Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/25592
標題: 時間序列分析在台灣畜產價格之應用
The application of time series analysis on livestock prices in Taiwan
作者: 王雪潘
Saengwong, Sureeporn
關鍵字: Time series analysis;Time series analysis;Forecasting;Price relationship;Livestock sector;Taiwan;Forecasting;Price relationship;Livestock sector;Taiwan
出版社: 動物科學系所
引用: Chapter 1 Council of Agriculture. 2012.Food Supply and Utilization Yearbook.Council of Agriculture Executive Yuan, ROC. Gujarati, D. N. 2003. Basic econometrics.(4th Edition). McGraw Hill: New York. Huang, W., Z. Huang, M. Matei, and T. Wang. 2012. Price volatility forecast for agricultural commodity futures: the role of high frequency data. Romanian Journal of Economic Forecasting 4: 83-103. Chapter 2 Adachi, K., and D. J. Liu. 2009. Estimating long-run price relationship with structural change of unknown timing: an application to the Japanese pork market. American Journal Agricultural Economics 91(5): 1440-1447. Anderson, L., R. A. Babula, H. Hartmann, and M. M. Rasmussen. 2007. A time series analysis of Danish markets for pork, chicken, and beef. ActaAgriculturaeScandinavica, Section C-Economy 4(2): 103-118. Assis, K., A.Amran, Y.Remali, and H. Affendy. 2010.A comparison of univariate time series methods for forecasting cocoa bean prices.Trends in Agricultural Economics 3(4): 207-215. Bakucs, L. Z., and I. Ferto. 2005. Marketing margins and price transmission on the Hungarian pork meat market. Agribusiness 21(2): 273-286. Chang, C. L, B. W. Huang, M. G. Chen, and M. McAleer. 2010. Modelling the asymmetric volatility in hog prices in Taiwan: The Impact of Joining the WTO. WORKING PAPER. Chang, C. L., B. W. Huang, M. G. Chen, and M. McAleer. 2011. Modelling the asymmetric volatility in hog prices in Taiwan: the impact of joining the WTO. Mathematics and Computers Simulation 81(7): 1491-1506. Council for Economic Planning and Development. 2012. Taiwan Statistical Data Book. Council for Economic Planning and Development,Executive Yuan, ROC. Council of Agriculture. 2011. Taiwan Pig Production Statistics. Council of Agriculture, Executive Yuan, ROC. Council of Agriculture. 2012.Food Supply and Utilization Yearbook.Council of Agriculture Executive Yuan, ROC. Gjolberg, O., and B. A. Bengtsson. 1997. Forecasting quarterly hog prices: simple autoregressive models vs. naive predict. Agribusiness 13(6): 673-679. Hatane, S. E. 2011a. The predictability of GARCH-type models on the returns volatility of primary Indonesian exported agricultural commodities. JurnalAkuntansi Dan Keuangan 13(2): 87-97. Hatane, S. E. 2011b. GARCH-type models on the volatility of Indonesian cocoa’s spot price returns. JurnalManajemen Dan Kewirausahaan 13(2): 117-123. Huang, B. W., M. G. Chen, C. L. Chang, and M. McAleer. 2009. Modelling risk in agricultural finance: application to the poultry industry in Taiwan. Mathematics Computers in Simulation 79(5): 1472-1487. Huang, W., Z. Huang, M. Matei, and T. Wang. 2012. Price volatility forecast for agricultural commodity futures: the role of high frequency data. Romanian Journal of Economic Forecasting 4: 83-103. Jordaan, H., B. Grove, A. Jooste,and Z.G. Alemu.2007. Measuring the Price Volatility of Certain Field Crops in South Africa using the ARCH/GARCH Approach.Agrekon46(3): 306-322. Kuwornu, J.K.M., A.M. Bonsu,and H. Ibrahim. 2011. Analysis of Foodstuff Price Volatility in Ghana: Implications for Food Security. European Journal of Business and Management 3(4): 100-118. Liu, Q. F. 2005. Price relations among hog, corn, and soybean meal futures. Journal of Futures Markets 25(5): 491-514. Shih, M. L., B. W. Huang, N. H. Chiu, C. Chiu, and W. Y. Hu. 2009. Farm price prediction using case-based reasoning approach- a case of broiler industry in Taiwan. Computer and Electronics in Agriculture 66(1): 70-75. Tsai, R., and G. W. Williams. 1993. Taiwanese Livestock and FeedgrainIndustries. Chapter 3 Council of Agriculture. 2011. Taiwan Pig Production Statistics. Council of Agriculture, Executive Yuan, ROC. Dickey, D., and W. A. Fuller. 1979. Distributions of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association 74(366): 1057-1072. Dickey, D., and W. A. Fuller. 1981. Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica 49(4): 1057-1072. Engle, R. F., and C. W. J. Granger. 1987. Cointegration and error correction: Representation, estimation and testing. Econometrica 55(2): 251-276. Johansen, S. 1988. Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control 12(2-3): 231-254. Johansen, S. 1991. Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica 59(6): 1551-1580. Johansen, S., and K. Juselius.1990. Maximum likelihood estimation and inference on cointegration with application to the demand for money. Oxford Bulletin of Economics & Statistics 52(2): 169-209. National Animal Industry Foundation. 