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Forecasting inflation in emerging markets: An evaluation of alternative models

journal contribution
posted on 2023-09-01, 14:12 authored by Zeyyad Mandalinci
This paper carries out a comprehensive forecasting exercise to assess the out-of-sample forecasting performances of various econometric models for inflation across three dimensions: time, emerging markets (EMs) and models. The competing models include univariate and multivariate models, fixed and time-varying parameter models, constant and stochastic volatility models, models using small and large datasets, and models with and without Bayesian variable selection. The results indicate that the forecasting performances of the different models change notably across both time and countries. Similarly to recent findings in the literature from developed countries, models that account for stochastic volatility and time-varying parameters provide more accurate forecasts for inflation than the alternatives in EMs. The results suggest that inflation predictability is correlated negatively with central bank independence. Also, institutional forecasts are superior to model-based forecasts for the majority of EMs. This suggests that the incorporation of subjective judgement can improve model-based inflation forecasts.

History

Refereed

  • Yes

Volume

33

Page range

1082-1104

Publication title

International Journal of Forecasting

ISSN

0169-2070

Publisher

Elsevier

File version

  • Published version

Language

  • eng

Legacy posted date

2017-12-05

Legacy creation date

2017-12-05

Legacy Faculty/School/Department

ARCHIVED Lord Ashcroft International Business School (until September 2018)

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