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Forecasting Inflation in Emerging Markets: An Evaluation of Alternative Models

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This paper carries out a comprehensive forecasting exercise to assess out-of-sample forecasting performance of various econometric models for inflation across three dimensions; time, emerging market countries and models. The competing forecasting models include univariate and multivariate, fixed and time varying parameter, constant and stochastic volatility, small and large dataset, with and without bayesian variable selection models. Results indicate that the forecasting performance of different models change notably both across time and countries. Similar to some of the recent findings of the literature that focus on developed countries, models that account for stochastic volatility and time-varying parameters provide more accurate forecasts for inflation than alternatives in emerging markets.

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  • Zeyyad Mandalinci, 2015. "Forecasting Inflation in Emerging Markets: An Evaluation of Alternative Models," CReMFi Discussion Papers 3, CReMFi, School of Economics and Finance, QMUL.
  • Handle: RePEc:qmm:wpaper:3
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    3. Geraldine Dany-Knedlik & Juan Angel Garcia, 2018. "Monetary Policy and Inflation Dynamics in ASEAN Economies," Discussion Papers of DIW Berlin 1755, DIW Berlin, German Institute for Economic Research.
    4. Ivașcu Codruț, 2023. "Can Machine Learning Models Predict Inflation?," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 17(1), pages 1748-1756, July.
    5. Macias, Paweł & Stelmasiak, Damian & Szafranek, Karol, 2023. "Nowcasting food inflation with a massive amount of online prices," International Journal of Forecasting, Elsevier, vol. 39(2), pages 809-826.
    6. Nyoni, Thabani & Nathaniel, Solomon Prince, 2018. "Modeling rates of inflation in Nigeria: an application of ARMA, ARIMA and GARCH models," MPRA Paper 91351, University Library of Munich, Germany.
    7. Cogoljević, Dušan & Gavrilović, Milan & Roganović, Miloš & Matić, Ivana & Piljan, Ivan, 2018. "Analyzing of consumer price index influence on inflation by multiple linear regression," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 941-944.
    8. Szafranek, Karol, 2019. "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1042-1059.
    9. Garegnani, Lorena & Gómez Aguirre, Maximiliano, 2018. "Forecasting Inflation in Argentina," IDB Publications (Working Papers) 8940, Inter-American Development Bank.
    10. Yuntong Liu & Yu Wei & Yi Liu & Wenjuan Li, 2020. "Forecasting Oil Price by Hierarchical Shrinkage in Dynamic Parameter Models," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-12, December.
    11. Krzysztof DRACHAL, 2020. "Forecasting the Inflation Rate in Poland and U.S. Using Dynamic Model Averaging (DMA) and Google Queries," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 18-34, July.
    12. Solikin M. Juhro & Bernard Njindan Iyke, 2019. "Forecasting Indonesian Inflation Within An Inflation-Targeting Framework: Do Large-Scale Models Pay Off?," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 22(4), pages 423-436.

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    More about this item

    Keywords

    Forecasting; Bayesian Analysis; Emerging Markets; Forecast Comparison;
    All these keywords.

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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