IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/28144.html
   My bibliography  Save this paper

Forecasting model of small scale industrial sector of West Bengal

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/28144/1/MPRA_paper_28144.pdf
    File Function: original version
    Download Restriction: no

    References listed on IDEAS

    as
    1. Fildes, Robert & Lusk, Edward J, 1984. "The choice of a forecasting model," Omega, Elsevier, vol. 12(5), pages 427-435.
    2. 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.
    3. Fildes, Robert, 1992. "The evaluation of extrapolative forecasting methods," International Journal of Forecasting, Elsevier, vol. 8(1), pages 81-98, June.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Stationarity; ARIMA models; Forecasts;

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Lists

    This item is featured on the following reading lists or Wikipedia pages:
    1. Recognized plagiarism

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:28144. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Joachim Winter) or (Rebekah McClure). General contact details of provider: http://edirc.repec.org/data/vfmunde.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.