Neural network models for inflation forecasting: an appraisal

Ali Choudhary and Adnan Haider

Applied Economics, 2012, vol. 44, issue 20, 2631-2635

Abstract: We assess the power of diverse Artificial Neural-Network (ANN) models as forecasting tools for monthly inflation rates for 28 Organization for Economic Co-operation and Development (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 Autoregressive model of order one (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.

Date: 2012
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Journal Article: Neural network models for inflation forecasting: an appraisal (2012)
Working Paper: Neural Network Models for Inflation Forecasting: An Appraisal (2011)
Working Paper: Neural Network Models for Inflation Forecasting: An Appraisal (2008)
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DOI: 10.1080/00036846.2011.566190

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