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研究生:王啟秀
研究生(外文):Chi-Hsiu Wang
論文名稱:台灣資訊產業產值預測模型之研究
論文名稱(外文):Production Forecasting of Taiwan's IT Industries: Bayesain Vector Autoregressive Approaches
指導教授:虞孝成虞孝成引用關係王淑芬王淑芬引用關係
指導教授(外文):Hsiao-Cheng YuShu-Fen Wang
學位類別:博士
校院名稱:國立交通大學
系所名稱:科技管理研究所
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
畢業學年度:94
語文別:英文
論文頁數:110
中文關鍵詞:貝氏向量自我迴歸冪轉換半導體光電產業電腦製造業產業群聚
外文關鍵詞:Bayesian Vector AutogressivePower TransformationSemiconductorPhotonicsComputer ManufacturingIndustrial Clusters
相關次數:
  • 被引用被引用:4
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  • 收藏至我的研究室書目清單書目收藏:1
近幾年來,台灣電子資訊等相關高科技產業呈現高度成長,取代八十年代的金融產業成為台灣經濟成長的骨幹。然而高科技產業往往有週期性景氣循環,產品生命週期短,因此產值預測困難。它會影響政府產業政策以及整體環境之投資計畫,以及廠商投資擴廠之決策,對於企業經營的獲利或虧損影響甚巨。
有鑑於此,本文之研究目的則利用時間序列模型中自我迴歸模型(ARIMA) 、向量自我迴歸模型(VAR) 、貝氏向量自我迴歸模型(BVAR)結合產業群聚效應,探討台灣IT產業(光電產業、半導體產業以及電腦製造業)與其他週邊產業彼此之間是否具有長期之均衡關係,將收集10年間的季資料區分為兩部分,第一部分用來建立模型,第二部分則作為預測之比較值。
結果發現,在三個產業的模型驗證中均呈現VAR的表現較差,其次為AR,而BVAR則是驗證模型中最好的,證明BVAR模型可以用於少樣本的時間序列與動態預測中。而跟其他研究機構比較結果如下 (1)與IT RI的產業報告比較半導體產業:BVAR模型不論在1998年以及2001年產業大幅成長衰退時預測均較ITRI精準。(2)與III的產業報告比較電腦製造產業:BVAR模型預測較III精準。
The production forecasting of high technology industries is an important issue for entrepreneurs and governments, but it suffers from the situation of fast growth and frequent fluctuation. In this article, we propose a forecasting method that combines the clustering effect, different transformation of data, and non-informative diffuse-prior Bayesian vector autoregression (DBVAR) model to forecast the productions of technology industries. The BVAR model possesses the superiority of Bayesian statistics in small sample forecasting and holds the dynamic property of VAR (Vector autoregression) model. Three industries are examined to verify the proposed method. The subjects are: (1) Using Four Forecasting Models to Forecast of Total Production Output of Taiwan’s Photonic Industry, (2) Using Four Forecasting Models to Forecast of Total Production Output of Taiwan’s Semiconductor Industry, (3) Using Four Forecasting Models to Forecast of Total Production Output of Taiwan’s Computer Manufacturing Industry. It is found that the DBVAR models outperform the other three conventional time series models including the autoregression (AR), vector autoregression (VAR), and Litterman Bayesian VAR (LBVAR) models. Moreover, the DBVAR models also could exactly find the inflection point of the trend and give a promising forecasting. Our forecasting method is therefore concluded as a feasible approach for production prediction, especially for technology industries in volatile environment.
