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Short-Term Forecasting of Inflation in Bangladesh with Seasonal ARIMA Processes

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  • Akhter, Tahsina

Abstract

The purpose of this study is to forecast the short-term inflation rate of Bangladesh using the monthly Consumer Price Index (CPI) from January 2000 to December 2012. To do so, the study employed the Seasonal Auto-regressive Integrated Moving Average (SARIMA) models proposed by Box, Jenkins, and Reinsel (1994). CUSUM, Quandt likelihood ratio (QLR) and Chow test have been utilized to identify the structural breaks over the sample periods and all three tests suggested that the structural breaks in CPI series of Bangladesh are in the month of February 2007 and September 2009. Hence, the study truncated the series and using CPI data from September 2009 to December 2012, the ARIMA(1,1,1)(1,0,1)12 models were estimated and forecasted. The forecasted result suggests an increasing pattern and high rates of inflation over the forecasted period 2013. Therefore, the study recommends that Bangladesh Bank should come forward with more appropriate economic and monetary policies in order to combat such increase inflation in 2013.

Suggested Citation

  • Akhter, Tahsina, 2013. "Short-Term Forecasting of Inflation in Bangladesh with Seasonal ARIMA Processes," MPRA Paper 43729, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:43729
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    References listed on IDEAS

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    1. Junttila, Juha, 2001. "Structural breaks, ARIMA model and Finnish inflation forecasts," International Journal of Forecasting, Elsevier, vol. 17(2), pages 203-230.
    2. Meyler, Aidan & Kenny, Geoff & Quinn, Terry, 1998. "Forecasting irish inflation using ARIMA models," MPRA Paper 11359, University Library of Munich, Germany.
    3. Robert Engle, 2001. "GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 157-168, Fall.
    4. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    5. Peter Schulze & Alexander Prinz, 2009. "Forecasting container transshipment in Germany," Applied Economics, Taylor & Francis Journals, vol. 41(22), pages 2809-2815.
    6. Andreja Pufnik & Davor Kunovac, 2006. "Short-Term Forecasting of Inflation in Croatia with Seasonal ARIMA Processes," Working Papers 16, The Croatian National Bank, Croatia.
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    Cited by:

    1. Nazmul Islam, 2017. "Forecasting Bangladesh's Inflation through Econometric Models," American Journal of Economics and Business Administration, Science Publications, vol. 9(3), pages 56-60, November.

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    More about this item

    Keywords

    Inflation; Forecasting; SARIMA; Bangladesh;
    All these keywords.

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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