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題名 透過利率期限結構建立總體經濟產出缺口之預測模型 ─ 以美國為例
Construct the forecast models for economic output gap through the term structure of interest rates ─ evidences for the United States
作者 張楷翊
貢獻者 李桐豪<br>陳威光
張楷翊
關鍵詞 殖利率曲線
總體經濟預測
支持向量機
羅吉斯迴歸
Yield curve
Economic forecast
SVM
Logistic regression
日期 2017
上傳時間 11-Jul-2017 11:32:50 (UTC+8)
摘要 經濟體的產出缺口一直是政策執行者的觀察重點，當一國出現產出缺口時，代表資源配置並不均衡，將發生通貨膨脹或是失業的現象，如能提早預期到未來是否會出現產出缺口，將可讓政策執行者即早進行政策實施，且有文獻指出，殖利率曲線資料中具有隱含未來經濟狀況之資訊。
本研究以美國財政部與聯準會之公開資料，將以殖利率曲線之斜率進行預測產出缺口；本文研究美國1977年至2016年之國民生產毛額成分與殖利率之資料，目標為建立對於未來一季將出現正向或負向缺口現象之模型，本研究建立三種預測模型進行比較，分別為線性迴歸模型、羅吉斯迴歸模型與機器學習中的支持向量機，以實質GDP的缺口預測而言，研究結果顯示，三者預測準確度均達到65%以上，支持向量機的準確度更達到80.85%。
得出以下結論，第一，殖利率曲線對於未來總體經濟產出缺口具有一定之解釋力；第二，對於高維度之預測模型在機器學習中的支持向量機表現會較一般常用之迴歸模型佳；第三，進出口的預測力在三個模型下均表現較差，可能為殖利率曲線對於進出口並不具有完整有效的資訊，可能有其餘的經濟指標或金融市場資訊可以解釋；第四，對於實質消費與投資等民間部門經濟行為有超過80%的預測力。
The output gap of the economy has always been the objectives of policy practitioners. When a country appear the output gap, it means that the allocation of resources is not equilibrium and the inflation or unemployment will occur. The output gap will allow policymakers to implement the policy as early as possible, and the literature notes that the information of the yield curve has information about the future economic situation.
In this paper, we using the data from the U.S. Department of Treasury and the Federal Reserve to predict the output gap by the slopes of the yield curve. Our goal is to construct the prediction model for the next quarter. To forecast the real GDP gap, three prediction models were compared, linear regression model, logistic regression model and support vector machine. The results show that the accuracy of the three predictions are more than 65%, support vector machine accuracy to reach 80.85%.
We can have conclusions showing below: First, the yield curve has significant explanatory power for the overall economic output gap in the future. Second, the support vector machine perform better than the commonly used regression model. Third, the predictive power of real import and export in the three models are poor performance, there may be the rest of the economic indicators or financial market information can be explained. Fourth, the real consumption and investment has the predictive power more than 80% of the forecast.
參考文獻 一、中文文獻
1.吳懿娟(2007)。我國殖利率曲線與經濟活動間關係之實證分析。中央銀行季刊第二十九卷第三期，p23-64。
2.李桐豪(2001)。債券市場發展對貨幣政策之影響。中央銀行季刊第二十三卷第一期，p23-46。
3.林慈芳(2009)。2012年臺灣經濟成長潛力及政策模擬分析。綜合規劃研究 96-97年，p25-54。
4.陳旭昇(2013)。時間序列分析—總體經濟與財務金融之應用。東華書局。
5.黃承龍、陳穆臻、王界人(2004)。支援向量機於信用評等之應用。計量管理期刊，vol. 1，No. 2，p-155-172。

二、英文文獻
1.Ang, Piazzesi & Wei. (2006). What does the yield curve tell us about GDP?, Journal of Economics, 131, 1-2.
2.Ariyo, Adewumi, Ayo. (2014). Stock price prediction using the ARIMA model, IEEE
3.Berge. (2014).Predicting recessions with leading indicators: model averaging and selection over the business cycle, The Federal Reserve Bank of Kansas city, research working papers
4.Bernanke, Boivin & Eliasz. (2005). Measuring the effects of monetary policy: a factor-augmented vector autoregression (FAVAR) approach, Quart J Econ 120, 387-422.
