本文研究台灣貨幣需求結構改變，我們研究「變數」值(Piecewise in Variable)的結構轉折而非「時間」值(Piecewise in Time)，因為轉折點只是轉折區間的特例，所以本文建立一「變數模糊時間序列」(Fuzzy Time Series in Variable)模型來探討「變數的轉折區間」，相較於傳統時間序列研究方法如：時間序列模型、門檻轉折點模型與模糊時間序列模型等，本文所建立的變數模糊時間序列模型，所求取的股價轉折區間，不僅可改善對稱模型殘差項的非隨機現象，同時也改善了門檻轉折模型之轉折點股價指數太低的現象，並且有效地將轉折點變更為較一般化的轉折區間，足見本文所提出變數模糊時間序列模型在結構轉折的偵測上具有相對優勢，詳述如下： （一）、相較於對稱模型方面：變數模糊時間序列模型可避免對稱模型估計貨幣需求函數所產生的偏差，並且有效改善其殘差項具有非白噪音現象。 （二）、相較於門檻轉折模型方面：1.變數模糊時間序列模型較能有效驗證以下假說：貨幣需求的股價指數彈性在高股價區時較大，以及貨幣需求的所得彈性在高股價區時較小。2.變數模糊時間序列模型所求出的股價指數轉折區間水準值，對央行目前及未來貨幣政策較具實用性，3. 變數模糊時間序列模型再預測貨幣需求時，未如門檻轉折模型產生高估的偏誤。 （三）、相較於傳統模糊時間序列模型方面：變數模糊時間序列模型已改善傳統模糊時間序列模型的結構轉折區間太長之不合理現象。 （四）、相較於以「時間」為轉折的傳統時間序列模型方面：當貨幣需求函數的重要解釋變數在短時間持續發生較大幅度變化時，傳統時間序列模型可能無法診斷出結構轉變的缺失，本文的變數模糊時間序列模型可避免此一缺失。 （五）、在政策的應用上： 1. 中央銀行若未將資料，區分高低股價指數來分段估計貨幣需求函數，將使貨幣需求的所得彈性抑或是狹義貨幣需求的股價指數彈性的估計，產生頗大的偏誤。 2. 經建會在計算台灣地區的景氣對策信號中，其金融面指標同時包括有M1B貨幣供給的增加率與股價指數變動率，如此將造成在高股價指數下，股價指數上揚時高估了台灣地區的景氣狀況，而在股價指數下降時，則反之。 另外，由於台灣欠缺貨幣需求函數的重要解釋變數「所得」的月資料，以往文獻以工業生產指數等為替代變數以估計月貨幣需求函數，本文不僅證明這些方法的缺失，並提出「模糊距離權數法」來估計出月國內生產毛額資料，此一資料不僅可避免月工業生產指數等方法的三項缺失，而且在貨幣需求的估計上與預測上均有較佳的表現。 / Whether the ”money demand function” makes “structural change” happened or not ,that is crucial research for the monetary theory field. Therefore, many foreign and domestic papers have ever made studies on this. There have two major methods of study structural change. The first method is piecewise in time that is so popular and so many lecture study by it e.g. Juda and Scadding(1982), Shen(1999) ,Lin and Huang(1999),etc . Tsay(1989) had proposed a new methoed that is piecewise in variable . Distinct situation is suitable in using the two methods .We have two reasons to use the new method to study the structural change of Taiwan’s money demand function. First one is that Friedman(1988,Paul（1992）,Wu and Shea（1993）and Shen(1996) find the trade-volume of stock market or stock price are the important factors of money demand function. TSE is 12495 in February of 1990 and 2573 in October of 1990. TSE is changing so huge but all the Papers of piecewise in time can’t detect the structural change of Taiwan money demand. The second reason is that to detect the ” interval of financial variable” of structural change of Taiwan money demand is more benefit to the Central Bank than to detect the ” past time point” of structural change.　To detect the ” interval of financial variable” of structural change of Taiwan money demand is much convenient matters for monetary policy of Center Bank from now and future. Our research propose “fuzzy time series in variable” try to find the ” smoothing interval of financial variable” of structural change of money demand . Our method has two major benefits as follow: 1. Difference to TAR model: The TAR model find out the ” point of financial variable” of structural change. It seems metaphorically money demand function’s structural change suddenly. Our method find out the ” interval of financial variable” of structural change .It’s more reasonable that structural change of money demand function is gradually. 2. Difference to STAR model: So many STAR（Smooth Transition Autoregressive ）papers also find out the Gradual Transition Interval .For example: Terasvirta and Anderson(1992), Sarantis(1999) etc. But those lectures have the following point on why our method can improve it (a).STAR is piecewise in time. (b). STAR investigate structural change by just one variable AR process. But economists concern the structural change of variables. (c). The power of STAR to detect structural change is too weak. 3. We propose new summation average entropy formula that can improve the interval of structural change too longer.

時間序列是用來預測未來趨勢的一種重要技術，然而在實務上建構時間序列模型時，參數很難有效估計。原因可能來自於時間序列本身的模糊性質，而導致參數的不確定性使得預測結果產生極大誤差。如果將參數模糊化引進時間序列的模型中，往往過於複雜。本論文提出相對移動率為新的模糊時間序列建構方法，讓原本具有模糊性質的時間序列經由反模糊化(defuzzification)後，以點估計的方式估計起始中心點，經由適當的修正調整為較佳的中心點以及半徑，建立有效的區間時間序列。並將相對移動率引進門檻自廻規模型中，取代原有之門檻值設定，並建立區間時間序列。最後，我們使用台灣加權股價指數為例，以本論文所提出之方法進行區間預測及效率評估。 / The time series is an important technology that is used to predict future trends, however in the real world, parameter is difficult to estimate effectively when we construct a time series model due to the of the fuzzy property of the times series data. The estimated parameters in the time series will cause a big error due to the uncertainty of fuzzy data. It is too complex to introduce the fuzzy parameters into the time series model. In this thesis, we propose relative moving ratio as a new criteria in constructing procedure of an interval time series. We defuzzify a fuzzy data and use point estimation to obtain an initial center, then we adjust the center and radius making it more appropriately. The resulting center and radius is then become an interval time series that can be use to forecast an interval data. We also apply relative moving ratio in threshold autoregressive models by replacing the threshold in constructing interval time series. Finally, in empirical studies chapter, we use Taiwan weighted Stock Index as examples to evaluate the performance of the proposed two methods in building the interval time series.