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Forecasting model of small scale industrial sector of West Bengal

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  • Bera, Soumitra Kumar
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    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.

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    Bibliographic Info

    Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 28144.

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    Date of creation: 20 Nov 2010
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    Handle: RePEc:pra:mprapa:28144

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    Keywords: Stationarity; ARIMA models; Forecasts;

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    1. 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.
    2. Fildes, Robert & Lusk, Edward J, 1984. "The choice of a forecasting model," Omega, Elsevier, vol. 12(5), pages 427-435.
    3. Fildes, Robert, 1992. "The evaluation of extrapolative forecasting methods," International Journal of Forecasting, Elsevier, vol. 8(1), pages 81-98, June.
    4. 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.
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