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題名：台灣地區汽車持有需求預測之研究
作者：汪志忠 引用關係
作者(外文)：Chi-Chung Wang
校院名稱：國立成功大學
系所名稱：交通管理學系碩博士班
指導教授：黃國平
學位類別：博士
出版日期：2007
主題關鍵詞：汽車持有需求；聯立方程模式；ARMAX；永續發展；sustainable development；automobile demand；ARMAX；simultaneous-equations model
原始連結：連回原系統網址new window
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汽車是石油能源消耗與移動污染源中排放二氧化碳的主要來源，也是運輸部門推動二氧化碳排放減量政策的主要對象。汽車總量事實上決定了污染程度，台灣地區汽車的持有逐年成長，二氧化碳排放問題日益嚴重。因此，就能源消耗與二氧化碳排放污染問題，由於台灣目前的機動車輛持有狀態已隨著所得之提高漸漸成為一種必需品，因此儘早掌握汽車的成長趨勢有其必要性。
本論文主要研究目的在於建立汽車持有需求之動態預測模型，首先考慮汽車持有需求此一依變數的時間記憶特性，建立納入解釋變數之ARMAX 模式；其次考慮汽車持有需求與價格變數可能同時被決定的特性，建立聯立方程模式；最後進一步考慮汽車與機車之間可能的替代關係，建立汽、機車需求與價格變數之聯立方程模式。
本研究收集的資料包括1981-2006年間台灣地區的汽車持有登記(AUTO)、機車持有登記(MOTOR)，以及總體經濟變數如交通類別之消費者物價指數(TCCPI)、人口數(POP)、就業人口數(EM)、家戶數(HOUSE)、實質國民生產毛額(GDP)、國際原油價格(OILP)等年資料以及1990-2006年間的季資料，同時以三項虛擬變數反應1999與1987年的兩次牌照管理政策與2001年台灣加入WTO事件。然而對於年資料與季資料變數的穩定性質，ADF與KPSS單根檢定分析結果顯示：季資料變數經過自然對數轉換後為不穩定，而年資料變數經過自然對數轉換則為穩定，因此在實證模式建立將係以年資料變數作為樣本。
在總體經濟變數對於汽車持有需求的領先關係上，根據 Granger因果關係檢定結果，顯示在5%的顯著水準下，交通類別之消費者物價指數(TCCPI)、人口數(POP)、就業人口數(EM)、家戶數(HOUSE)、實質國民生產毛額(GDP)、國際原油價格(OILP)的自然對數對於汽車持有需求的自然對數lnAUTO無Granger因果關係存在，不是汽車持有需求的的自然對數lnAUTO的領先指標。
模式估計結果顯示ARMA(1,1)模式為本研究 數列的最佳ARMA模式型態，而包含解釋變數的ARMAX(1,1)模式，估計結果顯示lnTCCPI、lnGDP與虛擬變數D1為ARMAX(1,1)模式的三項顯著的解釋變數，其中lnTCCPI與lnGDP的顯著之水準為1%，D1的顯著之水準為10%；在聯立方程模式A，經以3SLS法估計，對於 模式，落後依變數 為顯著，而顯著的解釋變數則為實質國民生產毛額的自然對數 以及虛擬變數D1。對於 模式而言，顯著的解釋變數為國際原油價格的自然對數 與的匯率的自然對數 ；聯立方程模式B主要是進一步考慮汽車持有需求與機車持有需求之間可能存在的替代性關係，因此不同於聯立方程模式A之處即在於聯立方程系統中再加入機車持有需求方程式以反映此種可能存在的替代關係。經以3SLS法估計，結果顯示對於 模式，另一依變數 為顯著，落後依變數 為顯著，而顯著的解釋變數則為虛擬變數D1；對於 模式，另一依變數 為顯著，落後依變數 為顯著，而顯著的解釋變數則為虛擬變數D2；而對於 模式，顯著的解釋變數為實質國民生產毛額的自然對數 與國際原油價格的自然對數 。
在模式評估方面，本研究使用平均絕對誤差(MAE)、根均方誤差(RMSE)、平均絕對百分比誤差(MAPE)與根均方百分比誤差(RMSPE) 四項評估指標進行模式評估，並分別以全樣本(1981-2006)與部分樣本(1981-2004) 的方式進行模式配適度評估與預測精確度評估。在全樣本(1981-2006)配適度評估部分，ARMAX(1,1)之配適度是最佳的，其次為聯立方程模式A；在部分樣本(1981-2004)配適度與預測精確度評估部分， ARMAX(1,1)之配適度仍是最佳的，其次為聯立方程模式A；但在樣本外的預測精確度方面，則以聯立方程模式A的精確度最高，ARMAX(1,1)模式次之。最後本研究認為由於ARMAX(1,1)的樣本外預測偏離度非常高，故在預測應用模式的選擇上，認為應用聯立方程模式A將較為適當與合理。
最後，情境預測結果指出，台灣地區如同其他開發中國家的汽車持有需求現象，經濟成長是造成汽車持有需求年平均成長率增加的主要因素，也就是汽車持有需求多由經濟成長所驅動，若每年經濟成長率增加1%時，汽車持有需求年平均成長率約增加0.53%。另一方面，原油價格與汽車持有需求年平均成長率則呈現反向關係，高油價訊息會反應於汽車持有需求成長上，模擬結果發現，當油價每年成長1%時，汽車持有需求年平均成長率約可減少至0.05%，也證實了原油價格與汽車持有需求年平均成長率之間的反向影響程度。
本研究預測台灣未來2007-2015年可能遭遇的汽車持有需求的成長現象，而為朝向永續發展的目標，本研究成果將有助於運輸部門及早思考規劃台灣地區的汽車持有的運輸管理政策，能源部門則可藉此規劃能源消費需求與二氧化碳排放評估等議題。
The dynamic characteristics of automobile demand are critical for national economic and energy prediction. Although forecasting automobile demand has been previously investigated, it has not been within such a dynamic simulation framework in Taiwan. In this study, dynamic automobile demand models are investigated. An ARMAX model and two simultaneous-equations models are constructed and evaluated, using 1981-2006 annual data.
Automobile registration data is used and several economic variables including real GDP, population, employment, households, world crude oil prices, exchange rate, transportation and communication CPI and motorcycle registration data are considered. Besides, variables’ stationarity is examined by the ADF and KPSS unit root tests to avoid spurious regression.
Estimated results of ARMAX model show that quantity of automobile demand can be explained by the real GDP and Transportation and communication CPI (TCCPI), a first-order autoregressive and a stochastic movingaverage filter at lag 1.
Results of the first simultaneous-equations model (two endogenous variables, automobile demand and TCCPI) indicate that quantity of automobile demand is explained by the lagged dependent variable and the real GDP. Another endogenous variable TCCPI is explained by the world crude oil price and the exchange rate.
Results of the second simultaneous-equations model (three endogenous variables, automobile demand, motorcycle demand and TCCPI) indicate that quantity of automobile demand may be explained by the lagged dependent variable and the quantity of motorcycle demand. There is substitution relationship between automobile and motorcycle demand.
Evaluation results indicate that there is excellent model fitness for ARMAX and two simultaneous-equations models in the sample period 1981-2006. For the application of forecasting, this study considers the out-of-sample prediction accuracy. Results show that first simultaneous-equations model has the best out-of-sample prediction performance. Finally, through this model, the future quantity of automobile is forecasted and a number of simulation experiments considering various oil price and economic growth scenarios for 2007-2015 are demonstrated.
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