5.Box, George, Jenkins, Gwilym. (1970). Time series analysis: forecasting and control.
6.Chauvet, Potter. (2002). Predicting a recession: evidence from the yield curve in the presence of structural breaks, Economic letters 77(2), 245-253.
7.Chinn, Kucko. (2017). The predictive power of the yield curve across countries and time, National Bureau of Economic Research.
8.Chionis, Gogas & Pragidis. (2009). Predicting european union recessions in the euro era: the yield curve as a forecasting tool of economic activity, International Advances in Economic Research ,16(1).
9.Devi, Sundar and Alli. (2013). An effective time series analysis for stock trend prediction using ARIMA model for Nifty midcap-50, International Journal of Data Mining & Knowledge Management Process (IJDKP), Vol. 3, No. 1.
10.Diebold & Li. (2006). Forecasting the term structure of government bond yields, Journal of Economics, 130, 337-364.
11.Dritsaki. (2015). Forecasting real GDP rate through econometric models: an empirical study from Greece, Journal of International Business and Economics, Vol. 3, No. 1, 13-19.
12.Erdigan, Bennett & Ozyildirim. (2014). Recession prediction using yield curve and stock market liquidity deviation measures, Review of Finance, Oxford Academic, Vol. 19, No. 1
13.Estrella, Mishkin. (1996). The yield curve as a predictor of U.S. recessions, Federal Reserve Bank of New York, Current Issues in Economics and Finance, June 1996, Vol 2 No. 7.
14.Estrella, Hardouvelis. (1991). The term structure as a predictor of real economic activity, Journal of Finance, 46, 555-576.
15.Hodrick, Prescott. (1997). Postwar business cycles: an empirical investigation, J Money Credit Bank 29(1), 1-16.
16.Jahan, Mahmud. (2013). What is the output gap?, International Monetary Fund.
17.King, Rebelo. (1993). Low frequency filtering and real business cycles, Journal of Economic Dynamics and Control, 17 1/2, 207-231.
18.Liu, Moench. (2014). What predicts U.S. recessions?, Federal Reserve Bank of New York Staff Reports
19.Maity, Chatterjee. (2012). Forecasting GDP growth rates of India: an empirical study, International Journal of Economics and Management Sciences, 1(9), 52-58.
20.Moench. (2008). Forecasting the yield curve in a data-rich environment: a no-arbitrage factor-augmented VAR approach, Journal of Econometrics, 146(1), 26-43.
21.Papadimitriou, Gogas, Matthaiou and Chrysanthidou. (2015). Yield curve and recession forecasting in a machine learning framework, Computational Economics, Volume 45, Issue 4, 635-645.
22.Philips, Jin. (2015). Business cycles, trend elimination, and the HP filter, Cowles Foundation Discussion Paper, No. 2005.
23.Vapnik and Cortes. (1995). Support-vector networks. machine learning, 20(3): 273-297.
24.Wei, Bian and Yuan. (2010). Analysis and forecast of Shaanxi GDP based on the ARIMA model, Asian Agricultural Research, 2(1), 34-41.
25.Wright. (2006). The yield curve and predicting recessions, Finance and Economics Discussion Series, Federal Reserve Board.
26.Zakai. (2014). A time series modeling on GDP of Pakistan, Journal of Contemporary Issues in Business Research, 3(4), 200-210.
27.Zhang. (2013). Modeling and forecasting regional GDP in Sweden using autoregressive models, Working Paper, Hogskolan Dalarna University.
