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題名 能源價格與台灣總體經濟之研究—運用MIDAS模型
Energy Prices and Taiwan’s Macro Economy – the application of MIDAS model作者 陳泳如 貢獻者 林信助
陳泳如關鍵詞 能源價格
混頻因果關係檢定
MIDAS模型
即時預報
Energy price
Mixed-frequency causality test
MIDAS model
Nowcast日期 2021 上傳時間 4-Aug-2021 14:25:29 (UTC+8) 摘要 能源價格變動與總體經濟的相關性雖已存在不少討論，但能源價格變動究竟以何種形式及管道衝擊總體經濟仍存在許多爭論。特別是過去大多數相關文獻遷就於總體經濟資料的限制，大多以低頻率季資料來研究其與能源價格變動之間的相關性。不過，隨著高頻資料的取得愈發容易，加上混頻模型的蓬勃發展，利用高頻資料即時預報低頻總體經濟指標之可行性大幅提高。故本文欲藉由混頻因果關係檢定及各式混頻模型，在保有完整能源價格之高頻訊息下，重新檢視能源價格與總體經濟之關係。本文將2000年第一季至2019年第四季作為樣本期間，以西德州中級原油每日現貨價格作為能源價格來源，並將其建構為價格變動之波動率形式對經濟成長率進行即時預報。本文的實證結果顯示，能源價格變動之波動率與經濟成長率間存在負向關係。與既有國內研究的不同之處在於，本文發現能源價格變動之波動率富含的高頻訊息能提供更多關於當季經濟成長率之資訊，並進一步改善模型預測績效。
Despite the existence of numerous studies on the correlation between energy price variation and the performance of the macro economy, plenty disputes remain in the form and the channel through which energy price shocks affect the macro economy. In particular, most of the relevant literature in the past was subject to the lower frequency of macroeconomic data, mostly using low-frequency quarterly data to study the correlation between them and energy price variation. However, due to the availability of high-frequency data and the development of mixed-frequency models, it is more feasible to forecast macroeconomic indicators with high-frequency data. Motivated by such a development, this paper attempts to re-examine the correlation between energy prices and the macro economy with mixed-frequency causality test and related models, while maintaining the integrity of information existing in the high frequency energy price data. In this paper, the sampling period is from Q1 2000 to Q4 2019. We use daily spot prices for West Texas Intermediate crude oil as the source of energy prices and then construct them as the form of the volatility of energy price growth rate to nowcast economic growth rate. Our empirical results reveal a negative relationship between the volatility of energy price growth rate and economic growth rate. However, different from the existing domestic research, this paper finds that the high-frequency information contained in the volatility of energy price growth rate can provide more information about the current quarter economic growth rate and further improve the forecast accuracy of the model.參考文獻 吳俊毅、朱浩榜（2020）。即時預報臺灣的經濟成長率：MIDAS模型之應用，中央銀行季刊，42卷第1期，頁59-84。陳志鴻（2010）。中央銀行對於歷次石油危機的政策實施分析。國立清華大學高階經營管理碩士在職專班碩士論文，新竹市。陳虹均、郭炳伸、林信助（2012）。能源價格衝擊與臺灣總體經濟。臺灣經濟預測與政策，42卷第2期，頁1-36。張天惠（2012）。我國金融情勢指數與總體經濟預測，中央銀行季刊，34卷第2期，頁11-42。張文碩（2013）。國際金價與國際油價對台灣加權指數影響效果之分析與探討。東海大學經濟學系碩士論文，台中市。黃柏勛（2016）。檢定油價波動對總體經濟之不對稱影響 －以台灣為例。國立清華大學經濟學系碩士論文，新竹市。劉金全、劉漢、印重（2010）。中國宏觀經濟混頻數據模型應用。經濟科學，5期。Balke, N. S., Brown, S. P., & Yucel, M. K. (2002). Oil price shocks and the US economy: Where does the asymmetry originate?. The Energy Journal, 23(3).Clements, M. P., & Galvão, A. B. (2008). Macroeconomic forecasting with mixed-frequency data: Forecasting output growth in the United States. Journal of Business and Economic Statistics, 26(4), 546-554.