本研究利用共整合模型探討美國貨幣政策、股市、匯市與物價長期均衡關係，並利用非線性平滑轉換誤差修正模型分析是否存在非線性之特質，以有效捕捉到線性模型無法觀察到的短期波動現象。
實證結果發現： 
1、貨幣政策、股市、匯市與物價具長期共整關係，短期動態下非線性平滑轉換誤差修正模型，較誤差修正模型更能捕捉到非線性模型的短期波動現象。
2、短期動態調整行為，在平穩狀態下，前期消費者物價指數變動率、美元指數變動率與本期物價指數變動率為正相關；美國聯邦資金利率變動與消費者物價指數變動率呈負相關；誤差修正項係數為負數，顯示失衡調整力量能拉回至共移均衡。在大幅偏離狀態下，道瓊工業指數變動率、美國聯邦資金利率變動與消費者物價指數變動率為正相關；美元指數變動率與消費者物價指數變動率為負相關；誤差修正項係數為正數，顯示失衡調整力量，短期及長期下皆無法拉回至共移均衡，失衡情況愈加嚴重。
3、平滑轉換誤差修正模型可捕捉到美國股價指數變動率對消費者物價指數變動率，為正及無相關（在不同狀態下）。

This research utilizes the cointegration model to explore the long-run equilibrium relationships of monetary policy, stock market, foreign exchange market to the price level. In order to catch the phenomenon of short run fluctuations, that can't be observed effectively by the linear model, the STECM model (Smooth Transition Error Correction Model) is also been used to analyze if there exist the nonlinear characteristics.
The empirical results find that the macroeconomic variables of monetary policy, stock market and foreign exchange market has long-run cointegration relationships with price level. The STECM Model can catch the phenomenon of short run fluctuations in nonlinear model better than the error correction model under the short run dynamics. About the short run adjustment behavior, in the stable regime, the change’s rate of previous period consumer price index, and of the U.S. dollar index are positively correlated to the change’s rate of consumer price index；the change of the federal funds rate of U.S.A. is negatively correlated to change’s rate of consumer price index；the coefficients of the error correction terms are negative, indicates the disequilibrium adjustment strength can revert the variables back to cointegrated equilibrium. In the severely deviated regime, the change’s rate of the Dow Jones industrial index, and change of the federal funds rate of U.S.A are positively correlated to the change’s rate of consumer price index；the change’s rate of U.S. dollar index is negatively correlated to the change’s rate of consumer price index；the coefficients of the error correction terms are positive, indicating the disequilibrium adjustment strength can not be revert the variables back to cointegrated equilibrium, no matter in the short run or in the long run. The disequilibrium situations are even more serious. The STECM Model can catch that the change’s rate of US stock market index is positively correlated and no correlated to change’s rate of consumer price index in different regimes.

第一章緒論1
第一節研究背景與動機1
第二節研究目的3
第三節研究架構4
第二章文獻探討6
第一節各總體經濟變數與物價的關係6
第二節STECM模型的應用10
第三章研究方法13
第一節研究對象13
第二節單根檢定14
第三節Johansen共整合檢定16
第四節非線性模型19
第四章實證分析26
第一節研究資料26
第二節單根檢定結果	27
第三節共整合檢定結果31
第四節非線性模型檢定結果32
第五章結論40
第一節結論40
第二節建議41
參考文獻42
表2.1各總體經濟變數與物價相關文獻一覽表9
表3.1非線性模型選擇25
表4.1各變數來源資料表26
表4.2基本統計量28
表4.3取對數統計量-差分項28
表4.4單根檢定-原始序列29
表4.5單根檢定-一階差分29
表4.6物價變動率與各總體經濟變數變動（率）AIC、SBC值31
表4.7物價變動率與各總體經濟變數變動(率）之共整向量檢定32
表4.8物價變動率與各總體經濟變數變動（率）誤差修正項非線性檢定33
表4.9非線性模型選擇之檢定結果33
表4.10誤差修正模型係數估計34
表4.11 Exponential平滑轉換誤差修正模型係數估計35
表4.12 ECM對物價變動率模型中控制變數之影響37
圖1.1貨幣供給與物價、產出關聯圖3
圖1.2研究架構流程圖5
圖3.1實證方法之應用流程13
圖4.1a消費者物價指數時間趨勢圖-差分後30
圖4.1b道瓊工業指數時間趨勢圖-差分後30
圖4.1c美元指數時間趨勢圖-差分後30
圖4.1d資金利率時間趨勢圖-差分後30
圖4.1e貨幣供給量時間趨勢圖-差分後30
圖4.2物價變動率非線性轉換變數圖36