2007. Taiwan pig production statistics.National Animal Industry Foundation. Phillips, P. C. B., and P. Perron.1988. Testing for a unit root in time series regression. Biometrika 75(2): 335-346. USDA.2009. Taiwan: grain and feed annual: corn, wheat, rice situation & Outlook. GAIN Report Number: TW9031. USDA Foreign Agricultural Service. Chapter 4 Adachi, K., and D.J. Liu. 2009. Estimating long-run price relationship with structural change of unknown timing: an application to the Japanese pork market. American Journal Agricultural Economics 91(5): 1440-1447. Anderson, L., R.A. Babula, H. Hartmann, and M.M. Rasmussen. 2007. A time series analysis of Danish markets for pork, chicken, and beef. ActaAgriculturaeScandinavica, Section C-Economy 4(2): 103-118. Bakucs, L.Z., and I. Ferto. 2005. Marketing margins and price transmission on the Hungarian pork meat market. Agribusiness 21(2): 273-286. Box, G.E.W., and G.M. Jenkins. 1970. Time Series Analysis: Forecasting and Control, Holden Day: San Francisco. Chang, C.L., B.W. Huang, M.G. Chen, and M. McAleer. 2011. Modelling the asymmetric volatility in hog prices in Taiwan: the impact of joining the WTO. Mathematics and Computers Simulation 81(7): 1491-1506. Council of Agriculture. 2011. Searching System for Animal Products. Available from URL: http://agrapp.coa.gov.tw/agrPR-net/index.htm [accessed 25 November, 2011]. Dickey, D.A., and W.A. Fuller. 1979. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association 74(366): 427-431. Dickey, D.A., and W.A. Fuller. 1981. Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica 49(4): 1057-1072. Engle, R.F., and C.W.J. Granger. 1987. Co-integration and error correction: representation, estimation, and testing. Econometrica 55(2): 251-276. Gjolberg, O., and B.A. Bengtsson. 1997. Forecasting quarterly hog prices: simple autoregressive models vs. naive predict. Agribusiness 13(6): 673-679. Huang, B.W., M.G. Chen, C.L. Chang, and M. McAleer. 2009. Modelling risk in agricultural finance: application to the poultry industry in Taiwan. Mathematics Computersin Simulation 79(5): 1472-1487. Johansen, S. 1988. Statistical analysis of cointegration vectors.Journal of Economic Dynamics and Control 12(2-3): 231-254. Johansen, S. 1991. Estimation and hypothesis testing of cointegration vectors in gaussian vector autoregressive models.Econometrica 59(6): 1551-1580. Johansen, S., and K. Juselius. 1990. Maximum likelihood estimation and inference on cointegration-with applications to the demand for money. Oxford Bulletin of Economics & Statistics 52(2): 169-209. Liu, Q.F. 2005. Price relations among hog, corn, and soybean meal futures.Journal of Futures Markets 25(5): 491-514. MacKinnon, J.G. 1996. Numerical distribution functions for unit root and cointegration tests.Journal of Applied Econometrics 11(6): 601-618. MacKinnon, J.G., A.A. Haug, and L. Michelis. 1999. Numerical distribution functions of likelihood ratio tests for cointegration. Journal of Applied Econometrics 14(5): 563-577. National Animal Industry Foundation. 2010. Taiwan Pig Production Statistics, National Animal Industry Foundation. Pesaran, M.H., and Y. Shin. 1998. Generalized impulse response analysis in linear multivariate models. Economics Letters 58(1): 17-29. Phillips, P.C.B., and P. Perron. 1988. Testing for a unit root in time series regression. Biometrika 75(2): 335-346. Shih, M.L., B.W. Huang, N.H. Chiu, C. Chiu, and W. Y. Hu. 2009. Farm price prediction using case-based reasoning approach- a case of broiler industry in Taiwan. Computer and Electronics in Agriculture 66(1): 70-75. USDA. 2009. Taiwan: Grain and Feed Annual: Corn, Wheat, Rice Situation & Outlook. GAIN Report Number: TW9031, USDA Foreign Agricultural Service. Chapter 5 Assis, K., A.Amran, Y.Remali, and H. Affendy. 2010.A comparison of univariate time series methods for forecasting cocoa bean prices.Trends in Agricultural Economics 3(4): 207-215. Asteriou, D., and S. G. Hall.2007.Applied Econometrics: A Modern Approach Using EViews and Microfit.Revised ed. Palgrave Macmillan: New York. Bollerslev, T. 1986. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31: 307-327. Box, G. E. P., and G. M. Jenkins.1970.Time Series Analysis, Forecasting and Control. Holden Day: San Francisco. Council of Agriculture. 2012. Searching System for Animal Products. Available from URL: http://agrapp.coa.gov.tw/agrPR-net/index.htm [accessed 25 July, 2012]. Dickey, D., and W. A. Fuller. 1979. Distributions of the estimators for autoregressive time series with a unit root.Journal of the American Statistical Association 74: 427-431. Dickey, D., and W. A. Fuller. 1981. Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica 49: 1057-1072. Enders, W. 2004.Applied Econometric Time Series. (2nd Edition), John Wiley & Sons Inc: New York. Engle, R. F. 1982. Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. Inflation.Econometrica50: 987-1008. Engle, R. F., D. M. Lilien, and R. P. Robins. 