2009年至2011年，倫敦金屬交易所(LME)銅價出現大幅上升行情，LME銅價自3,260美元/噸，於2011年一月達到9,532美元/噸，漲幅92%，銅價逐漸上揚，隨著世界經濟的復甦，全球銅市場再度擁有上揚的推力，創下歷史的新高，且有挑戰更高價格的趨勢。 目前銅箔基層板為印刷電路板之基礎材料，被廣泛運用在民生家電、電腦、通訊、醫療、軍事用途。而所需要使用的銅原料都必須仰賴進口，因而企業在面臨強大的競爭壓力下，要如何避免銅價的波動所帶來的威脅，就成為企業重要的課題之一。目前國內尚未有對於銅價避險之研究，本研究將透過避險策略，以提供給企業對於銅原料成本管理擬定避險策略之參考。 本研究以LME銅價以及上海期貨交易所(SHEF)銅價作為資料分析的基礎。利用ARIMA模型判斷LME及SHEF銅價最適合之模型，研究結果顯示LME銅價為ARIMA(4,1,4)，SHEF銅價為(2,1,2)，在以LME銅價預測模型及避險策略判斷何時適用哪種避險方法，實證結果顯示： 1. 若預測未來趨勢為盤整階段，適用Collar、Leveraged TARN Swap、Leveraged Range Swap及Leveraged Knockout Forward。 2. 若預測未來趨勢為小跌，適用Fixed Swap、Extendible Fixed Swap、Leveraged TARN Swap、Leveraged Range Swap及Leveraged Knockout Forward。 3. 若預測未來趨勢為大跌，適用Fixed Swap及Extendible Fixed Swap。

本論文採用了由Box and Jenkins(1976)所提出的ARIMA模型，以及由BOX and Tiao(1975)所提出的Intervention Model，去配適台灣的失業率型態，以及比較其預測的結果。 結果顯示出台灣的失業率具有季節性的型態，亦即台灣的失業率並非僅僅受到月分之間的相關，年分之間也有所關連。是故，當本論文在預測失業率的水準時，也考慮到此一因素，加入季節性的ARIMA模型對台灣的失業率加以預測。另外，時間序列的資料常常受到外生因素的干擾。對於失業率來說，政策上的改變將會影響失業率本身的結構，因此利用介入模式預測失業率，可以得到一組較精確的預測值。介入模式的事件有以下五個，分別是解嚴、六年國建、台灣引進外勞、中共飛彈試射、新十大建設。前四個事件的確影響了失業率的結構，不過第五項，也就是新十大建設並沒有顯著影響失業率的結構。理由可能是新十大建設的內容並不能合宜的解決經濟上與社會上的問題，以及這些建設尚未完工，以致無法達到期預期的效果。 比較兩模型的預測結果時，採用了MPE、MSE、MAE、MAPE作為模型評估的準則，結果指出介入模式的預測結果比起季節性ARIMA的預測結果來的有效率。 / This article adopts the ARIMA model, which was first introduced by Box and Jenkins (1976), and the intervention model, which was developed by Box and Tiao (1975), to fit the time series data for the unemployment rate in Taiwan, and thus to compare the results of the forecasts. The results reveal that there is a seasonal effect in the data on the unemployment rate. This indicates that the unemployment rate figures are not only related from month to month but are also related from year to year. When forecasting the level of unemployment, we should examine not only the neighboring months but also the corresponding months in the previous year. Time series are frequently affected by certain external events. In the discussion on the unemployment rate, the policies implemented by the government as well as military threats indeed influence the structure of the series. By making a forecast using the intervention model, we can evaluate the effect of the external events which would give rise to more accurate forecasts. In this study, there were five interventions included in relation to the unemployment rate series, which were as follows. First, the lifting of Martial Law in February 1987. Second, the Six-year National Development Plan launched in June 1991. Third, the hiring of foreign labor in Taiwan, which took effect in October 1991. Fourth, the threats of missile tests from the PRC in Feb 1996. Fifth, the ten new construction programs launched in November 2003. The first four events were indeed found to give rise to a structural change in the unemployment rate series at the moment when they occurred. This result might also have implied that not all of the actual effect of expansionary policies could have exactly decreased the unemployment rate, and therefore have solved the economic and social problems simultaneously. When we refer to the comparison of the above two models, the ultimate choice of a model may depend on its goodness of fit, such as the residual mean square, AIC, or BIC. As the main purpose of this study is to forecast future values, the alternative criteria for model selection can be based on forecast errors. The comparison is based on statistics such as MPE, MSE, MAE and MAPE. The results indicate that the intervention model outperforms the seasonal ARIMA model.

