International Journal of Current Microbiology and Applied Sciences (IJCMAS)
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Original Research Articles                      Volume : 8, Issue:7, July, 2019

PRINT ISSN : 2319-7692
Online ISSN : 2319-7706
Issues : 12 per year
Publisher : Excellent Publishers
Email : /
Editor-in-chief: Dr.M.Prakash
Index Copernicus ICV 2018: 95.39
NAAS RATING 2020: 5.38

Int.J.Curr.Microbiol.App.Sci.2019.8(7): 1719-1730

Statistical Study on Modeling and Forecasting of Jute Production in West Bengal
Soumitra Sankar Das1*, Soumik Ray2, Abhishek Sen3, G. Samba Siva4 and Shantanu Das5
1Department of Agricultural Statistics & Computer Application, BAU, RAC, Kanke 834006, India
2Department of Agricultural Statistics, CUTM, Paralakhemundi, Gajapati,
Orisha 761211, India
3Department of Soil Science and Agricultural Chemistry, UBKV, Pundibari, West Bengal 736165, India
4ICAR-Central Research Institute for Dryland Agriculture, Hyderabad 500 059, India
5College of Agriculture, Tripura, Lembucherra 799210, India
*Corresponding author

Present investigation was an attempt to study the trend of jute production in West Bengal for the period starting from 1950 to 2016. For stochastic trend estimation, a number of time series parametric regression models viz. Linear model, Quadratic model, Exponential model, Logarithmic model, Power model and Auto Regressive Integrated Moving Average (ARIMA) were employed and compared for finding out an appropriate econometric model to capture the trend of jute production of the country. Based on the performance of several goodness of fit criteria viz. Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and R-squared values best fitted model was selected. The assumptions of ‘Independence’ and ‘Normality’ of error terms were examined by using the ‘Run-test’ and ‘Kolmogorov-Smirnov (K-S) test’ respectively. This study found ARIMA (1, 1, 2) as most appropriate to model the jute production of West Bengal. The forecasted value by using this model was obtained as 9149.22 (In ' 000 Bales of 180 Kgs. each) by 2021.

Keywords: Parametric regression model, Auto Regressive Integrated Moving Average (ARIMA), Normality test, Forecasting

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How to cite this article:

Soumitra Sankar Das, Soumik Ray, Abhishek Sen, G. Samba Siva and Shantanu Das. 2019. Statistical Study on Modeling and Forecasting of Jute Production in West Bengal.Int.J.Curr.Microbiol.App.Sci. 8(7): 1719-1730. doi:
Copyright: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike license.