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Forecasting Monthly Inflation: An Application To Suriname

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  • Gavin Ooft

    (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

  • Gavin Ooft, 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

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    References listed on IDEAS

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    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

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