摘要 …………………………………………………………………… I
Abstract ………………………………….……………………………... II
誌謝 …………………………………………………………………… III
Table of Contents ………………………………………………………. IV
List of Tables …………………………………………………………... VII
List of Figures ……………………………………………….…………. VIII
1. Introduction ……………………………………………………… 1
2. Literature Review ………………………………………………... 5
2.1 BVAR Forecasting Models …..……………………..………….. 5
2.2 Logarithmic Transformation and Power Transformation Forecasting Models ………………………..………….………...
8
3. Methodologies ……………………………………………………. 11
3.1 The General VAR Model ………………………………………. 11
3.2 The BVAR Models …………………………………………….. 12
3.2.1 The Litterman’s BVAR Model ………………………………..……… 12
3.2.2 The Noninformative Prior BVAR Model ………………..…..…….. 14
3.3 The Power Transformation BVAR Model ……………………..….…. 20
3.4 Rolling Forecasting Procedures and Performance Criteria ……. 24
3.4.1 Look Back &Look Ahead Span Procedures ………………..……..……… 24
3.4.2 Performance Criteria………………………….…………..…..…….. 25
4. Using Four Forecasting Models to Forecast of Total Production Output of Taiwan’s Photonic Industry ………………………………………………...…………
27
4.1 Dependent and Independent Variables…………………..……... 28
4.2 Pre processing of Dependent and Independent Variables …...… 30
4.2.1 Logarithmic Adjustment …………………………….……………….. 30
4.2.2 Seasonal Adjustment ……………….………………………………… 30
4.2.3 The First-order Difference Adjustment ……...………..…………… 31
4.3 The Selection of Dependent Variables ……………………….. 33
4.4 The Lag Order Selection Procedures ………………………….. 35
4.4.1 The Order Selection of the VAR Model………………………….… 35
4.4.2 The Order Selection of the BVAR Model………………………….. 37
4.5 Assessment of Forecast Results ………………….....……….… 38
4.6 Finding and Discussion …………………………………….…………… 42
5. Using Four Forecasting Models to Forecast of Total Production Output of Taiwan’s Semiconductor Industry ………………………………………………...…………
43
5.1 Dependent and Independent Variables…………………..……... 43
5.2 Pre processing of Dependent and Independent Variables …...… 46
5.2.1 Logarithmic adjustment …………………………….……………….. 46
5.2.2 Seasonal adjustment ……………….………………………………… 46
5.2.3 The First-order difference adjustment ……………..……………… 46
5.3 The Selection of Dependent Variables ……………………..….. 49
5.4 The Lag Order Selection Procedures ………………………….. 51
5.4.1 The Order Selection of the VAR Model………………………….… 51
5.4.2 The Order Selection of the BVAR Model………………………….. 52
5.5 Assessment of Forecast Results ………………….....……….… 53
5.6 Another Approach for Semiconductor Industry ……………….. 57
5.6.1 Dependent and Independent Variables Collection…...………….… 57
5.6.2 Some Comparisons with the Industrial Technology Research Institute’s Prediction on Semiconductor Production……………..
63
5.7 Power Transformation for Semiconductor Industry ………….... 69
5.8 Finding and Discussion …………………………………….…………… 73
6. Using Four Forecasting Models to Forecast of Total Production Output of Taiwan’s Computer Manufacturing Industry ………………………………………………...…………
75
6.1 Dependent and Independent Variables…………………..……... 76
6.2 Pre processing of Dependent and Independent Variables …...… 80
6.3 The Lag Order Selection Procedures ………………………….. 80
6.4 Assessment of Forecast Results ………………….....……….… 82
6.5 Some Comparisons with the Institute for Information Industry’s (III)
Prediction on Computer Manufacturing Production……..……….….. 85
6.6 Power Transformation for Computer Manufacturing Industry....... 89
6.7 Findings and Discussion ………………………………..….…………… 95
7. Conclusion and Future Direction 97
7.1 Conclusion …………………………..…………………..……... 76
7.2 Future Researches ……………………………………...…...… 100
[1] Amirizadeh. H. and R.M Todd. “More growth ahead for ninth district states”, Federal Reserve Bank of Minneapolis Quarterly Review, 8, pp.8-17, 1984.
[2] Ashley. R. “On the relative worth of recent macroeconomic forecasts”, International Journal of Forecasting, 4, pp.363-376, 1988.
[3] Ariño, M.A. & Franses, P.H. “Forecasting the levels of vector autoregressive log-transformed time series”, International Journal of Forecasting, 16, pp.111-116, 1988.
[4] Bergeron, S., Lallich, S., & Bas, C.L. (1998). “Location of innovating activities, industrial structure and techno-industrial clusters in the French economy”, Evidence from US patenting. Research Policy, 26, 733-751, 1985-1990.
[5] Cargill, T.F. and S.A. Morus. “A vector autoregression model of NAVADA economy”, Economic Review, Federal Reserve Bank of San Francisco, No. 1, pp.21-32, 1988.
