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題名 含外生多變量TAR模型分析及其應用在黃金價格的預測
Multivariate TAR Model with Exogenous Variables Analysis and its Applications to the Gold Price Forecasting作者 侯博耀
Hou, Bo-Yao貢獻者 曾正男
侯博耀
Hou, Bo-Yao關鍵詞 外生變數
ARIMA
TAR
門檻值
黃金價格
Exogenous variables
ARIMA
TAR
Threshold
Gold price日期 2023 上傳時間 2-Jun-2023 11:44:26 (UTC+8) 摘要 本研究利用含外生多變量門檻自迴歸(TAR)模型，分析並預測110年至112年的黃金價格。相較傳統的ARIMA模型，含外生多變量TAR模型更能有效反映時間數列結構改變的過程與趨勢，對於預測上具有更大的優勢。此外，TAR模型的適用範圍很廣，因為時間數列通常為非線性，而且容易受到多個變數影響，因此加入多個外生變數，可以更準確的分析資料並進行預測。我們以黃金價格為例，提出之多變量TAR模型，較傳統預測模型有更高的預測精準度。研究目標:含外生多變量TAR模型分析及其預測。研究方法:找出含外生多變量門檻函數，計算含外生多變量TAR門檻值並進行模式架構分析及其預測。研究發現:含外生多變量TAR模型預測能力較傳統預測方法更佳。研究創新:提出外生變數門檻模式演算法。研究價值:財務實證分析上預測策略。
In this research, we use a multivariate threshold autoregressive (TAR) model with exogenous variables to analyze and predict the gold price from 110 to 112 years. Compared with the traditional ARIMA model, the multivariate TAR model with exogenous variables can more effectively reflect the process and trend of time series structure changes, and has greater advantages in prediction. In addition, the TAR model has a wide range of applications, because the time series generally has nonlinear phenomena and is easily affected by multiple variables. Therefore, adding multiple exogenous variables as a consideration can analyze the data and make predictions more accurately. Taking the gold price as an example, the multivariate TAR model proposed has higher prediction accuracy than traditional forecasting models. Research Objectives: Analysis and prediction of multivariate TAR model with exogenous factors. Research Methods: Find out the exogenous multivariate threshold function, calculate the multivariate TAR threshold with exogenous variables, and conduct model architecture analysis and prediction. Research Findings: The multivariate TAR model with exogenous variables predictive ability is better than traditional forecasting methods. Research Innovation: Proposed exogenous variable threshold model algorithm. Research Value: Forecasting Strategies in Financial Empirical Analysis.參考文獻 1.經濟部能源局https://www.moeaboe.gov.tw/ECW/populace/home/Home.aspx2.吳柏林(1995)時間數列分析導論。台北：華泰書局。3.楊奕農(2009)時間序列分析：經濟與財務上之應用。台北，雙葉書廊。4.Tong H. and Lim K. S. (1980). Threshold Autoregressive, Limit Cycles and Cyclical Data (with Discussion), Journal of the Royal Statistical Society. Series B, Vol.42, No.3, pp.245-292.5.Subba Rao T. and Gabr M. (1980). A test for linearity of stationary time series analysis, Journal of Time Series Analysis , Vol.1, No.1, pp145-158.6.Haggan V. and Ozaki T. (1980). Amplitude-dependent Exponential AR Model Fitting for Non-linear Random Vibrations, in Time Series, (O. D. Anderson ed.), North-Holland, Amsterdam.7.J. D. Byers and D.A. Peel (1995). Evidence on volatility spillovers in the interwar floating exchange rate period based on high/low prices, Applied Economics Letters, Taylor and Francis Journals, Vol.2, No.10, pp394-396.8.Chow, G. C. (1960). Tests of equality between sets of coefficients in two linear regressions. Econometrica: Journal of the Econometric Society, 28(3), 591-605.9.Liu Y, Garceau NY, Loros JJ and Dunlap JC (1997). Thermally regulated translational control of FRQ mediates aspects of temperature responses in the Neurospora circadian clock, Cell, Vol.89, pp477–486 .10.Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica: Journal of the Econometric Society, 47-78.11.Andrews, D. W., & Ploberger, W. (1994). Optimal tests when a nuisance parameter is present only under the alternative. Econometrica: Journal of the Econometric Society, 1383-1414.12.Kumar K and Wu B (2001). Detection of change points in time series analysis with fuzzy statistics, International Journal of Systems Science, Vol.32, No.9, pp1185-1192.13.Zhou, H. (2005). A Bayesian approach to integrating stochastic search variable selection and change point detection. Journal of Econometrics, 126(1), 57-77.14.Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A k-means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(1), 100–108.15.Shen, Y., & Hakes, D. R. (1995). A modified threshold regression approach to measuring discrimination. Journal of Human Resources, 30(3), 457-477.16.Hansen, B. E. (1999). Threshold effects in non-dynamic panels: Estimation, testing, and inference. Journal of econometrics, 93(2), 345-368. 描述 碩士
國立政治大學
應用數學系
106751009資料來源 http://thesis.lib.nccu.edu.tw/record/#G0106751009 資料類型 thesis dc.contributor.advisor 曾正男 zh_TW dc.contributor.author (Authors) 侯博耀 zh_TW dc.contributor.author (Authors) Hou, Bo-Yao en_US dc.creator (作者) 侯博耀 zh_TW dc.creator (作者) Hou, Bo-Yao en_US dc.date (日期) 2023 en_US dc.date.accessioned 2-Jun-2023 11:44:26 (UTC+8) - dc.date.available 2-Jun-2023 11:44:26 (UTC+8) - dc.date.issued (上傳時間) 2-Jun-2023 11:44:26 (UTC+8) - dc.identifier (Other Identifiers) G0106751009 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/145077 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 應用數學系 zh_TW dc.description (描述) 106751009 zh_TW dc.description.abstract (摘要) 本研究利用含外生多變量門檻自迴歸(TAR)模型，分析並預測110年至112年的黃金價格。相較傳統的ARIMA模型，含外生多變量TAR模型更能有效反映時間數列結構改變的過程與趨勢，對於預測上具有更大的優勢。此外，TAR模型的適用範圍很廣，因為時間數列通常為非線性，而且容易受到多個變數影響，因此加入多個外生變數，可以更準確的分析資料並進行預測。我們以黃金價格為例，提出之多變量TAR模型，較傳統預測模型有更高的預測精準度。研究目標:含外生多變量TAR模型分析及其預測。研究方法:找出含外生多變量門檻函數，計算含外生多變量TAR門檻值並進行模式架構分析及其預測。研究發現:含外生多變量TAR模型預測能力較傳統預測方法更佳。研究創新:提出外生變數門檻模式演算法。研究價值:財務實證分析上預測策略。 zh_TW dc.description.abstract (摘要) In this research, we use a multivariate threshold autoregressive (TAR) model with exogenous variables to analyze and predict the gold price from 110 to 112 years. Compared with the traditional ARIMA model, the multivariate TAR model with exogenous variables can more effectively reflect the process and trend of time series structure changes, and has greater advantages in prediction. In addition, the TAR model has a wide range of applications, because the time series generally has nonlinear phenomena and is easily affected by multiple variables. Therefore, adding multiple exogenous variables as a consideration can analyze the data and make predictions more accurately. Taking the gold price as an example, the multivariate TAR model proposed has higher prediction accuracy than traditional forecasting models. Research Objectives: Analysis and prediction of multivariate TAR model with exogenous factors. Research Methods: Find out the exogenous multivariate threshold function, calculate the multivariate TAR threshold with exogenous variables, and conduct model architecture analysis and prediction. Research Findings: The multivariate TAR model with exogenous variables predictive ability is better than traditional forecasting methods. Research Innovation: Proposed exogenous variable threshold model algorithm. Research Value: Forecasting Strategies in Financial Empirical Analysis. en_US dc.description.tableofcontents 1.