Agricultural Research Management Information System

Home
Research Summary
Search
About Us
- ARMIS
- Brochure
Contact Us
Report
User Request
Data Input
Help
- Operation Manual
  - PDF
  - Video
- Program Area & Commodity
We have reached 37600 number of research entries at this moment.

- Logout

Research Detail

Home
Research
Detail

Forecasting Wholesale Price of Coarse Rice in Bangladesh: a Seasonal Autoregressive Integrated Moving Average Approach

Mr.Md. Fuad. Hassan
Lecturer
Department of Agricultural Statistics, Bangladesh Agricultural University, Mymensingh,Bangladesh, *E-mail: fuad_bau016@yahoo.com

Abstract/ Executive Summary:

This article attempts to develop the model and to forecast the wholesale price of coarse rice in Bangladesh. Seasonal Autoregressive Integrated Moving Average (SARIMA) models have been developed on the monthly data collected from July 1975 to December 2011and validated using the data from December 2010 to December 2011. The results showed that the predicted values were consistent with the upturns and downturns of the observed series. The model with non seasonal autoregressive 1, difference 1 and moving average 1 and seasonal difference 1 and moving average 1 that is SARIMA (1,1,1)(0,1,1)₁₂ model has been found as the most suitable model with least Root Mean Square Error (RMSE) of 61.657, Normalised Bayesian Information Criteria (BIC) of 8.300 and Mean Absolute Percent Error (MAPE) of 3.906. The model was further validated by Ljung-Box test (Q18=17.394 and p>.20) with no significant autocorrelation between residuals at different lag times. Finally, a forecast for the period January 2012 to December 2013 was made.

Keywords: Coarse Rice, Forecasting, Seasonal autoregressive Integrated Moving Average

Location: Mymensingh

Start Date:

End Date:

Program Area: Socio-economic and Policy

Non-commodity: Analysis and interpretations

Objectives:

To develop the model and to forecast the wholesale price of coarse rice in Bangladesh.

Methodology:

Data collection: The monthly data of the wholesale prices of coarse rice (Tk. per quintal) from the year July 1975 to December 2011 have been collected from Department of Agricultural Marketing, Food Planning and Monitoring Unit (FPMU) of the Ministry of Food System Management Division of Bangladesh. Notations of SARIMA model: The popular Box-Jenkins methodology was applied to fit best seasonal autoregressive integrated moving average model SARIMA (p,d,q)(P,D,Q)_S where p is the order of autoregression, d is the order of integration, q is the order of moving average, P is the order of seasonal autoregression, D is the order of seasonal integration, Q is the order of seasonal moving average and s is the length of seasonal period for forecasting coarse rice price of Bangladesh. Data analysis: Accuracy of forecasting depends on the time series data which must be stationary. The stationary of the series was made by means of seasonal and non-seasonal differencing. Then the order of autoregression and moving average were identified using autocorrelation function (ACF) and partial autocorrelation function (PACF) of the differenced series. A best ARIMA model was fitted for a set of coarse rice price data from July 1975 to December 2011 and the fitted model was used to predict values for a validation period (from December 2010 to December 2011) to evaluate the time series model. Most suitable models were selected on the basis of their ability of reliable prediction. Three measures, namely, Root Mean Square Error (RMSE), Normalised Bayesian Information Criteria (BIC) (Makridakis et al. 1998) and Mean Absolute Percent Error (MAPE) were used (Schwarz et al. 1978). Lower values of RMSE, Normalised BIC and MAPE were preferable. Furthermore, Ljung-Box test (portmanteau test) was performed to test if the residual ACF at different lag times was significantly different from zero, where not being different from zero was expected. After the best model was identified, forecast for future values from January 2012 to December 2013 was made.

Source of Information: J. Bangladesh Agril. Univ. 11(2): 271–276, 2013, ISSN 1810-3030

Article Link (Online Journal):

Funding Source:

1. Government

Budget:

Total Budget (Tk.) :

Achievement/Findings:

Seasonal auto regressive integrated moving average model traces out the seasonal effect of the desired variable. This research identified SARIMA (1,1,1) (0,1,1)₁₂ as the best model for forecasting on the basis of latest model selection criteria. On an average the wholesale price of rice in Bangladesh follows an upward trend. This study forecast some future prices which would help the policy planners to take necessary action for the time to come. Basically forecasting accuracy relies on accuracy of the data on the interested variables. Data banks in our country should be well organized to get the best result by the forecasting models. For forecasting purpose mixed regression model i.e. considering various independent variables (political stability and import-export gap etc.) with basic SARIMA can be used.

Knowledge Management: Journal