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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳旭昇(Shiu-Sheng Chen) | |
| dc.contributor.author | Chun-Yi Lee | en |
| dc.contributor.author | 李俊毅 | zh_TW |
| dc.date.accessioned | 2021-06-07T17:50:30Z | - |
| dc.date.copyright | 2020-08-25 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-08-05 | |
| dc.identifier.citation | 陳旭昇(2007),時間序列-總體經濟與財務金融之應用-,東華書局,2013第二版 胡仲英、陳寶瑞、洪慧燕、胡經芳、陳麗釧(2002)。景氣對策信號的檢討與修正。經濟研究，2，1-12 徐志宏(2008)。景氣對策信號之檢討與修正。經濟研究，8，89-120 徐志宏(2013)。景氣對策信號之檢討與修正。經濟研究，13，1-42 梁國源、賴柏志、黃文怡(1998)。台灣景氣指標之功能評估與改進之研究。行政院經濟建設委員會經濟研究處87年度委託研究報告，128pp。P73 管中閔、黃裕烈、徐世勛(2000)。新一波景氣循環的認定與景氣對策信號的改進。行政院經濟建設委員會委託研究報告。 蔡玉時(2004)。景氣對策信號構成項目與檢查值修正分析。經濟研究，5，55-83。 蕭峯雄、洪慧燕(1992)。景氣分析與對策。台北：遠東經濟研究顧問社。 陳劍虹(2018)。臺灣景氣對策信號之檢討與改善。經濟研究，18，P59-90 涂育嘉(2014)。台灣經濟成長率之混合頻率預測-MIDAS迴歸應用。國立中央大學經濟學研究所碩士論文。 劉欣姿(2020)。台灣GDP即時預測之研究。經濟研究，20，P1-16。 吳俊毅、朱浩榜(2020)。即時預報台灣經濟成長率:MIDAS模型之應用。中央銀行季刊，42-1，P59-84。 Alper,C.E.,Fendoglu,S., Saltoglu,B.(2008).Forecasting Stock Market Volatilities Using MIDAS Regressions: An Application to the Emerging Markets. MPRA Paper No. 7460. Andreou, E., Ghysels, E., Kourtellos, A. (2010). Regression models with mixed sampling frequencies. Journal of Econometrics, 158(2), 246-261. Andreou, E., Ghysels, E., Kourtellos, A. (2011). Forecasting with mixed-frequency data. In M. P. Clements D. F. Hendry (Eds.), Oxford Handbook of Economic Forecasting (pp. 225-245). Armesto, M.T., Engemann, K.M., Owyang, M.T. (2010). Forecasting with mixed frequencies. Federal Reserve Bank of St. Louis Review, 92(6), 521-536. Armesto, M. T., Hernández-Murillo, R., Owyang, M. T., Piger, J. M. (2009). Measuring the information content of the beige book: A mixed data sampling approach. Journal of Money, Credit and Banking, 41(1), 35-55. Aruoba, S. B., Diebold, F. X., Scotti, C. (2009). Real-time measurement of business conditions. Journal of Business and Economic Statistics, 27, 417-427. Almon, S. (1965). The distributed Lag between Capital Appropriations and Expenditures. Econometrica, 33(1), 178-196. Baele, L., Bekaert, G. Inghelbrecht, K., (2010). The determinants of stock and bond return comovements. Review of Financial Studies, 23(6), 2374-2428. Bai, J., (2003). Inferential theory for factor models of large dimensions. Econometrica, 71(1), 135-171. Bai, J., Ng, S. (2002). Determining the number of factors in approximate factor models. Econometrica, 70(1), 191-221. Bernanke, B. S., Gertler, M., Watson, M. (1997). Systematic monetary policy and the effects of oil price shocks. Brookings Papers on Economic Activity, 28(1), 91-157. Belhumeur, P. N., Hespanha, J. P., Kriegman, D. J. (1997). Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on pattern analysis and machine intelligence, 19(7), 711-720. Brown, D. P., Ferreira, M. A. (2003). The Information in the Idiosyncratic Volatility of Small Firms. Working paper, Univesrity of Wisconsin and ISCTE. Chen, X., Ghysels, E. (2011). News-Good or Bad-and its Impact on Volatility Forecasts over Multiple Horizons. The Review of Financial Studies, 24(1), 46-81. Chen, Y. C., Tsay, W. J., (2011). Forecasting Commodity Prices with Mixed-Frequency Data: An OLS-Based Generalized ADL Approach. Working Paper, 中央研究院經濟研究所. Clements, M. P., Galvão, A. B. (2008). Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US output growth. Journal of Business and Economic Statistics, 26(4), 546-554. Clements, M.P., Galvão, A. B., Kim, J. H. (2008). Quantile forecasts of daily exchange rate returns from forecasts of realized volatility. Journal of Empirical Finance, 15(4), 729-750. Colacito, R., Engle, R. F. Ghysels, E. (2011). A component model for dynamic correlations. Journal of Econometrics, 164(1), 45-59. Corsi, F., (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7(2), 174-196. Cox, D. R. (1981). Statistical analysis of time series: Some recent developments [with discussion and reply]. Scandinavian Journal of Statistics, 8(2), 93-115. Ding, C., He, X. (2004, July). K-means clustering via principal component analysis. In Proceedings of the twenty-first international conference on Machine learning (p. 29). ACM. Engle, R. F., Gonzalo, J., Rangel, J. G. (2008). The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes. Review of Financial Studies, 21(3), 1187-1222. Forni, M., Hallin, M., Lippi, M., Reichlin, L. (2000). The generalized dynamic-factor model: Identification and estimation. Review of Economics and Statistics, 82(4), 540-554. Foroni, C., Marcellino, M., Schumacher, C. (2011). U-MIDAS: MIDAS regressions with unrestricted lag polynomials. Discussion Paper Deutsche Bundesbank and European University Institute. Forsberg, L., Ghysels, E. (2006). Why do absolute returns predict volatility so well?. Journal of Financial Econometrics, 5(1), 31-67. Frale, C., Monteforte, L. (2011). FaMIDAS: A Mixed Frequency Factor Model with MIDAS structure. Bank of Italy Temi di Discussione -Working Paper No. 788. Franses, P. H. (1996). Periodicity and stochastic trends in economic time series. OUT Catalogue,Oxford University Press.n9780198774549 Ghysels, E. (1994). On the periodic structure of the business cycle. Journal of Business and Economic Statistics, 12(3),289-298. Ghysels, E. (2016). Macroeconomics and the reality of mixed frequency data. Journal of Econometrics, 193(2),294-314 Ghysels, E., Rubia, A., Valkanov, R. (2008). Multi-Period Forecasts of Variance: Direct, Iterated, and Mixed-Data Approaches. Working paper, Alicante, UCSD and UNC. Ghysels, E., Santa-Clara, P., Valkanov, R. (2002). The MIDAS touch: Mixed data sampling regression models. Working paper, UNC and UCLA. Ghysels, E., Santa-Clara, P., Valkanov, R. (2005). There is a risk-return tradeoff after all. Journal of Financial Economics, 76(3), 509-548. Ghysels, E., Santa-Clara, P., Valkanov, R. (2006). Predicting volatility: getting the most out of return data sampled at different frequencies. Journal of Econometrics, 131(1-2), 59-95. Ghysels, E., Sinko, A. (2006). Comment on realized variance and market microstructure noise by p. r. hansen and asger lunde. Journal of Business and Economic Statistics, 24, 192-194. Ghysels, E., Wright, J. (2009). Forecasting professional forecasters. Journal of Business and Economic Statistics, 27(4), 504-516. Giannone, D., Reichlin, L., Small, D. (2008). Nowcasting: The real-time informational content of macroeconomic data. Journal of Monetary Economics, 55(4), 665-676. Gladyshev, E. G. (1961). Periodically correlated random sequences. Soviet Mathematics, 2, 385-388. Hamilton, J. D. (2008). Daily monetary policy shocks and new home sales. Journal of Monetary Economics, 55(7), 1171-1190. Harvey, A. C., Pierse, R. G. (1984) Estimating missing observations in economic time series. Journal of the American Statistical Association, 79(385), 125-131 Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of educational psychology, 24(6), 417. Kunsch, H. R. (1989). The jackknife and the bootstrap for general stationary. The Annals of Statistics, 17(3), 1217-1241. Kramer, M. A. (1991). Nonlinear principal component analysis using autoassociative neural networks. AIChE journal, 37(2), 233-243. Kuzin, V. N., Marcellino, M. Schumacher, C. (2011). MIDAS versus mixed-frequency VAR: Nowcasting GDP in the Euro Area. International Journal of Forecasting, 27(2), 529-542. Kvedaras, V., Račkauskas, A. (2010). Regression Models with Variables of Different Frequencies: The Case of a Fixed Frequency Ratio. Discussion paper, Oxford Bulletin of Economics and Statistics, forthcoming. Liu, R. Y., Singh, K. (1992). Moving blocks jackknife and bootstrap capture weak dependence -Exploring the limits of bootstrap. New York, NY: John