IDEAS home Printed from https://ideas.repec.org/p/ris/jhisae/0144.html
   My bibliography  Save this paper

Forecasting Monthly Inflation: An Application To Suriname

Author

Listed:
  • Ooft, Gavin

    (The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise)

Abstract

An accurate forecast for inflation is mandatory in the conduction of monetary policy. This paper presents models that forecast monthly inflation utilizing various economic techniques for the economy of Suriname. The paper employs Autoregressive Integrated Moving Average models (ARIMA), Vector Autoregressive models (VAR), Factor Augmented Vector Autoregressive models (FAVAR), Bayesian Vector Autoregressive models (BVAR) and Vector Error Correction (VECM) models to model monthly inflation for Suriname over the period from 2004 to 2018. Consequently, the forecast performance of the models is evaluated by comparison of the Root Mean Square Error and the Mean Average Errors. We also conduct a pseudo out-of-sample forecasting exercise. The VECM yields the best results forecasting up to three months ahead, while thereafter, the FAVAR, which includes more economic information, outperforms the VECM, based on the assessment of the pseudo out-of-sample forecast performance of the models.

Suggested Citation

  • Ooft, Gavin, 2020. "Forecasting Monthly Inflation: An Application To Suriname," Studies in Applied Economics 144, The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise.
  • Handle: RePEc:ris:jhisae:0144
    as