Çelik, S., & Ergin, H. (2014). Volatility forecasting using high frequency data: Evidence from stock markets. Economic modelling, 36, 176-190.Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427-431.Engle, R. F., Ghysels, E., & Sohn, B. (2008, August). On the economic sources of stock market volatility. In AFA 2008 New Orleans Meetings Paper.Ferderer, J. P. (1996). Oil price volatility and the macroeconomy. Journal of macroeconomics, 18(1), 1-26.Foroni, C., Marcellino, M., & Schumacher, C. (2015). Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials. Journal of the Royal Statistical Society: Series A (Statistics in Society), 178(1), 57-82.Ghysels, E., Santa-Clara, P., & Valkanov, R. (2004). The MIDAS Touch: Mixed Data Sampling Regression Mod. Finance.Ghysels, E., Sinko, A., & Valkanov, R. (2007). MIDAS regressions: Further results and new directions. Econometric Reviews, 26(1), 53-90.Ghysels, E., Hill, J. B., & Motegi, K. (2016). Testing for Granger causality with mixed frequency data. Journal of Econometrics, 192(1), 207-230.Ghysels, E., Hill, J. B., & Motegi, K. (2015). Simple Granger Causality Tests for Mixed Frequency Data. SSRN Electronic Journal.Hamilton, J. D. (1983). Oil and the macroeconomy since World War II. Journal of political economy, 91(2), 228-248.Hamilton, J. D. (1996). This is what happened to the oil price - Macroeconomy relationship. Journal of Monetary Economics, 38(2), 215-220.Hamilton, J. D. (2003). What is an oil shock? Journal of Econometrics, 113(2) , 363-398.Hooker, M. A. (1996). What happened to the oil price-macroeconomy relationship?. Journal of monetary Economics, 38(2), 195-213.Lee, K., Ni, S., & Ratti, R. A. (1995). Oil shocks and the macroeconomy: the role of price variability. The Energy Journal, 16(4).Mork, K. A. (1989). Oil and the macroeconomy when prices go up and down: an extension of Hamilton`s results. Journal of political Economy, 97(3), 740-744.Nonejad, N. (2020). Crude oil price point forecasts of the Norwegian GDP growth rate. Empirical Economics, 1-18.Ravazzolo, F., & Rothman, P. (2013). Oil and U.S. GDP: A Real-Time Out-of-Sample Examination. Journal of Money, Credit and Banking, 45(2–3), 449-463.Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599-607.Yu, X., & Huang, Y. (2021). The impact of economic policy uncertainty on stock volatility: Evidence from GARCH–MIDAS approach. Physica A: Statistical Mechanics and its Applications, 570, 125794. 描述 碩士
國立政治大學
國際經營與貿易學系
108351007資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108351007 資料類型 thesis dc.contributor.advisor 林信助 zh_TW dc.contributor.author (Authors) 陳泳如 zh_TW dc.creator (作者) 陳泳如 zh_TW dc.date (日期) 2021 en_US dc.date.accessioned 4-Aug-2021 14:25:29 (UTC+8) - dc.date.available 4-Aug-2021 14:25:29 (UTC+8) - dc.date.issued (上傳時間) 4-Aug-2021 14:25:29 (UTC+8) - dc.identifier (Other Identifiers) G0108351007 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/136272 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 國際經營與貿易學系 zh_TW dc.description (描述) 108351007 zh_TW dc.description.abstract (摘要) 能源價格變動與總體經濟的相關性雖已存在不少討論，但能源價格變動究竟以何種形式及管道衝擊總體經濟仍存在許多爭論。