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Forecasting model of small scale industrial sector of West Bengal

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  • Bera, Soumitra Kumar

Abstract

This study seeks to generate the forecasts for the small scale industrial sector of West Bengal for the ensuing decade till 2019-20. Forecasts have been generated for production, direct employment, capital formation and number of units in this sector. Auto Regressive Integrated Moving Average (ARIMA) model has been used taking the lead time of 13 years. The analysis of forecasted figures has revealed that the fixed capital investment and production would experience significant growth during the lead time of thirteen years. Number of units and employment are expected to observe meager growth during this period indicating low possibility of absorption of labor force in this sector. In the light of the forecasts, it is required on the part of the state government to take all concerted efforts and initiatives to strengthen the industrial base in West Bengal. In this regard catastrophic changes are required so far as industrial policy of West Bengal is concerned.

Suggested Citation

  • Bera, Soumitra Kumar, 2010. "Forecasting model of small scale industrial sector of West Bengal," MPRA Paper 28144, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:28144
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    References listed on IDEAS

    as
    1. Fildes, Robert & Hibon, Michele & Makridakis, Spyros & Meade, Nigel, 1998. "Generalising about univariate forecasting methods: further empirical evidence," International Journal of Forecasting, Elsevier, vol. 14(3), pages 339-358, September.
    2. Fildes, Robert & Lusk, Edward J, 1984. "The choice of a forecasting model," Omega, Elsevier, vol. 12(5), pages 427-435.
    3. J. Scott Armstrong, 2005. "The Forecasting Canon: Nine Generalizations to Improve Forecast Accuracy," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 1, pages 29-35, June.
    4. Fildes, Robert, 1992. "The evaluation of extrapolative forecasting methods," International Journal of Forecasting, Elsevier, vol. 8(1), pages 81-98, June.
    5. Armstrong, J. Scott, 2006. "Findings from evidence-based forecasting: Methods for reducing forecast error," International Journal of Forecasting, Elsevier, vol. 22(3), pages 583-598.
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    More about this item

    Keywords

    Stationarity; ARIMA models; Forecasts;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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