Wiley. León, Á., Nave, J. M., Rubio, G. (2007). The relationship between risk and expected return in Europe. Journal of Banking and Finance, 31(2), 495-512. Marcellino, M., Schumacher, C. (2010). Factor MIDAS for Nowcasting and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP. Oxford Bulletin of Economics and Statistics, 72(4), 518-550. Mariano, R. S., Murasawa, Y. (2003). A new coincident index of business cycles based on monthly and quarterly series. Journal of Applied Econometrics, 18(4), 427-443. Monteforte, L., Moretti, G. (2013). Real time forecasts of inflation: the role of financial variables. Journal of Forecasting, 32(1), 51-61. Nunes, L. C. (2005). Nowcasting quarterly gdp growth in a monthly coincident indicator model. Journal of Forecasting, 24(8), 575-592. Pearson, K. (1901). On lines and planes of closest fit to systems of point in space. Philosophical Magazine, 2(11), 559-572. Polits, D. N., Romano, J. P., Lai, T. L. (1992). Bootstrap confidence bands for spectra and cross-spectra. IEEE Transactions on Signal Processing, 40(5), 1206-1215. Pagano, M. (1978). On periodic and multiple autoregressions. The Annals of Statistics, 6(6), 1310-1317. Polits, D. N., Romano, J. P. (1994). The stationary bootstrap. Journal of the American Statistical Association, 89(428), 1303-1313. Rodriguez, A., Puggioni, G. (2010). Mixed frequency models: Bayesian approaches to estimation and prediction. International Journal of Forecasting, 26(2), 293-311. Schumacher, C., Breitung, J. (2008). Real-time forecasting of german gdp based on a large factor model with monthly and quarterly data. International Journalof Forecasting, 24(3), 386-398. Stock, J. H., Watson, M.W. (1989). New indexes of coincident and leading economic indicators. NBER macroeconomics annual, 4, 351–394. Stock, J. H., Watson, M. W. (2004). Combination forecasts of output growth in a seven-country data set. Journal of Forecasting, 23(6), 405-430. Tiao, G. C., Grupe, M. R. (1980). Hidden periodic autoregressive-moving average models in time series data. Biometrika, 67(2), 365-373. Timmermann, A. (2006). Forecast combinations. In G. Elliott, C. Granger, A. Timmermann (Eds.), Handbook of Economic Forecasting, vol. 1 . pp. 136-196 (North Holland: Amsterdam). Zadrozny, P. A. (1990). Forecasting US GNP at monthly intervals with an estimated bivariate time series model. Federal Reserve Bank of Atlanta Economic Review, 75, 2-15. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/15709 | - |
| dc.description.abstract | 金融數據係體現未來整體經濟環境之領先指標，故可藉其捕捉未來整體國家之經濟成長情形；此些金融變數雖具即時且快速之優點，然其公布頻率係以月為單位，且易受到市場氣氛波動而起伏。如欲使用此些金融變數以預測未來經濟成長變化，首先遭遇之問題即為如何將月資料型態之金融變數與季資料型態之經濟成長率相互結合。故本文擬利用另一種型態之回歸模型:即混合數據抽樣模型(Mixing Data Sampling, MIDAS)，進而解決資料頻率不對性之問題、輔以主成分分析方式(Principal components analysis, PCA)降低資料維度、從而提升分析品質，並將其應用於實證研究中，利用景氣對策信號及台灣加權股價指數報酬率對台灣實質經濟成長率進行預測。最終結果顯示運用MIDAS模型結合PCA預估經濟成長率，較使用簡單時間序列模型及平滑操作輔以簡單回歸分析方式(Ordinary Least Squares, OLS)更具效率，不僅大幅提升其解釋能力，亦可顯著改善長期預測誤差(Mean Square Error, MSE)。 綜言之，MIDAS模型可有效解決資料頻率不對稱之問題，並萃取出之解釋變數時間參數效果，PCA可降低資料維度，解決共線性問題，對於提升應變數之整體解釋與預估能力與有明確影響與貢獻。 | zh_TW |
| dc.description.abstract | Financial data is a leading indicator that reflects the overall economic environment in the future. It has the advantages of real-time and fast speed.However, the frequency of publication is in months.This article uses a mixed data sampling model (Mixing Data Sampling, MIDAS),Solve the problem of data frequency asymmetry,Supplemented by principal component analysis (PCA) to reduce data dimensions,To improve the quality of analysis,And apply it to empirical research,Forecast the real economic growth rate of Taiwan by using the Monitoring indicator and the return rate of Taiwan Capitalization Weighted Stock Index.The final result shows that the MIDAS model combined with PCA is used to estimate the economic growth rate,It is more efficient than using simple