特別是過去大多數相關文獻遷就於總體經濟資料的限制，大多以低頻率季資料來研究其與能源價格變動之間的相關性。不過，隨著高頻資料的取得愈發容易，加上混頻模型的蓬勃發展，利用高頻資料即時預報低頻總體經濟指標之可行性大幅提高。故本文欲藉由混頻因果關係檢定及各式混頻模型，在保有完整能源價格之高頻訊息下，重新檢視能源價格與總體經濟之關係。本文將2000年第一季至2019年第四季作為樣本期間，以西德州中級原油每日現貨價格作為能源價格來源，並將其建構為價格變動之波動率形式對經濟成長率進行即時預報。本文的實證結果顯示，能源價格變動之波動率與經濟成長率間存在負向關係。與既有國內研究的不同之處在於，本文發現能源價格變動之波動率富含的高頻訊息能提供更多關於當季經濟成長率之資訊，並進一步改善模型預測績效。 zh_TW dc.description.abstract (摘要) Despite the existence of numerous studies on the correlation between energy price variation and the performance of the macro economy, plenty disputes remain in the form and the channel through which energy price shocks affect the macro economy. In particular, most of the relevant literature in the past was subject to the lower frequency of macroeconomic data, mostly using low-frequency quarterly data to study the correlation between them and energy price variation. However, due to the availability of high-frequency data and the development of mixed-frequency models, it is more feasible to forecast macroeconomic indicators with high-frequency data. Motivated by such a development, this paper attempts to re-examine the correlation between energy prices and the macro economy with mixed-frequency causality test and related models, while maintaining the integrity of information existing in the high frequency energy price data. In this paper, the sampling period is from Q1 2000 to Q4 2019. We use daily spot prices for West Texas Intermediate crude oil as the source of energy prices and then construct them as the form of the volatility of energy price growth rate to nowcast economic growth rate. Our empirical results reveal a negative relationship between the volatility of energy price growth rate and economic growth rate. However, different from the existing domestic research, this paper finds that the high-frequency information contained in the volatility of energy price growth rate can provide more information about the current quarter economic growth rate and further improve the forecast accuracy of the model. en_US dc.description.tableofcontents 摘要 IAbstract II第一章 前言 1第一節 研究動機 1第二節 文獻回顧及貢獻 2第二章 變數定義與資料來源 6第三章 研究方法 9第一節 單根檢定 9第二節 混頻因果關係檢定 10第三節 混頻抽樣數據 (MIDAS)模型 12第四章 實證過程 16第一節 敘述性統計 16第二節 混頻因果關係檢定 16第三節 混頻數據抽樣 (MIDAS)模型估計結果 18第四節 控制變數之影響 23第五章 結論 28第一節 結論 28第二節 研究限制 28參考文獻 30附錄 33附錄A 國際油價變動之波動率與國內 GDP成長率相關性 33附錄B Wald檢定量推導過程 34附錄C U-MIDAS樣本內外績效 35附錄D 落後分配函數之權重分配 37 zh_TW dc.format.extent 2619308 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108351007 en_US dc.subject (關鍵詞) 能源價格 zh_TW dc.subject (關鍵詞) 混頻因果關係檢定 zh_TW dc.subject (關鍵詞) MIDAS模型 zh_TW dc.subject (關鍵詞) 即時預報 zh_TW dc.subject (關鍵詞) Energy price en_US dc.subject (關鍵詞) Mixed-frequency causality test en_US dc.subject (關鍵詞) MIDAS model en_US dc.subject (關鍵詞) Nowcast en_US dc.title (題名) 能源價格與台灣總體經濟之研究—運用MIDAS模型 zh_TW dc.title (題名) Energy Prices and Taiwan’s Macro Economy – the application of MIDAS model en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 吳俊毅、朱浩榜（2020）。即時預報臺灣的經濟成長率：MIDAS模型之應用，中央銀行季刊，42卷第1期，頁59-84。陳志鴻（2010）。中央銀行對於歷次石油危機的政策實施分析。國立清華大學高階經營管理碩士在職專班碩士論文，新竹市。陳虹均、郭炳伸、林信助（2012）。