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政大機構典藏 > 商學院 > 統計學系 > 學位論文 > Item 140.119/36655

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/36655

Title:	台灣地區失業率之預測分析 Preditive Analysis of Unemployment Rate in Taiwan
Authors:	陳依鋒 Chen, Yi-Feng
Contributors:	鄭天澤毛維凌 Jeng, T. T. Mao, Wei-Lin 陳依鋒 Chen, Yi-Feng
Keywords:	失業率 ARIMA模型轉換函數模型向量自迴歸模型共整合多步預測一步預測泰爾不等係數 unemployment rate ARIMA model transfer function model VAR cointegration multi-step forecasting one-step forecasting THEIL MAPE RMSPE MPE
Date:	1999
Issue Date:	2009-09-18 19:08:20 (UTC+8)
Abstract:	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.
Reference:	1. 陳雅玫(1992)對台灣地區的失業率，以時空數列模型描述地區本身及地區與地區之間的時空動態關係。 2. Funke(1992)針對德國的失業率使用單變量ARIMA模型及轉換函數模型兩種預測模型。 3. 施東河(1995)利用倒傳遞神經網路來預測台灣地區的失業率。 4. 江政益(1997)利用倒傳遞類神經網路模型針對台北市失業率資料進行預測；並建立一預測值之信賴區間。 5. 陳雯倩(1997)針對性別、教育等類別，分別以統計模型評估台灣地區失業率之影響因素。
Description:	碩士國立政治大學統計研究所 87354019 88
Source URI:	http://thesis.lib.nccu.edu.tw/record/#A2002001938
Data Type:	thesis
Appears in Collections:	[統計學系] 學位論文

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