    Download full text from publisher

    File URL: https://sites.krieger.jhu.edu/iae/files/2020/01/FORECASTING-MONTHLY-INFLATION-AN-APPLICATION-TO-SURINAME.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mark Gertler & Jordi Gali & Richard Clarida, 1999. "The Science of Monetary Policy: A New Keynesian Perspective," Journal of Economic Literature, American Economic Association, vol. 37(4), pages 1661-1707, December.
    2. Zarnowitz, Victor, 1979. "An Analysis of Annual and Multiperiod Qtrly Forecasts of Aggregate Income, Output, and the Price Level," The Journal of Business, University of Chicago Press, vol. 52(1), pages 1-33, January.
    3. Troy D. Matheson, 2006. "Factor Model Forecasts for New Zealand," International Journal of Central Banking, International Journal of Central Banking, vol. 2(2), May.
    4. Frederic S. Mishkin, 1984. "The causes of inflation," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 1-32.
    5. Athanasios Orphanides & John C. Williams, 2005. "Inflation scares and forecast-based monetary policy," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 498-527, April.
    6. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(1), pages 387-422.
    7. Mr. Toshitaka Sekine, 2001. "Modeling and Forecasting Inflation in Japan," IMF Working Papers 2001/082, International Monetary Fund.
    8. McCallum, Bennett T., 1990. "Inflation: Theory and evidence," Handbook of Monetary Economics, in: B. M. Friedman & F. H. Hahn (ed.), Handbook of Monetary Economics, edition 1, volume 2, chapter 18, pages 963-1012, Elsevier.
    9. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    10. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    11. Diana Gabrielyan, 2016. "Forecasting Inflation Using The Phillips Curve: Evidence From Swedish Data," University of Tartu - Faculty of Economics and Business Administration Working Paper Series 100, Faculty of Economics and Business Administration, University of Tartu (Estonia).
    12. Matthes, Christian & Wang, Mu-Chun, 2012. "What drives inflation in New Keynesian models?," Economics Letters, Elsevier, vol. 114(3), pages 338-342.
    13. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    14. Brent Meyer & Mehmet Pasaogullari, 2010. "Simple ways to forecast inflation: what works best?," Economic Commentary, Federal Reserve Bank of Cleveland, issue Dec.
    15. Litterman, Robert, 1986. "Forecasting with Bayesian vector autoregressions -- Five years of experience : Robert B. Litterman, Journal of Business and Economic Statistics 4 (1986) 25-38," International Journal of Forecasting, Elsevier, vol. 2(4), pages 497-498.
    16. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    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. Ooft, Gavin, 2018. "Modelling and Forecasting Inflation for the Economy of Suriname," EconStor Preprints 215534, ZBW - Leibniz Information Centre for Economics.
    2. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2022. "The global component of inflation volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 700-721, June.
    3. Rangan Gupta & Alain Kabundi & Stephen Miller & Josine Uwilingiye, 2014. "Using large data sets to forecast sectoral employment," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(2), pages 229-264, June.
    4. Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
    5. Jan-Erik Antipin & Farid Jimmy Boumediene & Pär Österholm, 2014. "Forecasting Inflation Using Constant Gain Least Squares," Australian Economic Papers, Wiley Blackwell, vol. 53(1-2), pages 2-15, June.
    6. Kaabia, Olfa & Abid, Ilyes & Guesmi, Khaled, 2013. "Does Bayesian shrinkage help to better reflect what happened during the subprime crisis?," Economic Modelling, Elsevier, vol. 31(C), pages 423-432.
    7. Muellbauer, John, 2018. "The Future of Macroeconomics," INET Oxford Working Papers 2018-10, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
    8. Hauzenberger Niko & Huber Florian & Pfarrhofer Michael & Zörner Thomas O., 2021. "Stochastic model specification in Markov switching vector error correction models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 25(2), pages 1-17, April.
    9. Bekiros Stelios & Paccagnini Alessia, 2015. "Estimating point and density forecasts for the US economy with a factor-augmented vector autoregressive DSGE model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(2), pages 107-136, April.
    10. Bloor, Chris & Matheson, Troy, 2011. "Real-time conditional forecasts with Bayesian VARs: An application to New Zealand," The North American Journal of Economics and Finance, Elsevier, vol. 22(1), pages 26-42, January.
    11. Chan, Joshua C.C. & Eisenstat, Eric & Koop, Gary, 2016. "Large Bayesian VARMAs," Journal of Econometrics, Elsevier, vol. 192(2), pages 374-390.
    12. Michael McLeay & Silvana Tenreyro, 2020. "Optimal Inflation and the Identification of the Phillips Curve," NBER Macroeconomics Annual, University of Chicago Press, vol. 34(1), pages 199-255.
    13. Kapetanios, George & Labhard, Vincent & Price, Simon, 2008. "Forecast combination and the Bank of England's suite of statistical forecasting models," Economic Modelling, Elsevier, vol. 25(4), pages 772-792, July.
    14. Bekiros, Stelios D. & Paccagnini, Alessia, 2014. "Bayesian forecasting with small and medium scale factor-augmented vector autoregressive DSGE models," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 298-323.
    15. Luigi Paciello, 2011. "Does Inflation Adjust Faster to Aggregate Technology Shocks than to Monetary Policy Shocks?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(8), pages 1663-1684, December.
    16. Pestova, Anna (Пестова, Анна) & Mamonov, Mikhail (Мамонов, Михаил), 2016. "Estimating the Influence of Different Shocks on Macroeconomic Indicators and Developing Conditional Forecasts on the Basis of BVAR Model for the Russian Economy [Оценка Влияния Различных Шоков На Д," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 4, pages 56-92, August.
    17. Koop, Gary & Korobilis, Dimitris, 2010. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(4), pages 267-358, July.
    18. Gossé, Jean-Baptiste & Guillaumin, Cyriac, 2013. "L’apport de la représentation VAR de Christopher A. Sims à la science économique," L'Actualité Economique, Société Canadienne de Science Economique, vol. 89(4), pages 309-319, Décembre.
    19. Karlsson, Sune, 2013. "Forecasting with Bayesian Vector Autoregression," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 791-897, Elsevier.
    20. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.

    More about this item

    Keywords

    Inflation; Forecasting; Time-Series Models; Suriname;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:ris:jhisae:0144. 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: Steve H. Hanke (email available below). General contact details of provider: https://edirc.repec.org/data/iaejhus.html .

    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.