[6] Chang, T. (2002). “The retrospective and forecast of Taiwan’s semiconductor industry of 2002 Q2 (in Chinese)”. Hsinchu, Taiwan: Industrial Technology Research Institute (ITRI).
[7] Chang, P. & Hsu, C. “The development strategies for Taiwan’s semiconductor industry”. IEEE Transactions on Engineering Management, 45 (4), 349-356,1988.
[8] Chang, S., Lai, H., & Yu, H. “A variable P value rolling Grey forecasting model for Taiwan semiconductor industry production”. Technological Forecasting and Social Change, forthcoming, 2004.
[9] Chen, C.W.S. & Lee, J.C. “On selecting a power transformation in time-series analysis”, Journal of Forecasting, 16, pp.343-354, 1997.
[10] Curry, D. J., Divakar, S., Mathur, S. K., Whiteman, C. H. “BVAR as A Category Management Tool: An Illustration and Comparison with Alternative Techniques”, Journal of Forecasting 14, pp.181-199, 1995.
[11] Dickey, D.A. and W.A. Fuller. “Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root”, Econometrica 49, pp.1057-1072, 1981.
[12] Doan, T., RATS User’s Manual, Estima, Evanston, IL, pp.8-20, 1992.
[13] Doan, T., Litterman, R. B. and Sims, C. A. “Forecasting and Conditional Projection Using Realistic Prior Distributions”, Econometric Reviews 3, pp.1-100, 1984.
[14] Dreze, J. H. and J.-A. Morales. “Bayesian full information analysis of simultaneous equations”, Journal of the American Statistical Association, 71, pp.919-23, 1980.
[15] Dua, P. and Ray, S. C. “A BVAR model for the Connecticut Economy”, Journal of Forecasting 14, pp.167-180, 1995.
[16] Dua, P. and Smyth, D. J. “Forecasting US Homes Sales Using BVAR Models and Survey Data on Households’ Buying Attitudes for Homes”, Journal of Forecasting 14, pp.217-227, 1995.
[17] Engle, R.F. and C.W. Granger. “Co-Integration and Error Correction: Representation, Estimation and Testing”, Econometrica 55, pp.251-276, 1987.
[18] Enders, W. Applied Econometric Time Series, John Wiley & Sons, 1995.
[19] Evans, Charles L. and Kenneth N. Kuttner. Can VARs Describe Monetary Policy? Unpublished Manuscript, April, 1998.
[20] Funke, M. “Assessing the forecasting accuracy of monthly vector autoregression models: The case of five OECD countries”, International Journal of Forecasting, 6, pp.363-378, 1990.
[21] Geisser, S. “Bayesian estimation in multivariate analysis”, Annals of Mathematical Statistics, 36, pp.150-9, 1965.
[22] Geweke, J. “Bayesian inference in econometric models using Monte Carlo integration”, Econometrica, 58, pp.1317-39, 1989.
[23] Geweke, J. “Monte Carlo simulation and numerical integration, in H. Amman, D. Kendrick and J. Rust (eds)”, Handbook of Computational Economics, North-Holland, Amsterdam, 1995.
[24] Gover, J.E. “Strengthening the competitiveness of U.S. microelectronics”. IEEE Transactions on Engineering Management, 40 (1), pp.3-13, 1993.
[25] Granger, C.W.J. & Newbold, P. “Forecasting transformed series”, Journal of The Royal Statistical Society B, 38, pp.189-203, 1976.
[26] Granger, C.W.J. & Newbold, P. “Forecasting Economic Time Series”, 2nd edition, New York: Academic Press, 1986.
[27] Guerrero, V.M. “Time-series analysis supported by power transformations”, Journal of Forecasting, 12, pp.37-48, 1993.
[28] Hamilton, J. D. Time Series Analysis, Princeton Press, NJ, 1994.
[29] Holden, K. “Vector Autoregression Modeling and Forecasting”, Journal of Forecasting 14, pp.159-166, 1995.
[30] Hsu, P. Wang, C., Shyu, J.Z., & Yu, H. (2003). A Litterman BVAR approach for production forecasting of technology industries. Technological Forecasting and Social Change, 70 (1), 67-82.
[31] Industrial Economics & Knowledge Center (IEK). “An unexpected recession of Taiwan’s IC industry (in Chinese)”. Hsinchu, Taiwan: Industrial Technology Research Institute (ITRI), 2001.
