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請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/3872
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor陳旭昇(Shiu-Sheng Chen)
dc.contributor.authorCheng-Wei Linen
dc.contributor.author林正偉zh_TW
dc.date.accessioned2021-05-13T08:37:50Z-
dc.date.available2016-08-26
dc.date.available2021-05-13T08:37:50Z-
dc.date.copyright2016-08-26
dc.date.issued2016
dc.date.submitted2016-07-24
dc.identifier.citation[1] Hirotugu Akaike. A new look at the statistical model identification. Automatic Control, IEEE Transactions on, 19(6):716–723, 1974.
[2] Ron Alquist and Lutz Kilian. What do we learn from the price of crude oil futures? Journal of Applied Econometrics, 25(4):539–573, 2010.
[3] Ron Alquist, Lutz Kilian, Robert J Vigfusson, et al. Forecasting the price of oil. Handbook of economic forecasting, 2:427–507, 2013.
[4] Robert B Barsky and Lutz Kilian. Do we really know that oil caused the great stagflation? a monetary alternative. In NBER Macroeconomics Annual 2001, Volume 16, pages 137–198. MIT Press, 2002.
[5] John M Bates and Clive WJ Granger. The combination of forecasts. Or, pages 451–468, 1969.
[6] Christiane Baumeister and Lutz Kilian. Forecasting the real price of oil in a changing world: A forecast combination approach. Journal of Business & Economic Statistics, 33(3):338–351, 2015.
[7] Lasse Bork, Pablo Rovira Kaltwasser, and Piet Sercu. Do exchange rates really help forecasting commodity prices? Available at SSRN 2473624, 2014.
[8] Bimal K Bose. Global warming: Energy, environmental pollution, and the impact of power electronics. Industrial Electronics Magazine, IEEE, 4(1):6–17, 2010.
[9] Steven T Buckland, Kenneth P Burnham, and Nicole H Augustin. Model selection: an integral part of inference. Biometrics, pages 603–618, 1997.
[10] Kenneth P Burnham and David R Anderson. Multimodel inference understanding aic and bic in model selection. Sociological methods & research, 33(2):261–304, 2004.
[11] M Busse, C Knittel, and F Zettelmeyer. Pain at the pump: How gasoline prices affect automobile purchasing. Technical report, mimeo, Northwestern University, 2010.
[12] Shiu-Sheng Chen. Forecasting crude oil price movements with oil-sensitive stocks. Economic Inquiry, 52(2):830–844, 2014.
[13] Yu-chin Chen, Kenneth S Rogoff, and Barbara Rossi. Can exchange rates forecast commodity prices? Quarterly Journal of Economics, 125(3), 2010.
[14] Sergey Chernenko, Krista Schwarz, and Jonathan H Wright. The information content of forward and futures prices: Market expectations and the price of risk. FRB International Finance discussion paper, (808), 2004.
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[16] Todd E Clark and Michael W McCracken. The power of tests of predictive ability in the presence of structural breaks. Journal of Econometrics, 124(1):1–31, 2005.
[17] Todd E Clark and Kenneth D West. Approximately normal tests for equal predictive accuracy in nested models. Journal of econometrics, 138(1):291–311, 2007.
[18] Francis X Diebold and Roberto S Mariano. Comparing predictive accuracy. Journal of Business & Economic Statistics, 13(3):253–263, 1995.
[19] Max Gillman and Anton Nakov. Monetary effects on nominal oil prices. The North American Journal of Economics and Finance, 20(3):239–254, 2009.
[20] Clive WJ Granger and Ramu Ramanathan. Improved methods of combining forecasts. Journal of Forecasting, 3(2):197–204, 1984.
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[22] Bruce E Hansen. Least squares model averaging. Econometrica, 75(4):1175–1189, 2007.
[23] Bruce E Hansen. Least-squares forecast averaging. Journal of Econometrics, 146(2): 342–350, 2008.
[24] Bruce E Hansen and Jeffrey S Racine. Jackknife model averaging. Journal of Econometrics, 167(1):38–46, 2012.
[25] Raftery Hoeting, Madigan and Volinsky. Bayesian model averaging: a tutorial. Statistical science, pages 382–401, 1999.
[26] Ching-Kang Ing and Tze Leung Lai. A stepwise regression method and consistent model selection for high-dimensional sparse linear models. Statistica Sinica, pages 1473–1513, 2011.
[27] Atsushi Inoue and Lutz Kilian. In-sample or out-of-sample tests of predictability: Which one should we use? Econometric Reviews, 23(4):371–402, 2005.
[28] Sylvain Leduc and Keith Sill. A quantitative analysis of oil-price shocks, systematic monetary policy, and economic downturns. Journal of Monetary Economics, 51(4): 781–808, 2004.
[29] Chu-An Liu and Biing-Shen Kuo. Model averaging in predictive regressions. The Econometrics Journal, 2016.
[30] Colin L Mallows. Some comments on c p. Technometrics, 15(4):661–675, 1973.
