Forecasting model of small scale industrial sector of West Bengal

Bera, Soumitra Kumar (2010): Forecasting model of small scale industrial sector of West Bengal.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Stationarity, ARIMA models, Forecasts
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes

References

Afzal Mohammad (et al.). 2002. Forecasting: A dilemma of modules. Pakistan Economic and Social Review 40(1): 1-18.

Armstong, J. S. 1983. Relative accuracy of judgmental and extrapolative methods in forecasting annual earnings. Journal of Forecasting. 15(2): 437–447.

Armstrong J. S. 2005. The forecasting canon: nine generalizations to improve forecast accuracy. Foresight 1(1): 49- 65.

Armstrong J. S. 2006. Findings from evidence-based forecasting: methods for reducing forecast error. Forthcoming (after revisions) in the International Journal of Forecasting.

Bartlett, M. S. 1946. On the theoretical specification of sampling properties of autocorrelated time series. Journal of Royal Statistical Society 8(27).

Bawa, R. S. and G. S. Kainth .1980. A time series analysis of net national product of India, Margin 12(3):51 – 80.

Bhatia G. S. 1999. The impact of new economic policy on output and employment in manufacturing sector: a case study of West Bengal in V. S. Mahajan (ed.), Economic Reforms and

Liberalization, New Delhi: Deep & Deep. Bowersox J. Donald, (et al.). 1981. Simulated product sales forecasting: A model for short-range forecasting operational decision making. Research in Marketing 4(2): 39-68.

Bowersox J. Donald, (et al.). 1981. Simulated product sales forecasting: A model for short-range forecasting operational decision making. Research in Marketing 4(2): 39-68.

Box, G. E. P. and D. A. Pierce .1970. Distribution of Residual Autocorrelations in ARIMA Time Series Models. Journal of American Statistical Association, 65(4): 1509 – 1526.

Box, G. E. P., G. M. Jenkins, and G. C. Reinsell.1994 .Time series analysis: Forecasting and control, Englewood Cliffs,

N. J.: Prentice – Hall. Box, G. E. P., G. M. Jenkins.1968. Some Recent Advance in Forecasting and Control. Applied Statistics: 91 – 109.

DeLurgio. 1998. Forecasting Principles and Application. New York: McGraw-Hill International Edition.

Fildes Robert, M. Makridakis (et al.). 1998. Generalizing about univariate forecasting methods: Further empirical evidence. International Journal of Forecasting 14: 339–358.

Fildes, R. 1992. The evaluation of extrapolative forecasting method. International Journal of Forecasting 8(1): 81 – 98.

Fildes, R. and E. J. Lusk. 1984. The Choice of a Forecasting Model. Omega 12(5): 427–435.

Fildes, R. and S. Makridakis. 1998. Forecasting and loss function. International Journal of Forecasting 4(3): 545–550.

Fildes, R. Makridakis, S. 1995. The impact of empirical accuracy studies on time series analysis and forecasting. International Statistical Review 63: 289–308.

Francis X. Diebold and Glenn P. Rudebusch.1991. Forecasting output with the composite leading index: a real time analysis. Journal of American Statistical Association. 86(4): 603-610.

Gupta, Sanjeev and R. S. Bawa. 2002. An analysis of long -term trends and forecasts of oilseeds output in India.

Indian Journal of Quantitative Economics 17(1-2): 111-131. Gupta, Sanjeev and R. S. Bawa. 2004. Growth performance and sales forecasts of two-wheeler industry in India. Indian Journal of Applied Economics 1(1): 158-165.

Gupta, Sanjeev and R. S. Bawa. 2006. Growth performance and sales forecasts of automobile industry in India. Forthcoming in the Indian Journal of Quantitative Economics 21(1-2).

Gupta, Sanjeev. 2003. Forecasting of Agricultural Output in India. New Delhi: Saloni Publishing House.

Gupta, Sanjeev and R. S. Bawa. 2006. Growth performance of small scale industry in West Bengal: A comparative study of pre–liberalization & liberalization periods Apeejay Journal of Management & Technology. 1(1): 50-55

Hanke, J. E. (et al.). 2001. Business Forecasting. New Delhi: Pearson Education. Ibrahim, I. B, and T, Otsuki.1982. Forecasting GNP components using the method of Box and Jenkins. Southern Economic Journal: 461 – 470.

Kumar Naresh and Balraj Singh .2003. Forecasts of Indian Automobile Industry Using Mathematical Models, Paradigm 3(2):105-116.

Ljung G. M. and G. E. P. Box. 1978. On measurement of lack of fit in time series models. Biometrika. 65: 67–72.

Makridakis, S. (et al.). 1984. The Forecasting Accuracy of Major Time Series Methods. Chichester: John Wiley.

Makridakis, S. and S. C. Wheelwright. 1987. The Handbook of Forecasting: a Manager’s Guide New York: John Wiley.

Makridakis, S., and S. C. Wheelwright, and R. J. Hyndman. 1998. Forecasting: Methods and Applications New York: John Wiley & Sons.

Makridakis, S., and Wheelwright, S. C. 1978. Interactive Forecasting: Univariate and Multivariate Methods San Francisco CA: Holden-Day.

Mentzer John T. and James E. Cox. 1984. Familiarity, application, and performance of sales forecasting techniques. Journal of Forecasting 3(1): 276-278.

Mentzer John T. and Keneneth B Kah. 1995. Forecasting in consumer and industrial markets. Journal of Business Forecasting: 21-28.

Nachane, D. M. (et. al). 1981. Forecasting freight and passenger traffic on Indian railways: generalized adaptive filtering approach. The Indian Economic Journal 29(2): 99-116.

Newbold, P. and C. W. J. Granger. 1974. Experience with forecasting principles abstract of handbook article forecasting univariate time series and the combination of forecasts. Journal of Royal Statistics Society 137(3): 131–165.

Pankratz, A. 1983. Forecasting with Univariate Box-Jenkins Models: Concepts and Cases New

Ramaswamy, K. V. 1994. Small-scale manufacturing industries-some aspects of size, growth and structure. Economic and Political Weekly 29(9): 13 -22.

Sabia, J. L. M. 1977. Autoregressive integrated moving average (ARIMA) model for birth forecasting. Journal of the American Statistical Association 72(354):264 – 270.

Sethi, A. S. 1999. Forecasting savings in India in post - liberalization scenario: a note. Indian Journal of Quantitative Economics, 14(2): 159 – 166