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Forecasting of Boro Rice Production in Bangladesh: an Arima Approach

N. M. F. Rahman
Department of BBA, Mirpur University College, Dhaka

Abstract/ Executive Summary:

The study was undertaken to examine the best fitted ARIMA model that could be used to make efficient forecast boro rice production in Bangladesh from 2008-09 to 2012-13. It appeared from the study that local, modern and total boro time series are 1st order homogenous stationary. It is found from the study that the ARIMA (0,1,0) ARIMA (0,1,3) and ARIMA (0,1,2) are the best for local, modern and total boro rice production respectively. It is observed from the analysis that short term forecasts are more efficient for ARIMA models. The production uncertainty of boro rice can be minimizing if production can be forecasted well and necessary steps can be taken against losses. The government and producer as well use ARIMA methods to forecast future production more accurately in the short run.

Keywords: Production, ARIMA model, Forecasting

Location: Department of BBA, Mirpur University College, Dhaka

Start Date:

End Date:

Program Area: Socio-economic and Policy

Commodity: Rice

Objectives:

To develop appropriate ARIMA models for the time series of local, modern and total boro rice production in Bangladesh and to make five year forecasts for all the time series with appropriate prediction interval.

Methodology:

To achieve the stipulated objectives, the present study has been carried out on the basis of local, modern and total boro rice production data pertaining the period 1967-68 to 2007-08, which were collected from secondary (BBS various issues) source. The time series data of local, modern and total boro rice production were modeled by Box-Jenkins type stochastic autoregressive integrated moving average (ARIMA) process. The Box-Jenkins type ARIMA process (Box and Jenkins, 1978) can be defined as t t φ (B)(Δd y − μ ) =θ (B)ε , Here, t y denotes local, modern and total boro rice production million metric tons, μ is the mean of t ΔdY , φ (B) = p p−φ1B −L−φ B 1 , θ (B) = q q−θ1B −L−θ B 1 , i θ denotes the ith moving average parameter, i φ denotes the ith autoregressive parameter, p , q and d denote the autoregressive, moving average and difference order of the process respectively, Δ and B denote the difference and back-shift operators respectively. The estimation methodology of the above model consists of three steps, namely, identification, estimation of parameters and diagnostic checking. The identification step involves the use of the techniques for determining the value of p, d and q. Here, these values are determined by using autocorrelation and partial autocorrelation functions (ACF and PACF) and Augmented Dickey-Fuller (ADF) test. The model used for ADF is 1 1 1 ( 1) − − + Δ − − = + + − t t yt y y t y λ α β ρ (Pindyck and Rubinfeld, 1991, p.461).The second step is to estimate the parameters of the model. Here, the method of maximum likelihood is used for this purpose. The third step is to check whether the chosen model fits the data reasonably well. For this reason the residuals are examined to find out if they are white noise. To test if the residuals are white noise the ACF of residuals and the Ljung and Box (1978) statistic are used. In case of two or more competing models passing the diagnostic checks the best fitted model is selected using the following criteria multiple R2, Adjusted R2 , Root mean squared error (RMSE), Akaike Information Criterion (AIC), Bayesian Information (BIC), Mean absolute error (MAE) and Mean absolute proportion percent error (MAPPE).

Source of Information: J. Bangladesh Agril. Univ. 8(1): 103–112, 2010 ISSN 1810-3030

Article Link (Online Journal):

Funding Source:

Total Budget (Tk.) :

Achievement/Findings:

A time series model accounts for patterns in the past movement of a variable and uses that information to predict its future movements. In a sense a time series model is just a sophisticated model of extrapolation. Time series data have become very popular to be intensively used in empirical research and econometricians have recently begun to pay very careful attention to such data. To select the best model for a particular time series the latest available model selection criteria are used. The study revealed that ARIMA (0,1,0), ARIMA (0,1,3) and ARIMA (0,1,2) models are appropriate for local, modern and total boro rice production in Bangladesh respectively and it is to be noted that the short-term forecast is better as the error of forecast increases with the increase of the period of forecast.

Knowledge Management: Journal