王恕含(2003)，殖利率曲線與利率非線性調整之探討，輔仁大學經濟學研究所碩士論文。
江盛全(2004)，失衡指數與台灣景氣循環，輔仁大學經濟學研究所碩士論文。
吳曼華(2002)，台灣的貨幣不確定與通貨膨脹、產出之研究，淡江大學管理科學研究所博士論文。
邵泓道(2007)，預付現金模型加入信用角色之短期通貨膨脹效果，淡江大學經濟學研究所碩士論文。
侯德潛、徐千婷(2002)，我國通貨膨脹預測模型之建立，中央銀行季刋，24卷3期，9-40。
高敏娉(2003)，股價指數與消費者物價指數的關係－台灣與美國的實證研究，中山大學財務管理學系研究所碩士論文。
殷劍峰(2008)，商品市場的金融化與油價泡沫，中國貨幣市場，2008年第11期。
郭保良(1999)，臺灣物價膨脹率的長短期分析與貨幣市場結構，臺灣大學經濟學研究所碩士論文。
楊奕農(2008)，時間序列分析-經濟與財務上之應用，雙葉書廊有限公司出版，初版六刷。
陳文郎(1986)，貨幣與物價-台灣的總體面與個體面之實證研究，臺灣大學經濟學研究所博士論文。
康碧晶(2008)，美國Fed貨幣政策與股債匯市及油價之關聯-分析：以小波轉換之應用，銘傳大學財務金融學系碩士在職專班碩士論文。
張靜芳(2001)，台灣貨幣與物價長期關係的研究，銘傳大學經濟學研究所碩士論文。
鄭育姍(2006)，匯率與總體變數非線性平滑轉換誤差修正模型之實證分析，淡江大學財務金融學系研究所碩士論文。
Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723.
Arango, L.E. and Gonza ́lez, A. (1999), Amonlinear specification of demand for narrow money in colombia, Borradores de Economis, 135.
Atkins, F.J. and Coe, P.J. (2002), An ARDL bounds test of the long-run fisher effect in the United States and Canada, Journal of Macroeconomics, 24(2), 255-266.
Bernanke, B.S. and Blinder A.S. (1992), The federal fund rate and the channels of monetary transmission, American Economic Review, 82(4), 901-921.
Brown, P.A. and Yucel, M.K. (1999), Oil prices and U.S. aggregate economics activity：A question of neutrality, Economics and Financial Review,2,16-24. 
Deflation in america-The greater of two evils, The Economist, 2009, May 9th, 15-16.
Dickey, D.A. and Fuller, W.A. (1979), Distribution of the estimators for autoregressive time series with a unit root, Journal of the American Statistical Association, 74, 427–431.
Dimitris, I., Costas, K., and Andreas, L. (2005), .The relationship between stock market returns and inflation:an econometric investigation using greek data, 
Enst bretagne’s applied stochastic models and data Analysis, 910-916.
Engle, R.F. and Granger, C.W.J. (1987), Cointegration and error correction: Representation, estimation and testing, Econometrics, 55, 251-276.
Fisher I. (1922), The purchasing power of money, New York:Macmillan.
Gonzalo, J. (1994), Five alternative methods of estimaing long-run equilibrium relationships, Journal of Econometrics, 60(1-2), 203-233.
Granger, C.W.J., Yau and Francis, N. (2003), Forecasting business cycles using deviations from long-run economics relationships, Macroeconomic Dynamics, 7, 734-758.
Haug, A.A. and Sikos P.L. (2002), The expectations hypothesis of the term structure: international evidence of non-linear adjustment, Working paper.
Huang, C,J., Lin, C.F. and Cheng J.C. (2001), Evidence on nonlinear error correction in money demand: The case of Taiwan, Applied Economics, 33, 1727-1736.
Johansen, S. and Juselius, K. (1990), Maximum likelihood estimation and inference on cointegration with application to the demand for money, Oxford Bulletin of Economics and Statistic, 169-209.
Johansen, S. (1988), Statistical analysis of cointegration vector, Journal of Economic Dynamics and Control, 12, 231-254.
Johansen, S. (1994), The role of the constant and linear terms in cointegration analysis of nonstationary time series, Econometric Reviews, 13, 205-231.
Krichene, N. (2006), World crude oil market：Monetary policy and the recent oil shock, IMF Working Paper NO. 06/62.
Lu ̈tkepohl, H.T., Tera ̈svirta, T. and Wolters, J. (1999), Investigating stability and linearity of german m1 money demand function, Journal of Applied Econometrics, 14, 511-525. 
Luukkonen, R., Saikkonen, P. and Tera ̈svirta, T. (1988), Testing linearity against smooth transitioon autoregressive models, Biometrika,75, 491-499.
Mcmillan, D.G. (2004), Non-linear error-correction:Evidence for UK interest rates:Manchester School, 72, 626-640.
Mcmillan, D.G. (2005), Smooth-transition error-correction in exchange rates, North American Journal of Economics and finance, 16, 217-232.
Muscatelli, V.A. and Spinelli F. (1996), Modeling monetary trends in Italy using historical data：The demand for broad money 1961-1990, Economic Inquiry, 34(3), 579-596. 
Perman, R. (1991), Cointegration:an introduction to the literature, Journal of economic studies, 18(3), 3-30.
Phillips, P. (1987), Time series regression with a unit root, Econometrica, 55, 277-301.
Phillips, P. and Perron P. (1988), Testing for unit root in time series regression, Biometrika, 75, 335-346.
Said, S. and Dickey, D. (1984 ), Testing for unit roots in autoregressive-moving average model of unknown order, Biometrica, 71, 599-607.
Sargent, T.J., and Wallace, N. (1981), Some unpleasant monetarist arithmetic, Federal Reserve Bank of Minneapolis Quartorly Review, 5(3), 1981, Winter, 1-17.
Schwarz, U.J. (1978), Mathematical-statistical description of the iterative beam removing technique (method CLEAN), Astron. Astrophys, 65, 345-356.
Teräsvirta, T. (1990), Specification, estimation and evaluation of smooth transition autoregressive models, Department of Economics, University of California, SanDiego, Discussion Paper No. 90-39, revised version.
(Terasvirta) ̈, T. (1994), Specification estimation and evaluation of smooth transition autoregressive models, Journal of American Statistic Association, 89, 281-312.
Tera ̈svirta, T., and Anderson, H. (1997), Characterizing nonlinearities in business cycles using smooth transition autoregressive models, Journal of Applied Econometrics, 7, 119-139.
Wolters, J. and Tera ̈svirta, T. and Lu ̈tkepohl, H. (1996), Modelling the demand for m3 in the unified germany, Working Paper Series in Economics and Finance 113, Stockholm School of Economics.
Ying Liu (2001), Modeling mortgage rate changes with a smooth transition error-correction model, Working Papers from Bank of Canada, Document de Travail, No. 23.