[32] Industrial Economics & Knowledge Center (IEK). “Annuals of Taiwan's semiconductor industry 2004 (in Chinese)”. Department of Industrial Technology, Taipei, Taiwan: Ministry of Economic Affairs (MOEA), 2004.
[33] Industrial Technology Research Institute (ITRI). “Annals of Taiwan’s semiconductor industry 1997 (In Chinese)”. Department of Industrial Technology, Taipei, Taiwan: Ministry of Economic Affairs (MOEA), 1997.
[34] Industrial Technology Research Institute (ITRI). “Annals of Taiwan’s semiconductor industry 1997 (In Chinese)”. Department of Industrial Technology, Taipei, Taiwan: Ministry of Economic Affairs (MOEA), 1998.
[35] Industrial Technology Research Institute (ITRI). “Annals of Taiwan’s semiconductor industry 1997 (In Chinese)”. Department of Industrial Technology, Taipei, Taiwan: Ministry of Economic Affairs (MOEA), 1999.
[36] Institute for Information Industry (III). “Annuals of Taiwan’s information industry 1999 (in Chinese)”. Department of Industrial Technology, Taipei, Taiwan: Ministry of Economic Affairs (MOEA), 1999.
[37] Institute for Information Industry (III). “Annuals of Taiwan’s information industry 2000 (in Chinese)”. Department of Industrial Technology, Taipei, Taiwan: Ministry of Economic Affairs (MOEA), 2000.
[38] Institute for Information Industry (III). “Annuals of Taiwan’s information industry 2001 (in Chinese)”. Department of Industrial Technology, Taipei, Taiwan: Ministry of Economic Affairs (MOEA), 2001.
[39] Institute for Information Industry (III). “Annuals of Taiwan’s information industry 2002 (in Chinese)”. Department of Industrial Technology, Taipei, Taiwan: Ministry of Economic Affairs (MOEA), 2002.
[40] Institute for Information Industry (III). “Annuals of Taiwan’s information industry 2004 (in Chinese)”. Department of Industrial Technology, Taipei, Taiwan: Ministry of Economic Affairs (MOEA), 2004.
[41] J.C. Lee, P.H. Hsu, C.H. Wang. “Production forecasting for technology industries: A Bayesian vector autoregression (BVAR) model based on industrial clusters”, Working Paper, Institute of Statistics, National Chiao Tung University, 2000.
[42] Joutz, F. L., Maddala, G. S. and Trost, R. P. “An Integrated Bayesian Vector Autoregression and Error Correction Model for Forecasting Electricity Consumption and Prices”, Journal of Forecasting 14, pp.287-310, 1995.
[43] Kadiyala, K. R. and Karlsson, S. “Forecasting with generalized Bayesian vector autoregressions”, Journal of Forecasting, 12, pp.365-78, 1993.
[44] Kadiyala, K. R. and Karlsson, S. “Numerical Method for Estimation and Inference in Bayesian VAR-Models”, Journal of Applied Econometrics 12, pp.99-132, 1997.
[45] Kumar, V., Leone, R. P. and Gaskins, J. N. “Aggregate and Disaggregate Sector Forecasting Using Consumer Confidence Measures”, International Journal of Forecasting 11, pp.361-377, 1995.
[46] Kuprianov, A. and W. Lupoletti. “The economic outlook for fifth district states in 1984: Forecasts from vector autoregression models”, Economic Review, 70, Federal Reserve Bank of Richmond, January/February, pp.12-23, 1984.
[47] Lesage, J.P. “Incorporating regional wage relations in local forecasting models with a Bayesian prior”, International Journal of Forecasting, 5, pp.37-47, 1989.
[48] Litterman, R. B. “A Bayesian Procedure for Forecasting with Vector Autoregression”, Working Paper, Massachusetts Institute of Technology, Dept. of Economics, 1980.
[49] Litterman, R. B. and Supel, T. M. “Using Vector Autoregressions to Measure the Uncertainty in Minnesota’s Revenue Forecasts”, Federal Reserve Bank of Minneapolis Quarterly Review, 7 (Spring), pp.10-22, 1983.