前言 52.研究方法 82.1ARIMA模型 82.2多變量TAR模型 92.3主相關外生變數 112.4如何決定轉折點(區間) 112.5AIC與SBC判定 122.6模式預測與修正 133.實證分析－黃金價格 143.1資料來源 143.2以ARIMA模型建構最佳模式 143.3用外生多變數建構門檻自迴歸模型 163.4預測結果 184.結論與建議 205.參考文獻 21附錄1. 23附錄2. 28 zh_TW dc.format.extent 1582406 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0106751009 en_US dc.subject (關鍵詞) 外生變數 zh_TW dc.subject (關鍵詞) ARIMA zh_TW dc.subject (關鍵詞) TAR zh_TW dc.subject (關鍵詞) 門檻值 zh_TW dc.subject (關鍵詞) 黃金價格 zh_TW dc.subject (關鍵詞) Exogenous variables en_US dc.subject (關鍵詞) ARIMA en_US dc.subject (關鍵詞) TAR en_US dc.subject (關鍵詞) Threshold en_US dc.subject (關鍵詞) Gold price en_US dc.title (題名) 含外生多變量TAR模型分析及其應用在黃金價格的預測 zh_TW dc.title (題名) Multivariate TAR Model with Exogenous Variables Analysis and its Applications to the Gold Price Forecasting en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 1.經濟部能源局https://www.moeaboe.gov.tw/ECW/populace/home/Home.aspx2.吳柏林(1995)時間數列分析導論。台北：華泰書局。3.楊奕農(2009)時間序列分析：經濟與財務上之應用。台北，雙葉書廊。4.Tong H. and Lim K. S. (1980). Threshold Autoregressive, Limit Cycles and Cyclical Data (with Discussion), Journal of the Royal Statistical Society. Series B, Vol.42, No.3, pp.245-292.5.Subba Rao T. and Gabr M. (1980). A test for linearity of stationary time series analysis, Journal of Time Series Analysis , Vol.1, No.1, pp145-158.6.Haggan V. and Ozaki T. (1980). Amplitude-dependent Exponential AR Model Fitting for Non-linear Random Vibrations, in Time Series, (O. D. Anderson ed.), North-Holland, Amsterdam.7.J. D. Byers and D.A. Peel (1995). Evidence on volatility spillovers in the interwar floating exchange rate period based on high/low prices, Applied Economics Letters, Taylor and Francis Journals, Vol.2, No.10, pp394-396.8.Chow, G. C. (1960). Tests of equality between sets of coefficients in two linear regressions. Econometrica: Journal of the Econometric Society, 28(3), 591-605.9.Liu Y, Garceau NY, Loros JJ and Dunlap JC (1997). Thermally regulated translational control of FRQ mediates aspects of temperature responses in the Neurospora circadian clock, Cell, Vol.89, pp477–486 .10.Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica: Journal of the Econometric Society, 47-78.11.Andrews, D. W., & Ploberger, W. (1994). Optimal tests when a nuisance parameter is present only under the alternative. Econometrica: Journal of the Econometric Society, 1383-1414.12.Kumar K and Wu B (2001). Detection of change points in time series analysis with fuzzy statistics, International Journal of Systems Science, Vol.32, No.9, pp1185-1192.13.Zhou, H. (2005). A Bayesian approach to integrating stochastic search variable selection and change point detection. Journal of Econometrics, 126(1), 57-77.14.Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A k-means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(1), 100–108.15.Shen, Y., & Hakes, D. R. (1995). A modified threshold regression approach to measuring discrimination. Journal of Human Resources, 30(3), 457-477.16.Hansen, B. E. (1999). Threshold effects in non-dynamic panels: Estimation, testing, and inference. Journal of econometrics, 93(2), 345-368. zh_TW