1987. Estimating time varying risk premia in the term structure: The ARCH-M model. Econometrica55: 391-407. Granger, C.W.J., and P. Newbold.1974. Spurious regressions in econometrics. Journal of Econometrics 2:111-120. Hatane, S.E. 2011a. The predictability of GARCH-type models on the returns volatility of primary Indonesian exported agricultural commodities.JurnalAkuntansi Dan Keuangan 13(2): 87-97. Hatane, S.E. 2011b. GARCH-type models on the volatility of Indonesian cocoa’s spot price returns. JurnalManajemen Dan Kewirausahaan 13(2): 117-123. Huang, W., Z. Huang, M. Matei,and T. Wang.2012. Price volatility forecast for agricultural commodity futures: the role of high frequency data. Romanian Journal of Economic Forecasting 4: 83-103. Jordaan, H., B. Grove,A. Jooste,and Z.G. Alemu.2007. Measuring the Price Volatility of Certain Field Crops in South Africa using the ARCH/GARCH Approach.Agrekon46(3): 306-322. Kuwornu, J.K.M., A.M. Bonsu,and H. Ibrahim. 2011. Analysis of Foodstuff Price Volatility in Ghana: Implications for Food Security. European Journal of Business and Management 3(4): 100-118. Lim, C.,and G.W. Pan.2005. Inbound tourism developments and patterns in China. Mathematics and Computers in Simulation68: 499-507. MacKinnon, J.G. 1996. Numerical distribution functions for unit root and cointegration tests. Journal of Applied Econometrics 11(6): 601-618. Ping, C.L. 2013. Typhoons, Culture, and Engineering in Taiwan.Agroborealis43:6-13. Schaffer, H. D., D. B. Hunt, and D. E. Ray.2007. U.S. Agricultural commodity policy and its relationship to obesity. Background Paper Developed for the Wingspread Conference on Childhood Obesity, Healthy Eating & Agriculture Policy Racine, Wisconsin.
摘要: 
ABSTRACT
Livestock sector in Taiwan plays important role in the one part of agricultural commodity, especially hog, broiler, cattle, and duck. Nowadays livestock farmers in Taiwan are very good producers, highly diligent, and have advanced feeding and breeding skills but they often lack of skills to make a good marketing plan.Because of agricultural commodity prices are high uncertainty (volatility) and difficult to predict, producers have to understand the characteristics and commodity prices for making decision to plan their production. The purposes of this dissertationareto study the relationships and to predict the future trends of Taiwan’s livestock products using time series analysis as follows these three chapters.
The study in Chapter 3 attempts to investigate the causal relationship between hog and feedstuff prices in Taiwan by using monthly time series over the period of January 2000 to October 2010. The prices from hog, feedstuff, soybean meal andcorn are considered performing through a multivariate vector autoregressive (VAR) model. As the empirical results, the long-run equilibrium is captured identifying for price elasticity among the variables through the Johansen cointegration diagnosis. Then, the Granger causality approach shows that bidirectional relationship is detected running from feedstuff to corn and from soybean meal to hog as well as unidirectional relationship running from corn to hog, from feedstuff to hog and from soybean meal to feedstuff.
The study in Chapter 4 attempts to model the possible cointegration of price elasticity, demonstrate the causality for a directional relationship and forecast the future prices of broiler, cattle, duck and hog in Taiwan by using time series analyses, such as the unit root, Johansen cointegration, Granger causality and variance decomposition tests. The Johansen cointegration test indicated significant price elasticity among the variables. The long-run Granger causality test showed that a bidirectional relationship exists between hog and broiler prices and that a unidirectional relationship exists from the duck price to the hog price. The autoregressive integrated moving average (ARIMA) and variance decomposition methods were used to predict the future livestock price and the riskiness of shocks in a future 12-month period.
The purpose of study in Chapter 5 is to investigate the performances of future livestock prices in Taiwan, namely broiler, cattle, duck and hogover the period of January 1993 to December 2011. Using single time series analysis, the combination of autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity in mean (GARCH-M) is performed to detect the future value included its own risks. The results show that the prices of broiler, cattle and hog are appropriate for using ARIMA-GARCH-M because the variation of these three variables can be detected for performing the volatility approach, except duck price using ARIMA with no volatility function. The price volatility forecasting analysis, therefore, indicates that broiler and cattle farmers are carried out the risks for selling their own products. In the case of hog price, the riskiness is transmitted to the markets.

Keywords: Time series analysis, Forecasting, Price relationship, Livestock sector, Taiwan
URI: http://hdl.handle.net/11455/25592
其他識別: U0005-2406201317375700
Appears in Collections:動物科學系

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