time series models and smoothing operations supplemented by simple regression analysis (Ordinary Least Squares, OLS),It not only greatly improves its interpretation ability, but also significantly improves the long-term prediction error (Mean Square Error, MSE). In summary,The MIDAS model can effectively solve the problem of data frequency asymmetry, and extract the effect of explanatory variable time parameters.PCA can reduce the data dimension and solve the problem of collinearity.It has a clear influence and contribution to improve the overall interpretation and estimation ability of dependent variable. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-07T17:50:30Z (GMT). No. of bitstreams: 1 U0001-0308202013073900.pdf: 4594371 bytes, checksum: a836e59a799871458899d90e7d0e2b47 (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 目錄 中文摘要…………………………………………………………………………………………I 英文摘要…………………………………………………………………………………………II 1 前言……………………………………………………………………………………………1 2 文獻回顧………………………………………………………………………………………5 2.1 混合數據抽樣模型(MIDAS)……………………………………………………………5 2.2 主成分分析(Principal components analysis, PCA)………………………………6 2.3 景氣對策信號……………………………………………………………………………7 3 研究方法………………………………………………………………………………………9 3.1 基礎設定…………………………………………………………………………………9 3.2 權重分配加總為1………………………………………………………………………11 3.2.1 Normalized Exponential Almon Lag Polynomial……………………………12 3.2.2 Normalized Beta Probability Density Function…………………………12 3.3 General Autogressive Distributed Lag (GADL)…………………………………13 3.4 Principal Components Analysis (PCA)……………………………………………16 3.4.1若矩陣C特徵根兩兩相異，則C有完整的線性獨立特徵向量………………17 4 資料分析………………………………………………………………………………………19 4.1 台灣經濟成長率…………………………………………………………………………20 4.2 台灣加權股價指數………………………………………………………………………21 4.3 景氣對策信號指標………………………………………………………………………22 4.4 工業生產指數及製造業銷售量指數……………………………………………………24 4.4.1 工業生產指數……………………………………………………………………24 4.4.2 製造業銷售量指數………………………………………………………………26 4.5 貨幣總計數M1B…………………………………………………………………………26 4.6 非農業部門就業人數……………………………………………………………………28 4.7 海關出口值及機械與電機設備進口值…………………………………………………29 4.8 製造業營業氣候測驗點…………………………………………………………………32 5 實證研究………………………………………………………………………………………33 5.1 簡單時間序列模型………………………………………………………………………35 5.2 納入景氣對策信號以及股價報酬率之簡單回歸模型…………………………………38 5.3 納入景氣對策信號以及股價報酬率的MIDAS模型……………………………………41 5.4 拆解景氣對策信號分項（分數）及股價報酬率之MIDAS模型………………………46 5.5 拆解景氣對策信號分項（成長率）及股價報酬率之MIDAS模型……………………50 5.6 拆解景氣對策信號分項（成長率）結合PCA及股價報酬率之MIDAS模型…………55 5.7 拆解景氣對策信號分項（分數）結合PCA及股價報酬率之MIDAS模型……………59 5.8 合併景氣對策信號分項分數與成長率於PCA後之MIDAS模型………………………63 5.9 前述實證研究之重要摘要如下…………………………………………………………65 6 結論……………………………………………………………………………………………69 參考文獻………………………………………………………………………………………71 附錄……………………………………………………………………………………………79 A 非線性回歸模型估計……………………………………………………………………79 A.1 Nonlinear Least Square Theory…………………………………………………79 A.2 Gauss-Newton法……………………………………………………………………80 A.2 Newton-Raphson法…………………………………………………………………81 A.2 兩種方法之比較……………………………………………………………………83 A.2 多變數之一般化……………………………………………………………………84 B 其他統計表………………………………………………………………………………85 | |
| dc.language.iso | zh-TW | |
| dc.subject | 台灣經濟成長率預估 | zh_TW |
| dc.subject | 混合數據抽樣模型 | zh_TW |
| dc.subject | 主成分分析 | zh_TW |
| dc.subject | Taiwan's economic growth rate forecast | en |
| dc.subject | MIDAS | en |
| dc.subject | PCA | en |
| dc.title | 即時台灣經濟成長率預估MIDAS模型與PCA整合應用 | zh_TW |
| dc.title | Integrated application of MIDAS model and PCA for real-time economic growth rate estimation of Taiwan | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 駱明慶(Ming-Ching Luoh),林馨怡(Hsin-Yi Lin) | |
| dc.subject.keyword | 混合數據抽樣模型,主成分分析,台灣經濟成長率預估, | zh_TW |
| dc.subject.keyword | MIDAS,PCA,Taiwan's economic growth rate forecast, | en |
| dc.relation.page | 87 | |
| dc.identifier.doi | 10.6342/NTU202002261 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2020-08-06 | |
| dc.contributor.author-college | 社會科學院 | zh_TW |
| dc.contributor.author-dept | 經濟學研究所 | zh_TW |
| 顯示於系所單位： | 經濟學系 | |
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