1. NCCU Academic Hub
  2. Publications
  3. College of Commerce
  4. Theses
  5. Item

Publications-Theses

Article View/Open

Publication Export

Google ScholarTM

NCCU Library

Citation Infomation

Related Publications in TAIR

題名 台灣地區總人口數之預測分析
作者 邱惟俊
貢獻者 鄭天澤
邱惟俊
關鍵詞 介入模式
時間數列迴歸
轉換函數模式
指數平滑法
總人口數
育齡婦女總生育率
粗出生率
粗死亡率
日期 1998
上傳時間 21-Apr-2016 09:55:10 (UTC+8)
摘要 人口政策是政府的重要政策之一，而總人口數則是政府制定政治、經濟、社會及文化發展計畫之主要參考依據，因此如何準確地預測未來的總人口數就成為政府相關部門重要的課題。
In this thesis, we plan to construct various time series models for the total population in Taiwan. The following time series models are considered: ARIMA intervention model, time series regression model, transfers founction intervention model and exponential smoothing method. The input variable considered in the transfer function intervention model include total fertility rate, crude birth rate and crude death rate. We also compare the prediction performance of these models by using mean absolute percentage error (MAPE) and root mean square percentage error (RNSPE). It turns out that the transfer function intervention model with total fertility rate as input is the best model. While the transfer function intervention model with crude birth rate as input ranks the second best. Finally we forecast the total population of the next ten years by using the above two best models and compare with the middle population projection by Manpower Planning Department in Executive YUAN-Council for Economic Planning and Development. The mean absolute percentage error are 0.138% and 0.165% respectively. This result justifies that the time series model has excellent predictive ability and should be considered for total population prediction.
參考文獻 『參考文獻』
[1] 內政部統計處，1998，生命表編算方法改進報告，行政院八十七年研考會經費補助案67-69頁。
[2] 吳柏林與廖敏治，1992，台灣地區結婚率、出生率、人口成長率的時間數列模式探討，人口學刊，第十四期，109-132頁。
[3] 林茂文，1992，時間數列分析與預測，台北：華泰書局。
[4] 鄭天澤與李旭煌，1995，台灣地區出國觀光旅客需求預測模式之比較分析，國立政治大學學報，第七十一期，179-210頁。
[5] 行政院經濟建設委員會人力規劃處，1999，中華民國台灣地區民國87年至140年人口推計。
[6] Ahlburg, D. A. (1992), “Population forecasting：Guest Editors’introduction”, International Journal of Forecasting, 8, 289-299.
[7] Akaike, H. (1973), “Information Theory and an Extension of the Maximum Likelihood Principle”, in Proceedings of the 2nd
International Symposium on Information Theory, 267-281.
[8] Bowerman, B. L., and R. T. O’Connell, (1993), Forecasting and Time Series：An Applied Approach, 3rd ed. Boston：Duxbury.
[9] Box, G. E. P. and G. M. Jenkins, (1994), Time Series Analysis, Forecasting and Control, 3rd ed. San Francisco：Holden-Day.
[10] Carter, L. R. (1996), “Forrecasting U.S. Mortality：A Comparison of Box-Jenkins ARIMA and Structural Time Series Models”, The Sociological Quarterly, 37, 127-144.
[11] El-Attar, S. (1988), “Population Forecasting：An Application of the Box-Jenkins Technique”, American Statistical Association, Proceedings of the Social Statistics Section, 305-310.
[12] Kashyap, R. L. and A. R. Rao, (1976), Dynamic Stochastic Models from Empirical Data, Academic Press, New York, San Francisco, London.
[13] Lee, R. D. (1992), “Stochastic Demographic Forecasting”,
International Journal of Forecasting, 8, 315-327.
[14] Lee, R. D. (1993), “Modeling and Forecasting the Time Series of US Fertility：Age Distribution, Range, and Ultimate Level”,International Journal of Forecasting, 9, 187-202.
[15] Lee, R. D., and Tuljapurkar, S. D. (1994), “Stochastic
Population Forecasts for the United States：Beyond High, Medium, and Low”, Journal of American Statistical Association, 89, 1175-1189.
[16] Pflaumer, P. (1992), “Forecasting US Population Totals with the Box-Jenkins Approach”, International Journal of Forecasting, 8, 329-338.
[17] Schwartz, G. (1978), “Estimating the Dimension of Model”, The Annals of Statistics, 6, 461-464.
[18] Voss, P. R. and C. D. Palit, (1981), “Forecasting State Population Using ARIMA Time Series Techniques”, Technical Series 70-6, University of Wisconsin-Madison, WI.
描述 碩士
國立政治大學
統計學系
86354007
資料來源 http://thesis.lib.nccu.edu.tw/record/#B2002001564
資料類型 thesis
dc.contributor.advisor 鄭天澤zh_TW
dc.contributor.author (Authors) 邱惟俊zh_TW
dc.creator (作者) 邱惟俊zh_TW
dc.date (日期) 1998en_US
dc.date.accessioned 21-Apr-2016 09:55:10 (UTC+8)-
dc.date.available 21-Apr-2016 09:55:10 (UTC+8)-
dc.date.issued (上傳時間) 21-Apr-2016 09:55:10 (UTC+8)-
dc.identifier (Other Identifiers) B2002001564en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/85893-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 統計學系zh_TW
dc.description (描述) 86354007zh_TW
dc.description.abstract (摘要) 人口政策是政府的重要政策之一，而總人口數則是政府制定政治、經濟、社會及文化發展計畫之主要參考依據，因此如何準確地預測未來的總人口數就成為政府相關部門重要的課題。zh_TW