[50] Litterman, R. B. “Specifying Vector Autoregressions for Macroeconomic Forecasting”, Staff Report 92, Federal Reserve Bank of Minneapolis, Research Dept, 1984.
[51] Litterman, R. B. “How Monetary Policy in 1985 Affects the Outlook”, Quarterly Review - Federal Reserve Bank of Minneapolis, Minneapolis 9 (4), pp.2-14, 1985.
[52] Litterman, R. B. “Forecasting with Bayesian Vector Autoregressions – Five Years of Experience”, Journal of Business and Economic Statistics 4 (1), pp.25-38, 1986.
[53] Liu, T., Gerlow, M.E. and S.H. Irwin. “The performance of alternative VAR models in forecasting exchange rates”, International Journal of Forecasting 10, pp. 419-433, 1994.
[54] Lutkepohi, H. Introduction to Multiple Time Series Analysis. 2nd ed., Springer-Verlag, Berlin, 1993.
[55] Lupoletti, William M., and Webb, Roy H. “Defining and Improving the Accuracy of Macroeconomic Forecasts: Contributions From a VAR model”, Working Paper 84-6, Federal Reserve Bank of Richmond, 1984.
[56] Mathews, J.A. “A silicon valley of the east: Creating Taiwan’s semiconductor industry”. California Management Review, 39 (4), pp.26-54, 1997.
[57] Marchetti, D. J. and Parigi, G. “Energy Consumption, Survey Data and the Prediction of Industrial Production in Italy: A Comparison and Combination of Different Models”, Journal of Forecasting 19, pp.419-440, 2000.
[58] McNees, S.K “Forecasting accuracy of alternative techniques: A Comparison of U.S. macroeconomic forecasts”, Journal of Business and Economic Statistics, 4, pp.5-15, 1986.
[59] Nelson, H.L. & Granger, C.W.J. “Experience with using the Box-Cox transformation when forecasting economic time series”, Journal of Econometrics, 10, pp.57-69, 1979.
[60] Po-Hsuan Hsu, Chi-Hsiu Wang, Joseph Z. Shyu, Hsiao-Cheng Yu (2002). “A Litterman BVAR Approach for Production Forecasting of Technology Industries”. Technological Forecasting and Social Changes, 2002.
[61] Ravishanker, N and Ray B. K. “Bayesian Analysis of Vector ARMA Models using Gibbs Sampling”, Journal of Forecasting 16, pp.177-194, 1997.
[62] Rudebusch, Glenn D. “Do Measures of Monetary Policy in a VAR Make Sense?” International Economic Review, November 39, 4, pp.907-31, 1998.
[63] Sarantis, N. and Stewart, C, “Structural, VAR and BVAR Models of Exchange Rate Determination: A Comparison of Their Forecasting Performance”, Journal of Forecasting 14, pp.201-215, 1995.
[64] Schwarz, G. “Estimating the Dimension of A Model”, Annals of Statistics 6, pp.461-464, 1978.
[65] Simpson, P.W., Osborn, D.R., & Sensier, M. (2001). Forecasting UK industrial production over business cycle. Journal of Forecasting, 20, 405-424.
[66] Sims, C. A. “Macroeconomics and Reality”, Econometrica 48 (1), pp.1-48, 1980.
[67] Spencer, D. E. “Developing A Bayesian Vector Autoregression Forecasting Model”, International Journal of Forecasting 9, pp.407-421, 1993.
[68] Sturm, J., Jacobs, J., & Groote, P. “Output effects of infrastructure investment in the Netherlands”, 1853-1913. Journal of Macroeconomics, 21 (2), pp.355-380, 1999.
[69] Swann, P. & Prevezer, M. “A comparison of the dynamics of industrial clustering in computing and biotechnology”. Research Policy, 25, pp.1139-1157, 1996.
[70] Tiao, G. C. and A. Zellner. “On the Bayesian estimation of multivariate regression”, Journal of the Royal Statistical Society, B26, pp.389-99, 1964.
[71] Tseng, F., Tzeng G.., & Yu, H. “Fuzzy seasonal time series for forecasting the production value of the mechanical industry in Taiwan”. Technology Forecasting and Social Change, 60 (3), pp.263-273, 1999.
[72] Zellner, A. An Introduction to Bayesian Inference in Econometrics, John Wiley, New York, 1971.
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