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Modelling and Forecasting the Consumer Price Index in Bangladesh Through Econometric Models

Md. Shahajada Mia
Department of Statistics, Pabna University of Science and Technology, Pabna-6600, Pabna, Bangladesh

A H M Musfiqur Rahman Nabeen
Department of Statistics, Pabna University of Science and Technology, Pabna-6600, Pabna, Bangladesh

Mst. Masrufa Akter
Department of Economics, University of Rajshahi, Rajshahi-6205, Rajshahi, Bangladesh

Abstract/ Executive Summary:

Persistent economic growth along with high Consumer Price Index (CPI) and low inflation is the major aim of the economic theory. This paper uses annual time series data on CPI from the period 1986 to 2018 and find the best econometric time series model for forecasting the CPI in Bangladesh. In this study different Autoregressive integrated moving average (ARIMA) model are used. To find the best ARIMA model we have used here Akaike information criteria (AIC), corrected Akaike information criteria (AICc) and Bayesian information criteria (BIC). This study presents ARIMA (2, 2, 0) model to forecast the CPI in Bangladesh based on the lowest values of AIC, AICc and BIC than other ARIMA models. Based on the selected ARIMA (2, 2, 0) model we forecast the CPI in Bangladesh from period 2019 to 2025. The results of the study show that the CPI in Bangladesh is to continue an upward trend with respect to time.

Keywords: Forecasting, CPI, Inflation rate, Box-Jenkins method, ARIMA models, Consumer Price Index

Location: All over Bangladesh

Start Date: 00-00-1986

End Date: 00-00-2018

Program Area: Socio-economic and Policy

Non-commodity: Price behavior

Objectives:

To find out the best econometric time series model for forecasting the CPI in Bangladesh.

Methodology:

Data Source: Data used in this study were records of Consumer Price Index (CPI) of Bangladesh for the period from 1986 to 2018 from the World Bank. Here 2010 is the base year whose index is 100. Autoregressive Integrated Moving Average Model: The model used in this study is the Autoregressive integrated moving average (ARIMA). The integer’s p and q are referred to as order of autoregressive and moving average respectively. Random error term are assumed to be independently and identically distributed (i.i.d) with mean zero and constant variance a². Box-Jenkins ARIMA Approach: For estimating the ARIMA model the three stages of modeling as suggested by Box and Jenkins namely identification, estimation and diagnostic checking were undertaken. For model identification, at first we test the stationary of the original data by using time series plot, autocorrelation function (ACF), and unit root test. If the series is not stationary then we needs to difference of the series until to get stationary. After the stationarity of the time series was attained, ACF and PACF (partial autocorrelation function) of the stationary series are employed to select the order of the Autoregressive (AR) process and the order of the Moving Average (MA) process of the ARIMA model. Integrated (I) process, which accounts for stabilizing or making the data stationary. Estimation of the model was done by the least square method. In the diagnostic checking phase the model residual analysis was performed. Model selection: Criteria We have used this paper three criterion to select the appropriate ARIMA model namely Akaike Information Criterion (AIC), corrected Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The lowest value of AIC, AICc and BIC predict the best ARIMA model among the tentative models.

Source of Information: American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS), ISSN (Print) 2313-4410, ISSN (Online) 2313-4402

Article Link (Online Journal):

Funding Source:

Total Budget (Tk.) :

Achievement/Findings:

In this study, we attempt to search the optimal ARIMA model to predict the future values of CPI in Bangladesh using the data from 1986 to 2018. The time series plot, ACF and PACF plot and unit root test suggest that the original series and 1st difference series CPI in Bangladesh are non-stationary but 2nd difference series is stationary. Then applied the Box-Jenkins procedure on the 2nd difference of CPI series and we identify the corresponding ARMA (p,q) process. Thereafter we select different ARIMA (2, 2, 0) models among different tentative model since it gives the lowest AIC, AICc and BIC values. The diagnostic test suggest that the residual are normally distributed and there is no serial correlation i.e. the selected model is more appropriate than others. Based on the selected ARIMA (2, 2, 0) model we have to predict the CPI of Bangladesh from 2019 to 2025. The results of the study show that the CPI in Bangladesh is to continue an upward trend with respect to time.

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