dc.description.abstract (摘要) In this thesis, we plan to construct various time series models for the total population in Taiwan. The following time series models are considered: ARIMA intervention model, time series regression model, transfers founction intervention model and exponential smoothing method. The input variable considered in the transfer function intervention model include total fertility rate, crude birth rate and crude death rate. We also compare the prediction performance of these models by using mean absolute percentage error (MAPE) and root mean square percentage error (RNSPE). It turns out that the transfer function intervention model with total fertility rate as input is the best model. While the transfer function intervention model with crude birth rate as input ranks the second best. Finally we forecast the total population of the next ten years by using the above two best models and compare with the middle population projection by Manpower Planning Department in Executive YUAN-Council for Economic Planning and Development. The mean absolute percentage error are 0.138% and 0.165% respectively. This result justifies that the time series model has excellent predictive ability and should be considered for total population prediction.en_US
dc.description.tableofcontents 目 錄
謝辭 -----------------------------------------------i
目錄 -----------------------------------------------ii
圖表目次 -------------------------------------------iii
中文摘要 -------------------------------------------v
英文摘要 -------------------------------------------vi
第一章：緒論 ---------------------------------------1
1-1 前言 ---------------------------------------1
1-2 文獻回顧 ----------------------------------- 3
1-3 研究方法摘要與章節結構 --------------------- 3
第二章：模式理論架構 ------------------------------- 5
2-1 ARIMA 介入模式 ----------------------------- 5
2-2 時間數列迴歸模式 --------------------------- 6
2-3 指數平滑法 --------------------------------- 7
2-4 轉換函數模式 ------------------------------- 8
2-5 模式選取準則 -------------------------------11
2-6 預測評估準則 -------------------------------12
第三章：實證分析 -----------------------------------13
3-1 資料來源與說明 -----------------------------13
3-2 ARIMA 介入模式 -----------------------------15
3-3 時間數列迴歸模式 ---------------------------18
3-4 指數平滑法 ---------------------------------20
3-5 轉換函數模式 -------------------------------23
第四章：結論 ---------------------------------------32
參考文獻 -------------------------------------------35
附錄一：人口統計資料 -------------------------------37
附錄二：統計圖表 -----------------------------------38		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#B2002001564en_US
dc.subject (關鍵詞) 介入模式zh_TW
dc.subject (關鍵詞) 時間數列迴歸zh_TW
dc.subject (關鍵詞) 轉換函數模式zh_TW
dc.subject (關鍵詞) 指數平滑法zh_TW
dc.subject (關鍵詞) 總人口數zh_TW
dc.subject (關鍵詞) 育齡婦女總生育率zh_TW
dc.subject (關鍵詞) 粗出生率zh_TW
dc.subject (關鍵詞) 粗死亡率zh_TW
dc.title (題名) 台灣地區總人口數之預測分析zh_TW
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 『參考文獻』
[1] 內政部統計處，1998，生命表編算方法改進報告，行政院八十七年研考會經費補助案67-69頁。
[2] 吳柏林與廖敏治，1992，台灣地區結婚率、出生率、人口成長率的時間數列模式探討，人口學刊，第十四期，109-132頁。
[3] 林茂文，1992，時間數列分析與預測，台北：華泰書局。
[4] 鄭天澤與李旭煌，1995，台灣地區出國觀光旅客需求預測模式之比較分析，國立政治大學學報，第七十一期，179-210頁。
[5] 行政院經濟建設委員會人力規劃處，1999，中華民國台灣地區民國87年至140年人口推計。
[6] Ahlburg, D. A. (1992), “Population forecasting：Guest Editors’introduction”, International Journal of Forecasting, 8, 289-299.
[7] Akaike, H. (1973), “Information Theory and an Extension of the Maximum Likelihood Principle”, in Proceedings of the 2nd
International Symposium on Information Theory, 267-281.
[8] Bowerman, B. L., and R. T. O’Connell, (1993), Forecasting and Time Series：An Applied Approach, 3rd ed. Boston：Duxbury.
[9] Box, G. E. P. and G. M. Jenkins, (1994), Time Series Analysis, Forecasting and Control, 3rd ed. San Francisco：Holden-Day.
[10] Carter, L. R. (1996), “Forrecasting U.S. Mortality：A Comparison of Box-Jenkins ARIMA and Structural Time Series Models”, The Sociological Quarterly, 37, 127-144.
[11] El-Attar, S. (1988), “Population Forecasting：An Application of the Box-Jenkins Technique”, American Statistical Association, Proceedings of the Social Statistics Section, 305-310.
[12] Kashyap, R. L. and A. R. Rao, (1976), Dynamic Stochastic Models from Empirical Data, Academic Press, New York, San Francisco, London.
[13] Lee, R. D. (1992), “Stochastic Demographic Forecasting”,
International Journal of Forecasting, 8, 315-327.
[14] Lee, R. D. (1993), “Modeling and Forecasting the Time Series of US Fertility：Age Distribution, Range, and Ultimate Level”,International Journal of Forecasting, 9, 187-202.
[15] Lee, R. D., and Tuljapurkar, S. D. (1994), “Stochastic
Population Forecasts for the United States：Beyond High, Medium, and Low”, Journal of American Statistical Association, 89, 1175-1189.
[16] Pflaumer, P. (1992), “Forecasting US Population Totals with the Box-Jenkins Approach”, International Journal of Forecasting, 8, 329-338.
[17] Schwartz, G. (1978), “Estimating the Dimension of Model”, The Annals of Statistics, 6, 461-464.
[18] Voss, P. R. and C. D. Palit, (1981), “Forecasting State Population Using ARIMA Time Series Techniques”, Technical Series 70-6, University of Wisconsin-Madison, WI.		zh_TW