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Study on Modeling and Forecasting of Tobacco Production in India
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The paper describes an empirical study of modeling and forecasting time series data of tobacco production in India. Yearly tobacco production data for the period of 1950-1951 to 2014-2015 of India were analyzed by time-series methods. Autocorrelation and partial autocorrelation functions were calculated for the data. The Box Jenkins ARIMA methodology has been used for forecasting. The diagnostic checking has shown that ARIMA (1, 1, 1) is appropriate. The forecasts from 2015-2016 to 2019-2020 are calculated based on the selected model. The forecasting power of autoregressive integrated moving average model was used to forecast tobacco production for five leading years. These forecasts would be helpful for the policy makers to foresee ahead of time the future requirements of tobacco production, import and/or export and adopt appropriate measures in this regard.
Keywords
ACF - Autocorrelation Function, ARIMA - Autoregressive Integrated Moving Average, Forecast, PACF - Partial Autocorrelation Function, Tobacco.
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- Bartlett, M.S. (1964). On the theoretical specification of sampling properties of autocorrelated time series. J. Roy. Stat. Soc., B 8 : 27–41.
- Box, G.E.P. and Jenkins, G. M. (1970). Time series analysis: forecasting and control, Holden Day, San Francisco, CA.
- Box, G.E.P. and Jenkins, G.M. (1976). Time series analysis: Forecasting and control. Rev. Ed. San Francisco. Holden-Day.
- Brockwell, P.J. and Davis, R. A. (1996). Introduction to time series and forecasting, Springer.
- Brown, R.G. (1959). Statistical forecasting for inventory control. McGraw-Hill, NEW YORK, U.S.A.
- Holt, C.C., Modigliani, F. , Muth, J.F. and Simon, H.A. (1960). Planning, production, inventores and work force. Prentice Hall, Englewood Cliffs, NJ, U.S.A.
- Iqbal, N., Bakhsh, K., Maqbool, A. and Ahmad, A.S. (2005). Use of the ARIMA Model for forecasting wheat area and production in Pakistan. J. Agric. & Soc. Sci., 2 : 120-122.
- Jenkins, G. M. and Watts, D.G. (1968). Spectral analysis and its application, day, San Francisco, California, USA.
- Kendall, M. G. and Stuart, A. (1966). The advanced theory of statistics. Vol. 3. Design and Analysis and Time-Series. Charles Griffin & Co. Ltd.,LONDON, UNITED KINGDOM.
- Ljunge, G.M. and Box, G.E.P. (1978). On a measure of lack of fit in time series models. Biometrika, 65 : 67–72.
- Makridakis, S., Anderson, A., Filds, R., Hibon, M., lewandowski, R., Newton, J., Parzen, E. and Winkler, R. (1982). The accuracy of extrapolation (time series) methods: Results of a forecasting competition, J. Forecasting Competition. J. Forecasting, 1: 111–153.
- Meese, R. and Geweke, J. (1982). A comparison of autoregressive univariate forecasting procedures for macroeconomic time series. Manuscript, University of California, Berkeley, CA, U.S.A.
- Muhammad, F., Javed, M. S. and Bashir, M. (1992). Forecasting sugarcane production in Pakistan using ARIMA Models, Pak. J. Agric. Sci., 9(1): 31-36.
- Prindycke, R.S. and Rubinfeld, D.L. (1981).Econometric models and economic forecasts, 2nd Ed. McGraw-Hill, NEW YORK, U.S.A.
- Quenouille, M.H. (1949). Approximate tests of correlation in time- series. J. Roy. Stat. Soc., B11: 68–84.
- Saeed, N., Saeed, A., Zakria, M. and Bajwa, T. M. (2000). Forecasting of wheat production in Pakistan using ARIMA models, Internat. J. Agric. & Biol., 4: 352-353.
- Yule, G.U. (1926). Why do we sometimes get nonsencecorrleations between times deries. A study in sampling and the nature of series. J. Roy. Stat. Soc., 89: 1–69.
- Yule, G.U. (1927). On a method of investigation periodicities in disturbed series, with specia; Reference To Wolfer’s Sunspot Number. Phill. Trans., A 226 : 267–98.
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