Neural Network Models for Inflation Forecasting: An Appraisal

Ali Choudhary

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Abstract: We assess the power of diverse artificial neural-network models (ANN) as forecasting tools for monthly inflation rates for 28 OECD countries. In the context of short out-of-sample forecasting horizon we find that, on average, the ANN models were a superior predictor for inflation for 45% while the AR1 model performed better for 23% of the countries. Furthermore, we develop arithmetic combinations of several ANN models and find that these may also serve as credible tools for forecasting inflation.

Keywords: Social; Sciences; &; Humanities (search for similar items in EconPapers)
Date: 2011-06-06
Note: View the original document on HAL open archive server: https://hal.science/hal-00704670
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Published in Applied Economics, 2011, pp.1. ⟨10.1080/00036846.2011.566190⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-00704670

DOI: 10.1080/00036846.2011.566190

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