能源價格衝擊與臺灣總體經濟。臺灣經濟預測與政策，42卷第2期，頁1-36。張天惠（2012）。我國金融情勢指數與總體經濟預測，中央銀行季刊，34卷第2期，頁11-42。張文碩（2013）。國際金價與國際油價對台灣加權指數影響效果之分析與探討。東海大學經濟學系碩士論文，台中市。黃柏勛（2016）。檢定油價波動對總體經濟之不對稱影響 －以台灣為例。國立清華大學經濟學系碩士論文，新竹市。劉金全、劉漢、印重（2010）。中國宏觀經濟混頻數據模型應用。經濟科學，5期。Balke, N. S., Brown, S. P., & Yucel, M. K. (2002). Oil price shocks and the US economy: Where does the asymmetry originate?. The Energy Journal, 23(3).Clements, M. P., & Galvão, A. B. (2008). Macroeconomic forecasting with mixed-frequency data: Forecasting output growth in the United States. Journal of Business and Economic Statistics, 26(4), 546-554.Çelik, S., & Ergin, H. (2014). Volatility forecasting using high frequency data: Evidence from stock markets. Economic modelling, 36, 176-190.Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427-431.Engle, R. F., Ghysels, E., & Sohn, B. (2008, August). On the economic sources of stock market volatility. In AFA 2008 New Orleans Meetings Paper.Ferderer, J. P. (1996). Oil price volatility and the macroeconomy. Journal of macroeconomics, 18(1), 1-26.Foroni, C., Marcellino, M., & Schumacher, C. (2015). Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials. Journal of the Royal Statistical Society: Series A (Statistics in Society), 178(1), 57-82.Ghysels, E., Santa-Clara, P., & Valkanov, R. (2004). The MIDAS Touch: Mixed Data Sampling Regression Mod. Finance.Ghysels, E., Sinko, A., & Valkanov, R. (2007). MIDAS regressions: Further results and new directions. Econometric Reviews, 26(1), 53-90.Ghysels, E., Hill, J. B., & Motegi, K. (2016). Testing for Granger causality with mixed frequency data. Journal of Econometrics, 192(1), 207-230.Ghysels, E., Hill, J. B., & Motegi, K. (2015). Simple Granger Causality Tests for Mixed Frequency Data. SSRN Electronic Journal.Hamilton, J. D. (1983). Oil and the macroeconomy since World War II. Journal of political economy, 91(2), 228-248.Hamilton, J. D. (1996). This is what happened to the oil price - Macroeconomy relationship. Journal of Monetary Economics, 38(2), 215-220.Hamilton, J. D. (2003). What is an oil shock? Journal of Econometrics, 113(2) , 363-398.Hooker, M. A. (1996). What happened to the oil price-macroeconomy relationship?. Journal of monetary Economics, 38(2), 195-213.Lee, K., Ni, S., & Ratti, R. A. (1995). Oil shocks and the macroeconomy: the role of price variability. The Energy Journal, 16(4).Mork, K. A. (1989). Oil and the macroeconomy when prices go up and down: an extension of Hamilton`s results. Journal of political Economy, 97(3), 740-744.Nonejad, N. (2020). Crude oil price point forecasts of the Norwegian GDP growth rate. Empirical Economics, 1-18.Ravazzolo, F., & Rothman, P. (2013). Oil and U.S. GDP: A Real-Time Out-of-Sample Examination. Journal of Money, Credit and Banking, 45(2–3), 449-463.Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599-607.Yu, X., & Huang, Y. (2021). The impact of economic policy uncertainty on stock volatility: Evidence from GARCH–MIDAS approach. Physica A: Statistical Mechanics and its Applications, 570, 125794. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202100828 en_US