[31] Andrew H McCallum and Tao Wu. Do oil futures prices help predict future oil prices? FRBSF Economic Letter, (2005-38), 2005.
[32] Knut Anton Mork, Øystein Olsen, and Hans Terje Mysen. Macroeconomic responses to oil price increases and decreases in seven oecd countries. The Energy Journal, pages 19–35, 1994.
[33] Frederick Mosteller and John W Tukey. Data analysis, including statistics. The Collected Works of John W. Tukey: Graphics 1965-1985, 5:123, 1988.
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/3872-
dc.description.abstract本文試以多種組合預測方法,評估多個文獻中曾提及的預測變數對原油月均價、月底價的預測效果,使用資料期間為1986:M2至2015:M10。根據Working (1960)闡述月均價格變動的前後期將會有相關性,本研究導入一個新變數用於預測原油月均價,其結果顯著優於no-change預測,使MSPE降低幅度達39%。此外,以過去文獻提及的預測變數來預測原油月底價時,預測效果集中於特定期間,無法於整體評估期間保持預測能力。同時,文獻上計算MSPE時有兩種算法,在預測效果於期間內並非均勻分布的情況下,將可能使兩種算法所得的MSPE Ratio有所差異,且此MSPE Ratio差異的大小亦與評估期間的選取有關。zh_TW
dc.description.abstractThis article uses monthly data from 1986:M2 to 2016:M10 to examine the predictive power for both monthly-averaged oil prices and end-of-month oil prices by multiple forecast combination methods. According to Working (1960) , our work derive a new predictor of monthly-averaged oil prices which can significantly reduce the MSPE by 39% compare with no-change forecast. When forecasting the end-of-month oil prices, the predictive power only exists in a certain period, can't hold the power for the whole evaluation period. Our work explain the relationship between MSPE calculated with two different ways. Because the predictive power only exists in a certain period, there would be some different between MSPE Ratio of two kinds of MSPE calculation ways.This difference would be correlated with the choice of evaluation period.en
dc.description.provenanceMade available in DSpace on 2021-05-13T08:37:50Z (GMT). No. of bitstreams: 1
ntu-105-R01323072-1.pdf: 729852 bytes, checksum: ec988fdf95ddf0311bc1ab2668a87e35 (MD5)
Previous issue date: 2016
en
dc.description.tableofcontents口試委員審定書. . . . . . . . . . . . . . . . . . . . . . . . . . . i
誌謝. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
中文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
英文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
1 前言. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2 計量模型. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1 預測組合方法: 線性模型. . . . . . . . . . . . . . . . . . . . . 6
2.2 模型權重. . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2.1 簡單平均法與Bates-Granger 法. . . . . . . . . . . . . . . . . 7
2.2.2 Information Criterion 與Smoothed Information Criterion. . . . 7
2.2.3 Cross-Validation 與Jackknife Model Averaging. . . . . . . . . 8
2.2.4 Mallows Model Averaging . . . . . . . . . . . . . . . . . . . 8
2.2.5 Plug-In Averaging . . . . . . . . . . . . . . . . . . . . . . 8
3 資料與變數選擇. . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.1 月底價變動率. . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.2 月均價變動率. . . . . . . . . . . . . . . . . . . . . . . . . . 10
4 實證結果. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.1 樣本內估計. . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.2 MSPE、MAPE 與Sucessiful Ratio . . . . . . . . . . . . . . . . . 11
4.3 MSPE 與MAPE 的兩種計算方式. . . . . . . . . . . . . . . . . . . 12
4.4 時間與預測表現. . . . . . . . . . . . . . . . . . . . . . . . . 16
4.5 再探MSPE 與MAPE 的兩種計算方式. . . . . . . . . . . . . . . . . 17
5 結論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
附錄一Working Effect . . . . . . . . . . . . . . . . . . . . . . .. 27
附錄二預測月均價的預測變數選擇. . . . . . . . . . . . . . . . . . . 29
參考文獻. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
dc.language.isozh-TW
dc.title原油價格組合預測模型之建構zh_TW
dc.titleForecasting Crude Oil Price: A Forecast Combination Approachen
dc.typeThesis
dc.date.schoolyear104-2
dc.description.degree碩士
dc.contributor.oralexamcommittee周有熙(Yu-Hsi Chou),劉祝安(Chu-An Liu)
dc.subject.keyword組合預測,油價預測,月均價,月底價,no-change 預測,AIC,BIC,AICc,HQ,HDBIC,CV,MMA,JMA,PIA,MSPE,MAPE,Working effect,zh_TW
dc.subject.keywordForecast combination,Forecast oil price,Monthly-averaged price,End-of-month price,No-change forecast,AIC,BIC,AICc,HQ,HDBIC,CV,MMA,JMA,PIA,MSPE,MAPE,Working effect,en
dc.relation.page38
dc.identifier.doi10.6342/NTU201601209
dc.rights.note同意授權(全球公開)
dc.date.accepted2016-07-25
dc.contributor.author-college社會科學院zh_TW
dc.contributor.author-dept經濟學研究所zh_TW
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