher (2002), Explaining and Forecasting Inflation in Emerging Markets: the Case of Mexico, working paper.
    Brave, S. and J. D.M. Fisher (2004), In Search of a Robust Inflation Forecast, Federal Reserve Bank of Chicago Economic Perspectives, 28, 12-31.
    Camba-Mendez, G. and D. Rodriguez-Palenzuela (2003), Assessment Criteria for Output Gap Estimates, Economic Modeling, 20, 529-562.
    Canova, F. (2007), G-7 Inflation Forecasts: Random Walk Phillips Curve or What Else?, Macroeconomic Dynamics, 11, 1-30.
    Cecchetti, S. G. (1995), Inflation Indicators and Inflation Policy, NBER Macroeconomics Annual, 189-219.
    Cecchetti, S. G., R. S. Chu, and C. Steindel (2000), The Unreliability of Inflation Indicators, Federal Reserve Bank of New York Current Issues in Economics and Finance, 6, 1-6.
    de Brouwer, G. and N. R. Ericsson (1998), Modelling Inflation in Australia, Journal of Business and Economic Statistics, 16, 433-449.
    Dickey, D. A. and W. A. Fuller (1979), Distribution of the Estimator for Autoregressive Times Series with A Unit root, Journal of the American Statistical Association,
    74(366), 427-431.
    Diebold, F. X. and R. S. Mariano (1995), Comparing Predictive Accuracy, Journal of Business and Economic Statistics, 13, 253-263.
    Domac, Ilker (2003), Explaining and Forecasting Inflation in Turkey, working Paper.
    Engle, R. F. and Granger, C. W. J. (1987), Co-integration and Error Correction: Representation, estimation, and testing, Econometrica, 55, 251-276.
    Fisher, J. D. M., C.T. Liu and R. Zhou (2002), When Can We Forecast Inflation?, Federal Reserve Bank of Chicago Economic Perspectives, 26, 32-44.
    Gerlach, S. and L. E. O. Svensson (2003), Money and Inflation in the Euro Area: A Case for Monetary Indicators?, Journal of Monetary Economics, 50, 1649-1672.
    Gordon, R. J. (1982), Inflation, Flexible Exchange Rates, and the Natural Rate of Unemployment, working paper.
    Granger, C. W. J. and P. Newbold (1974), Spurious Regressions in Econometrics, Journal of Econometrics, 2, 111-120.
    Hallman, J. R. P. and D. Small (1989), M2 Per Unit of Potential GNP as an Anchor for the Price Level, Board of Governors of the Federal Reserve System, Staff Study 157.
    Hallman, J. R. P. and D. Small (1991), Is the Price Level Tied to the M2 Monetary Aggregate in the Long Run?, American Economic Review, 81, 841-858.
    Hodrick, R. J. and E. C. Prescott (1997), Postwar U. S. Business Cycles: An Empirical Investigation, Journal of Money, Credit and Banking, 29, 1-16.
    Jaditz, T. and C. Sayers (1994), Predicting Inflation, working paper.
    Johansen, S. (1988), Statistical Analysis of Cointegration Vectors, Journal of Economic Dynamics and Control, 12, 231-254.
    Jorion, P. and F. S. Mishkin (1991), A Multi-country Comparison of Term Structure Forecasts at Long Horizons, Journal of Financial Economics, 29, 59-80.
    Kool, C. J.M. and J. A. Tatom (1994), The P-Star Model in Five Small Economies, Federal Reserve Bank of ST. Louis, 76, 11-29.
    Litterman, R. (1986), Forecasting with Bayesian Vector Autoregressions, Journal of Business and Economic Statistics, 4, 25-38.
    Mehra, Y. P. (2002), Survey Measures of Expected Inflation-Revisiting the Issues of Predictive Content and Rationality, Economic Quarterly, 88, 17-36.
    Mehra, Y. P. and C. Herrington (2008), On the Sources of Movements in Inflation Expectations: A Few Insights from A VAR Model, Federal Reserve Bank of Richmond Economic Quarterly, 94, 121-146.
    Mishkin, F. S. (1990), What Does the Term Structure Tell Us about Future Inflation?, Journal of Monetary Economics, 25, 77–95.
    Mohanty, M.S. and M. Klau (2001), What Determines Inflation in Emerging Market Countries?, BIS Papers, No. 8.
    Onder, A. Ozlem (2004), Forecasting Inflation in Emerging Markets by Using the Phillips Curve and Alternative Time Series Models, Emerging Markets Finance and Trade, 40, 71–82.
    Phillips, A.W. (1958), The Relationship Between Unemployment and the Rate of Change of Money Wages in the United Kingdom, Economica, 25, 283-299.
    Sims, C. A. (1980), Macroeconomics and Reality, Econometrica, 48, 1-48.
    Stock, J.H. and M.W. Watson (1998), Diffusion Indexes, working paper.
    Stock, J.H. and M.W. Watson (1999), Forecasting Inflation, Journal of Monetary Economics, 44, 293-335.
    Stock, J.H. and M.W. Watson (2003), Forecasting Output and Inflation: The Role of Asset Prices, Journal of Economic Literature, 41, 788-829.
    Stock, J.H. and M.W. Watson (2008), Phillips Curve Inflation Forecasts, working paper.
    Stockton, D. J. and J. E. Glassman (1987), An Evaluation of the Forecast Performanceof Alternative Models of Inflation, The Review of Economics and Statistics, 69, 108-117.
    Description: 碩士
    國立政治大學
    經濟學系
    98258013
    99
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0098258013
    Data Type: thesis
    Appears in Collections:[經濟學系] 學位論文

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