描述 碩士
國立政治大學
金融學系
104352004
資料來源 http://thesis.lib.nccu.edu.tw/record/#G1043520041
資料類型 thesis
dc.contributor.advisor 李桐豪<br>陳威光zh_TW
dc.contributor.author (Authors) 張楷翊zh_TW
dc.creator (作者) 張楷翊zh_TW
dc.date (日期) 2017en_US
dc.date.accessioned 11-Jul-2017 11:32:50 (UTC+8)-
dc.date.available 11-Jul-2017 11:32:50 (UTC+8)-
dc.date.issued (上傳時間) 11-Jul-2017 11:32:50 (UTC+8)-
dc.identifier (Other Identifiers) G1043520041en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/110808-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 金融學系zh_TW
dc.description (描述) 104352004zh_TW
dc.description.abstract (摘要) 經濟體的產出缺口一直是政策執行者的觀察重點，當一國出現產出缺口時，代表資源配置並不均衡，將發生通貨膨脹或是失業的現象，如能提早預期到未來是否會出現產出缺口，將可讓政策執行者即早進行政策實施，且有文獻指出，殖利率曲線資料中具有隱含未來經濟狀況之資訊。
本研究以美國財政部與聯準會之公開資料，將以殖利率曲線之斜率進行預測產出缺口；本文研究美國1977年至2016年之國民生產毛額成分與殖利率之資料，目標為建立對於未來一季將出現正向或負向缺口現象之模型，本研究建立三種預測模型進行比較，分別為線性迴歸模型、羅吉斯迴歸模型與機器學習中的支持向量機，以實質GDP的缺口預測而言，研究結果顯示，三者預測準確度均達到65%以上，支持向量機的準確度更達到80.85%。
得出以下結論，第一，殖利率曲線對於未來總體經濟產出缺口具有一定之解釋力；第二，對於高維度之預測模型在機器學習中的支持向量機表現會較一般常用之迴歸模型佳；第三，進出口的預測力在三個模型下均表現較差，可能為殖利率曲線對於進出口並不具有完整有效的資訊，可能有其餘的經濟指標或金融市場資訊可以解釋；第四，對於實質消費與投資等民間部門經濟行為有超過80%的預測力。		zh_TW
dc.description.abstract (摘要) The output gap of the economy has always been the objectives of policy practitioners. When a country appear the output gap, it means that the allocation of resources is not equilibrium and the inflation or unemployment will occur. The output gap will allow policymakers to implement the policy as early as possible, and the literature notes that the information of the yield curve has information about the future economic situation.
In this paper, we using the data from the U.S. Department of Treasury and the Federal Reserve to predict the output gap by the slopes of the yield curve. Our goal is to construct the prediction model for the next quarter. To forecast the real GDP gap, three prediction models were compared, linear regression model, logistic regression model and support vector machine. The results show that the accuracy of the three predictions are more than 65%, support vector machine accuracy to reach 80.85%.
We can have conclusions showing below: First, the yield curve has significant explanatory power for the overall economic output gap in the future. Second, the support vector machine perform better than the commonly used regression model. Third, the predictive power of real import and export in the three models are poor performance, there may be the rest of the economic indicators or financial market information can be explained. Fourth, the real consumption and investment has the predictive power more than 80% of the forecast.		en_US
dc.description.tableofcontents 第一章 緒論 1
第一節 研究背景 1
第二節 研究動機與目的 3
第二章 文獻探討 4
第一節 經濟衰退預測模型 4
第二節 殖利率曲線對經濟活動之預測力 5
第三章 資料建立與研究方法 7
第一節 研究架構 7
第二節 參考數據 8
第三節 預測模型 11
第四節 建立預測模型過程 16
第四章 實證結果與分析 19
第五章 結論與建議 25
第一節 結論 25
第二節 後續研究 26
參考文獻 27
附錄 30		zh_TW
dc.format.extent 1888067 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G1043520041en_US
dc.subject (關鍵詞) 殖利率曲線zh_TW
dc.subject (關鍵詞) 總體經濟預測zh_TW
dc.subject (關鍵詞) 支持向量機zh_TW
dc.subject (關鍵詞) 羅吉斯迴歸zh_TW
dc.subject (關鍵詞) Yield curveen_US
dc.subject (關鍵詞) Economic forecasten_US
dc.subject (關鍵詞) SVMen_US
dc.subject (關鍵詞) Logistic regressionen_US
dc.title (題名) 透過利率期限結構建立總體經濟產出缺口之預測模型 ─ 以美國為例zh_TW
dc.title (題名) Construct the forecast models for economic output gap through the term structure of interest rates ─ evidences for the United Statesen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 一、中文文獻
1.吳懿娟(2007)。我國殖利率曲線與經濟活動間關係之實證分析。中央銀行季刊第二十九卷第三期，p23-64。
2.李桐豪(2001)。債券市場發展對貨幣政策之影響。中央銀行季刊第二十三卷第一期，p23-46。
3.林慈芳(2009)。2012年臺灣經濟成長潛力及政策模擬分析。綜合規劃研究 96-97年，p25-54。
4.陳旭昇(2013)。時間序列分析—總體經濟與財務金融之應用。東華書局。
5.黃承龍、陳穆臻、王界人(2004)。支援向量機於信用評等之應用。計量管理期刊，vol. 1，No. 2，p-155-172。

二、英文文獻
1.Ang, Piazzesi & Wei. (2006). What does the yield curve tell us about GDP?, Journal of Economics, 131, 1-2.
