本論文內容共一冊，約四萬餘言，共分為五章。 第一章：緒論 由於未來至公元二○○○年經濟成長對電力需求的需要，以目前發電能量，可否配合 的評估，即電力長期負載預測，至為重要，故本文擬以統計的預測方法，根據自民國 五十八年一月至七十四年九月各產業別（分二十五類）月份電力售電量及住宅電燈售 電量時間數列資料，以二元變量時間數列模式，預測未來公元二○○○年各產業別用 電需求，並透過學者專家，大用電戶對能源使用替代性，未來產業結構性變化，技術 進步的先驗知識以問卷方式加以分析，以調適純由資料預測結果所無法反應的前述先 驗知識。 第二章：首先探討一元變量（UNIVARIATE）自我迴歸，移動平均整合模式（ARZ MA） 。第一節：（１）自我迴歸模式（AR）的自我相關函數（ACF ），相關函數（PACF） 。（２）移動平均模式（MA）的ACF 及PACF。（３）自我迴歸移動平均（ARMA）的AC F 及PACF。第二節：當隨機時間數列非平穩型如何經由差分（DIFFERENCING）轉換（ TRANSFORMATION），形成一般化的自我迴歸，移動平均整合模式。第三節：說明要設 計一預測體系，便是要建立一統計模式，建立過程是反覆試行的，其中包括利用 ACF 及PACF確認模式，其次用（１）最大概似估計法（MIE ）（２）有條件最少平方法（ ３）無條件最少平方法（４）非線性估計法。估計模式節參數，再其次是模式的偵測 檢查，最後利用此模式預測未來的觀察值。 第三章：轉換函數分析（TRANSFER FUNCTION ANALYSIS）簡介二變量（LIVARIATE ） 隨機過程。 第一節二變量AR（PROCESS ）設定、估計、預測、轉換函數模式的探討。第二節利用 交叉共變異，相關係數函數確認轉換模式，並作估計偵測，第三節：預測方法的介紹 。 第四章：討論台灣地區各產業別未來至公元二○○○年的展望及未來用電需求成長， 第一節各產業別解釋變數（生產指數GDP ），回顧與預測結果分析。第二節各產業別 未來用電需求預測結果分析，和台電所作長期負載預測報告作比較。第三節用電需求 預測文獻回顧。第五章：結論與建議，並附產業預測結果和成長趨勢圖、問卷表。

由於住宅價格變動毫無預警制度，人民往往憑著個人主觀的判斷而決定何時購屋或售屋，而此種主觀判斷住宅市場利多及利空的觀念，對住宅市場的供需會產生失衡現象，因此是否可從經濟面的訊息找到住宅價格變動的答案，使住宅價格在尚未變動前，政府即已掌握資訊，提前做好穩定住宅價格的因應對策，使民眾依其需要而購屋，則是本研究之主要目的。 　　本研究從文獻中整理出影響住宅價格變動的七個總體經濟變數，這些總體經濟變數包含工資、物價、所得、貨幣供給額、股價、匯率及利率等，並利用向量自我迴歸模式(VAR)進行實證，以便較客觀的獲得變數間的落後期數及暸解變數間雙向、單向及領先、同步、落後情形，且進一步探討住宅價格與每一個總體經濟變數間影響程度大小及影響情形，以釐清各變數之間的關係。 　　本研究利用VAR模型進行住宅價格與總體經濟變數關係的研究，經由實證，得到下列的結論： 　　一、實證結果方面 　　本研究之實證主要有因果關係檢定與分析、變異數分解之分析及衝擊反應之分析三方面，其實證結果如下所述。 　　（一）因果關係檢定與分析 　　由因果關係檢定與分析中，得到股價、物價、匯率、貨幣供給額及利率均能做為住宅價格變動的領先指標。 　　（二）變異數分解之分析 　　由住宅價格之變異數分解中，得知住宅價格自身的解釋程度僅占三分之一，另三分之二被其他的總體經濟變數所解釋，顯示住宅價格受總體經濟變數的影響相當大；而從其他總體經濟變數之變異數分解中，得知住宅價格變動會干擾到總體經濟變數，而使總體經濟變數受干擾而變動變動。 　　（三）衝擊反應之分析 　　從總體經濟變數對住宅價格的衝擊反應分析圖中可以明顯看出除工資外，其餘總體經濟變數變動對住宅價格造成的衝擊均相當明顯，但匯率及利率對住宅價格的衝擊是負向的。 　　住宅價格對所得、股價、匯率及利率的衝擊相當明顯，而其對匯率的衝擊是負向。 　　二、政策應用方面 政府的決策過程中常會有時間落後的現象，而本研究實證的目的則是要使政府能事先掌握住宅價格的變動，並提前做好穩定住宅價格的因應對策，減少政府決策過程的時間落後現象，而實證結果應用至政策方面的內容則由以下說明之。 　　（一）藉由因果關係檢定與分析的實證內容，可以縮短政府對住宅價格不合理變動問題認定落後的時間。 　　（二）從變異數分解之分析的實證內容中，可以使決策者在解決住宅價格問題時，將行動落後的時間減少。 　　（三）由衝擊反應之分析中，可以使政府在執行穩定住宅價格政策時，將衝擊落後的時間縮小。 / Since there is no alarm system in the change of housing prices, people often decide when to buy or when to sell based on personal and subjective judgement. Such concept to judge subjectively whether the housing market is bull or bear will cause unequilibrium in the supply and demend of the housing market. There it is possible to find out the answers to the change of housing prices from economic side so that the government can have enough information and can be prepared in the reaction to stabilizing the housing prices, and so that the public can buy house according to their needs is the main purpose of this project. 　　Seven variables in macroeconomics influencing the change of housing prices have been taken from reative literature, including wage, commodity price, income, money supply, stock price, exchange rate, and interest rate. VAR has been employed to verify so that the more objective lagging period among variable can be known, and the bi-directional, uni-directional, leading, contemporaneous, and lagging situation among variables can be understood. Furthermore, the degree and the status of influence of each macroeconomic variable to the housing price will be investigated to clarify the relations among the variables. 　　The present project investigate the relations between housing price and macroeconomic variables. We have the following findings: 　　I、In Empirical Study: 　　The empirical study in this project includes causal relation test and analysis, the analysis of variable decompositon, and the analysis of impact response. The results are shown in the following: 　　(I) Causality Test and Analysis 　　In the causality test and analysis, we find out that stock price, commodity price, exchange rate, money supply and interest rate all can be the leading indicators in the change of housing prices. 　　