English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113303/144284 (79%)
Visitors : 50814643      Online Users : 529
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/94544


    Title: 台灣失業率的預測-季節性ARIMA與介入模式的比較
    Forecasting Taiwan’s Unemployment Rate –A Comparison Between Seasonal ARIMA and the Intervention Model
    Authors: 胡文傑
    Contributors: 高安邦
    胡文傑
    Keywords: 失業率
    介入模式
    季節性ARIMA模型
    預測
    Unemployment Rate
    Intervention Model
    Seasonal ARIMA Model
    Forecasting
    Date: 2007
    Issue Date: 2016-05-06 16:55:01 (UTC+8)
    Abstract: 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.
    Reference: 1. English section
    Abel, Andrew B. and Ben Bernanke (2001). Macroeconomics. Addison Wesley Press.
    Box, G. E. P., and G. M Jenkins. (1976). Time Series Analysis: Forecasting and control. San Francisco: Holden-Day.
    Dickey, D. A. and W. A. Fuller (1979). “Distribution of Estimators for Autoregressive Time Series with a Unit Root.” Journal of the American Statistical Association, 74, 427-431.
    Edlund, Per-Olov and Sune Karlsson (1993). “Forecasting the Swedish Unemployment Rate: VAR vs. Transfer Function Modelling.” International Journal of Forecasting, 9(1), 61-76.
    Golan, Amos and Jeffrey M. Perloff (2004). “Superior Forecasts of the U.S. Unemployment Rate Using a Nonparametric Method.” Review of Economics and Statistics, 86(1), 433-438.
    Hamilton, James D. (1994). Times Series Analysis. Princeton University Press. Princeton, NJ.
    Joseph Gibaldi (2004). MLA Handbook for Writers of Research Papers. Modern Language Association of America.
    Ljung, G. M., and G. E. P. Box (1978). “On A Measure of Lack of Fit in Time Series Models.” Biometrika, 66, 297-303.
    McGinnis, Harry (1994). “Determining the Impact of Economic Factors on Local Government Growth Policy: Using Time-Series Analysis and Transfer Function Models.” Urban Studies, 31(2), 233-246.
    Montgomery, Alan L, et al (1998). “Forecasting the U.S. Unemployment Rate.” Journal of the American Statistical Association, 93(442), 478-493.
    Montgomery, Douglas, et al (1990). Forecasting and Time Series Analysis. McGraw-Hill Press.
    Moshiri, Saeed and Laura Brown (2004). “Unemployment Variation over the Business Cycles: A Comparison of Forecasting Models.” Journal of Forecasting, 23(7), 497-511.
    Pankratz (1991). Forecasting with Univariate Box-Jenkins Model, John Wiley and Sons, Inc, 562.
    Pelaez, Rolando F (2006). “Using Neural Nets to Forecast the Unemployment Rate.” Business Economics, 41(1), 37-44.
    Rothman, Philip (1998). “Forecasting Asymmetric Unemployment Rates.” Review of Economics and Statistic, 80(1), 164-168.
    Stevenson, Max and Maurice Peat (2000). “Determining the Impact of Economic Factors on Local Government Growth Policy: Using Time-Series Analysis and Transfer Function Models.” Australian Journal of Labour Economics, 4(1), 41-55.
    Tsay, R. S. and G. C. Tiao (1984). “Consistent Estimates of Autoregressive Parameters and Extended Sample Autocorrelation Function for Stationary and Nonstationary ARMA Models.” Journal of American Statistical Association, 79, 84-96.
    Vandaele, Walter (1983). “Applied Time Series and Box-Jenkins Models,” 94, 103.
    Wei, William W. S. (2006). Time Series Analysis. Addison Wesley Press.
    Wilson, Patrick J. and L. J. Perry (2004). “Forecasting Australian Unemployment Rates Using Spectral Analysis.” Australian Journal of Labour Economics, 7(4), 459-480.
    2. Chinese section
    自由電子報2002年4月21日,「六年國建 學者支持中有批評」,http://www.libertytimes.com.tw/2002/new/apl/21/today-e4.htm。
    行政院主計處全球資訊網 http://www.dgbas.gov.tw/mp.asp?mp=1。
    行政院勞委會全球資訊網 http://www.cla.gov.tw/。
    何金泉(2006),「台灣地區各縣市失業率追蹤研究- Panel實證分析」,國立中正大學國際經濟所碩士論文。
    葉小蓁(1998),《時間序列分析與應用》,台北:葉小蓁。
    國史館全球資訊網 http://www.drnh.gov.tw/。
    陳依鋒(2000),「台灣地區失業率之預測分析」,國立政治大學統計研究所碩士論文。
    國家政策研究基金會 http://www.npf.org.tw /。
    陳雅玫(1992),「台灣地區失業率之影響因素評估」,國立政治大學統計研究所碩士論文。
    陳雯倩(1997),「台灣地區失業率之影響因素評估」,國立成功大學統計學研究所碩士論文。
    張清溪、許嘉棟、劉鶯釧、吳聰敏(2004),《經濟學理論與實際》,台北:翰蘆圖書。
    賴景昌(2004),《總體經濟學》,台北:雙葉書廊。
    Description: 碩士
    國立政治大學
    經濟學系
    94258023
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0094258023
    Data Type: thesis
    Appears in Collections:[經濟學系] 學位論文

    Files in This Item:

    File SizeFormat
    index.html0KbHTML2312View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback