New perspectives on forecasting inflation in emerging market economies: An empirical assessment

Roberto Duncan and Enrique Martínez-García
Authors registered in the RePEc Author Service: Enrique Martínez García ()

International Journal of Forecasting, 2019, vol. 35, issue 3, 1008-1031

Abstract: We use a broad-range set of inflation models and pseudo out-of-sample forecasts to assess their predictive ability among 14 emerging market economies (EMEs) at different horizons (1–12 quarters ahead) with quarterly data over the period 1980Q1-2016Q4. We find, in general, that a simple arithmetic average of the current and three previous observations (the RW-AO model) consistently outperforms its standard competitors—based on the root mean squared prediction error (RMSPE) and on the accuracy in predicting the direction of change. These include conventional models based on domestic factors, existing open-economy Phillips curve-based specifications, factor-augmented models, and time-varying parameter models. Often, the RMSPE and directional accuracy gains of the RW-AO model are shown to be statistically significant. Our results are robust to forecast combinations, intercept corrections, alternative transformations of the target variable, different lag structures, and additional tests of (conditional) predictability. We argue that the RW-AO model is successful among EMEs because it is a straightforward method to downweight later data, which is a useful strategy when there are unknown structural breaks and model misspecification.

Keywords: Inflation forecasting; Random walk; Emerging market economies; Policy credibility; Robust forecasts (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Working Paper: New Perspectives on Forecasting Inflation in Emerging Market Economies: An Empirical Assessment (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:35:y:2019:i:3:p:1008-1031

DOI: 10.1016/j.ijforecast.2019.04.004

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