本文採用Box and Jenkins（1976）所提出的ARIMA模型來進行時間序列資料的配適，並引入轉換函數模型，以政府高教支出、就業人數與固定資本形成毛額為輸入變數，國民生產毛額為輸出變數。 實證結果發現，政府高等教育支出在落後5至6期之後，對於經濟成長會有比較顯著的正面效果。可以解釋為目前一般大專院校畢業生需要一至兩年的時間，以適應職場環境，因此以一般大學的4年修業期間來看，再加上社會新鮮人的適應期，則為6年後，政府對高等教育的投資始會對經濟產生助益。而除了政府高等教育支出可作為經濟成長的領先指標外，當期的經濟表現亦受到前一期經濟表現的影響，有顯著正相關。並且在檢視過轉換函數模型的正確性之後，預測未來5期的國民生產毛額仍是呈現一個上升的趨勢。因此，為促進經濟成長，政府對於高等教育的投資是可以思考的方向之一。然而，必須注意的是高等教育體系的投資，會有一段的時間落後。因此，如要提升國家競爭力，對於高等教育的投資，不但不能荒廢，更應該有長久的規劃。

近年來由於亞洲金融風暴的肆虐，產生經濟不景氣，使得失業的問題逐漸受到社會所關注，本論文企圖以三個時間序列方法：1.單變量ARIMA模型；2.轉換函數(TF)模型；3.向量自迴歸(VAR)模型來建立台灣地區的失業率時間序列預測模型。資料則是利用台灣地區民國75年1月至民國87年12月的失業率月資料作實證預測分析，為了知道資料是否來自時間趨勢模型，測試是否經過差分消掉一部份的記憶會發生預測的誤差，所以先以多步(multi-step)預測和一步(one-step)預測的方法計算出民國88年1月至88年12月預測值，而預測評估準則則採用(1)MAPE、RMSPE、MPE及泰爾不等係數(THEIL)；(2)變化方向誤差與趨勢變化誤差兩大方向來做預測比較。最後將算出的12期預測值與行政院主計處整體統計資料庫中所得到的失業率實際值利用預測評估準則做比較，結果發現一步預測法較多步預測法準確；而向量自迴歸模型(VAR)在大部份的預測期數上有較小的MAPE、RMSPE、MPE及THEIL值，因為此VAR模型考慮了在變數之間的共整合現象，有助於模型的預測，所以有較好預測的能力；反而是較複雜的ARIMA模型及轉換模型預測能力稍差一點。 / In this thesis, we plan to construct three time series models to forecast the Taiwan unemployment Rate. These time series models are ARIMA model、transfer function (TF) model and Vector Autoregressive (VAR) model. The data set consists of monthly observations for the period 75:1-87:12 for unemployment rate. We want to know if the data came from time trend model. First, we use multi-step forecasting and one-step forecasting to calculate 12 forecasted values from 88:01-88:12. Then We compare the prediction performance of these two methods by using：(1) MAPE、RMSPE、MPE and Theil’s Inequality Coefficient (THEIL)；(2) Direction of Change Error and trend Change Error etc. It is found that one-step forecasting is more correct than multi-step forecasting and the forecasting performance of VAR model is improved by explicitly taking account of cointegration between the variables in the model，so VAR model has lower MAPE、RMSPE、MPE and THEIL for most horizons. However，the more parsimonious ARIMA and transfer function models have higher MAPE、RMSPE、MPE for most horizons.

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.