IDEAS home Printed from https://ideas.repec.org/a/abk/jajeba/ajebasp.2017.56.60.html

Forecasting Bangladesh's Inflation through Econometric Models

Author

Listed:
  • Nazmul Islam

Abstract

This research tries to sketch the concrete steps that help carry out to use ARIMA time series models for forecasting Bangladesh’s inflation. The focus, in this paper, is short-term basis annual inflation forecasting. For this purpose, different ARIMA models are used and the candid model is proposed. Based on the diagnostic and evaluation criteria, the most accurate model is selected. The order of the best ARIMA model was found to be ARIMA (1, 0, 0) to forecast the future inflation for a period up to five years. The predicted inflation rate is 4.40 in 2016 and in the consecutive years, it will rise slightly. The findings of the paper will give us a short-term view of inflation in Bangladesh and support in implementing policies to maintain stable inflation.

Suggested Citation

  • 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.
  • Handle: RePEc:abk:jajeba:ajebasp.2017.56.60
    DOI: 10.3844/ajebasp.2017.56.60
    as

    Download full text from publisher

    File URL: https://thescipub.com/pdf/ajebasp.2017.56.60.pdf
    Download Restriction: no

    File URL: https://thescipub.com/abstract/ajebasp.2017.56.60
    Download Restriction: no

    File URL: https://libkey.io/10.3844/ajebasp.2017.56.60?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kenny, Geoff & Meyler, Aidan & Quinn, Terry, 1998. "Forecasting Irish inflation using ARIMA models," Research Technical Papers 3/RT/98, Central Bank of Ireland.
    2. Meese, Richard & Geweke, John, 1984. "A Comparison of Autoregressive Univariate Forecasting Procedures for Macroeconomic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(3), pages 191-200, July.
    3. Akhter, Tahsina, 2013. "Short-Term Forecasting of Inflation in Bangladesh with Seasonal ARIMA Processes," MPRA Paper 43729, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nyoni, Thabani, 2019. "ARIMA modeling and forecasting of Consumer Price Index (CPI) in Germany," MPRA Paper 92442, University Library of Munich, Germany.
    2. Nyoni, Thabani, 2019. "Forecasting consumer price index in Norway: An application of Box-Jenkins ARIMA models," MPRA Paper 92411, University Library of Munich, Germany.
    3. Vesna Karadzic & Bojan Pejovic, 2021. "Inflation Forecasting in the Western Balkans and EU: A Comparison of Holt-Winters, ARIMA and NNAR Models," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 23(57), pages 517-517.
    4. Abdullah Ghazo, 2021. "Applying the ARIMA Model to the Process of Forecasting GDP and CPI in the Jordanian Economy," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 12(3), pages 70-77, May.
    5. Marc Brisson & Bryan Campbell & John W. Galbraith, 2001. "Forecasting Some Low-Predictability Time Series Using Diffusion Indices," CIRANO Working Papers 2001s-46, CIRANO.
    6. Marcellino, Massimliano, 2004. "Forecasting EMU macroeconomic variables," International Journal of Forecasting, Elsevier, vol. 20(2), pages 359-372.
    7. Ferrara, Laurent & Marcellino, Massimiliano & Mogliani, Matteo, 2015. "Macroeconomic forecasting during the Great Recession: The return of non-linearity?," International Journal of Forecasting, Elsevier, vol. 31(3), pages 664-679.
    8. Rareș-Petru MIHALACHE & Dumitru Alexandru BODISLAV, 2023. "Forecasting the Romanian inflation rate: An Autoregressive Integrated Moving-Average (ARIMA) approach," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(1(634), S), pages 67-76, Spring.
    9. Michael Debabrata Patra & Partha Ray, 2010. "Inflation Expectations and Monetary Policy in India: An Empirical Exploration," IMF Working Papers 2010/084, International Monetary Fund.
    10. Kenny, Geoff & Meyler, Aidan & Quinn, Terry, 1998. "Bayesian VAR Models for Forecasting Irish Inflation," Research Technical Papers 4/RT/98, Central Bank of Ireland.
    11. Syarifah Inayati & Nur Iriawan & Irhamah, 2024. "A Markov Switching Autoregressive Model with Time-Varying Parameters," Forecasting, MDPI, vol. 6(3), pages 1-23, July.
    12. James H. Stock & Mark W. Watson, 1998. "A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series," NBER Working Papers 6607, National Bureau of Economic Research, Inc.
    13. KUMAR Manoj & ANAND Madhu, 2014. "An Application Of Time Series Arima Forecasting Model For Predicting Sugarcane Production In India," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 9(1), pages 81-94, April.
    14. Meyler, Aidan, 1999. "A Statistical Measure Of Core Inflation," Research Technical Papers 2/RT/99, Central Bank of Ireland.
    15. Massimiliano Marcellino, "undated". "Forecast pooling for short time series of macroeconomic variables," Working Papers 212, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    16. Luc Bauwens & Gary Koop & Dimitris Korobilis & Jeroen V.K. Rombouts, 2015. "The Contribution of Structural Break Models to Forecasting Macroeconomic Series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 596-620, June.
    17. Tamerlan Mashadihasanli, 2022. "Stock Market Price Forecasting Using the Arima Model: an Application to Istanbul, Turkiye," Journal of Economic Policy Researches, Istanbul University, Faculty of Economics, vol. 9(2), pages 439-454, July.
    18. Ralf Brüggemann & Helmut Lütkepohl & Massimiliano Marcellino, 2008. "Forecasting euro area variables with German pre-EMU data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(6), pages 465-481.
    19. Jeff Tayman & Stanley Smith & Jeffrey Lin, 2007. "Precision, bias, and uncertainty for state population forecasts: an exploratory analysis of time series models," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 26(3), pages 347-369, June.
    20. Naresh Bansal & Jack Strauss & Alireza Nasseh, 2015. "Can we consistently forecast a firm’s earnings? Using combination forecast methods to predict the EPS of Dow firms," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 39(1), pages 1-22, January.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:abk:jajeba:ajebasp.2017.56.60. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Jeffery Daniels (email available below). General contact details of provider: https://thescipub.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.