2.Ariyo, Adewumi, Ayo. (2014). Stock price prediction using the ARIMA model, IEEE
3.Berge. (2014).Predicting recessions with leading indicators: model averaging and selection over the business cycle, The Federal Reserve Bank of Kansas city, research working papers
4.Bernanke, Boivin & Eliasz. (2005). Measuring the effects of monetary policy: a factor-augmented vector autoregression (FAVAR) approach, Quart J Econ 120, 387-422.
5.Box, George, Jenkins, Gwilym. (1970). Time series analysis: forecasting and control.
6.Chauvet, Potter. (2002). Predicting a recession: evidence from the yield curve in the presence of structural breaks, Economic letters 77(2), 245-253.
7.Chinn, Kucko. (2017). The predictive power of the yield curve across countries and time, National Bureau of Economic Research.
8.Chionis, Gogas & Pragidis. (2009). Predicting european union recessions in the euro era: the yield curve as a forecasting tool of economic activity, International Advances in Economic Research ,16(1).
9.Devi, Sundar and Alli. (2013). An effective time series analysis for stock trend prediction using ARIMA model for Nifty midcap-50, International Journal of Data Mining & Knowledge Management Process (IJDKP), Vol. 3, No. 1.
10.Diebold & Li. (2006). Forecasting the term structure of government bond yields, Journal of Economics, 130, 337-364.
11.Dritsaki. (2015). Forecasting real GDP rate through econometric models: an empirical study from Greece, Journal of International Business and Economics, Vol. 3, No. 1, 13-19.
12.Erdigan, Bennett & Ozyildirim. (2014). Recession prediction using yield curve and stock market liquidity deviation measures, Review of Finance, Oxford Academic, Vol. 19, No. 1
13.Estrella, Mishkin. (1996). The yield curve as a predictor of U.S. recessions, Federal Reserve Bank of New York, Current Issues in Economics and Finance, June 1996, Vol 2 No. 7.
14.Estrella, Hardouvelis. (1991). The term structure as a predictor of real economic activity, Journal of Finance, 46, 555-576.
15.Hodrick, Prescott. (1997). Postwar business cycles: an empirical investigation, J Money Credit Bank 29(1), 1-16.
16.Jahan, Mahmud. (2013). What is the output gap?, International Monetary Fund.
17.King, Rebelo. (1993). Low frequency filtering and real business cycles, Journal of Economic Dynamics and Control, 17 1/2, 207-231.
18.Liu, Moench. (2014). What predicts U.S. recessions?, Federal Reserve Bank of New York Staff Reports
19.Maity, Chatterjee. (2012). Forecasting GDP growth rates of India: an empirical study, International Journal of Economics and Management Sciences, 1(9), 52-58.
20.Moench. (2008). Forecasting the yield curve in a data-rich environment: a no-arbitrage factor-augmented VAR approach, Journal of Econometrics, 146(1), 26-43.
21.Papadimitriou, Gogas, Matthaiou and Chrysanthidou. (2015). Yield curve and recession forecasting in a machine learning framework, Computational Economics, Volume 45, Issue 4, 635-645.
22.Philips, Jin. (2015). Business cycles, trend elimination, and the HP filter, Cowles Foundation Discussion Paper, No. 2005.
23.Vapnik and Cortes. (1995). Support-vector networks. machine learning, 20(3): 273-297.
24.Wei, Bian and Yuan. (2010). Analysis and forecast of Shaanxi GDP based on the ARIMA model, Asian Agricultural Research, 2(1), 34-41.
25.Wright. (2006). The yield curve and predicting recessions, Finance and Economics Discussion Series, Federal Reserve Board.
26.Zakai. (2014). A time series modeling on GDP of Pakistan, Journal of Contemporary Issues in Business Research, 3(4), 200-210.
27.Zhang. (2013). Modeling and forecasting regional GDP in Sweden using autoregressive models, Working Paper, Hogskolan Dalarna University.		zh_TW