TAILIEUCHUNG - Statistical study on modeling and forecasting of jute production in west Bengal, India

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 (In ' 000 Bales of 180 Kgs. each) by 2021. | Statistical study on modeling and forecasting of jute production in west Bengal, India

• Nông nghiệp
• Parametric regression model
• Auto regressive integrated moving average
• Normality test
• Jute production in west
• Time series parametric regression models
• Science and technology development
• Short term flood forecasting
• Amended semi parametric regression ensemble model
• The parametric component
• Multirecursive regression method
• Tạp chí khoa học
• Semi parametric model
• Back propagation method
• Cascade correlation algorithm
• Conjugate gradien
• Exponential fitting
• Nonparametric regression
• Normal distribution
• Parametric regression
• Wheat data for India