(II) The Analysis of Variable Decomposition 　　It is learned from the variable decomposition of housing prices that housing price can only explain one third of the cause in its change, the other two thirds are explained by other macroeconomic variables. It shows that housing prices are subject to the influence of macroeconomic variables greatly. 　　From the variable decomposition of other macroeconomic variables, we know that the change in housing prices will affect macroeconomic variables so that the macroeconomic variables will change. 　　(III) The Analysis of Impact Response 　　It can be obviously seen from the analysis figure of the impact response of the macroeconomics to housing prices, all macroeconomic variables will cause obvious impact to housing prices expect for wage. However, both exchange rate and interest rate have negative impact to housing prices. 　　Housing prices' impact to income, stock prices, exchange rate and interest rate is quite obvious, among which, the impact to exchange rate is negative. 　　II、Policy Application 　　It is a common phenomenon that there often will be lagging in time in government's decision making. The purise of the empirical study in this project is to let the government to know in advance the change of housing prices and to let the government to know in advance the change of housing prices and to let the government be prepared in the reaction of stabilizing the housing prices to minimize the lagging in the decision making process. The contents of application of the empirical study to policy are explained in the following: 　　(I) With the empirical results of the change of the causality test and analysis, the time for the government to recognize the unreasonable changes in housing prices can be shortened. 　　(II) With the empirical results of the analysis of variable decomposition, the decision makers' lagging in the action responding to housing pricescan be minimized. 　　(III) With the analysis in impact response, the lagging in impact will be minimized when the government executing her housing price stabilizing policy.

動態資料往往隨著時間區間取法或測量工具的不同而有差異,此種不確定的特質我們稱為模糊性。但是傳統的時間數列仍是以確定的觀察值來記錄具有模糊性的動態資料。為了更完整的表示一個動態過程,我們考慮模糊時間數列(fuzzy time series)以具有不確定性的模糊集合來取代明確的數值,保持原來的模糊性。 本文探討模糊時間數列中模糊自我迴歸模式(fuzzy autoregressive model簡寫為 FAR 模式)的建構過程,並分別利用此模式來預測中央政府總預算和匯率。FAR 模式乃根據Box-Jenkins(1970)所提出的 ARMA 三階段模式建立的流程並推廣Zadeh(1965)所提出的模糊集合理論而來。在這過程中 ,我們考慮人類思維方法,使FAR 模式更具有彈性且適合未來預測時的需要。而對於所討論的動態過程,也不需要任何模式上的假設(例如:線性或穩定 ),因此 FAR 模式的適用範圍極為廣泛,更不會因為模式的誤判而導致預測時的嚴重錯誤。最後,我們將 FAR 模式的預測結果與傳統 ARMA 模式做比較。 文中關於模糊時間數列的一些性質,例如:模糊趨勢(fuzzy trend)和模糊穩定(fuzzy stationary),由於傳統文獻中沒有加以討論,本文亦提出定義和新的看法。 / Representations of dynamic data are always different as the time interval or measuring tool change. We call these characteristics of uncertainty fuzziness. But traditional time series use crisp observations to record a fuzzy dynamic process. To completely represent, we consider fuzzy time series replacing the crisp numbers with fuzzy sets and preserve original fuzziness. In this paper, the fuzzy autoregressive model (FAR model) of fuzzy time series is studied and used to forecast the Central government expenditure and exchange rates, respectively. The modeling process is according to Box- Jenkins' (1970) method of ARMA model and merged with the fuzzy set theory proposed by Zadeh (1965). Reasonable human judgements and ways of thinking are taken into consideration throughout the modeling process to make the FAR model more elastic and appropriate for forecasting. Unlike certain incorrectly identified models which lead to inaccurate forecasts, the FAR model can be widely applied due to its not having any assumptions on the original time series (e.g., linearity and stationarity). Finally, the performances of the FAR model to Central government expenditure and exchange rates are compared with that of the traditional ARMA model. Additionally, some properties about fuzzy time series, e.g., fuzzy trend and fuzzy stationary, have not been studied in the literature, and we propose definitions and new opinions.