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Using artificial neural networks to forecast producer price index for New Zealand

Using artificial neural networks to forecast producer price index for New Zealand Trend in the producer price is of much value to the central bank authorities in identifying the cost-push inflation that can improve their understanding of future directions of inflation in the aggregate economy and informulating sound policies and macroeconomic plans. Forecasting of the producer price movement is complex; the popular use of conventional methods is fraught with inaccuracies which often produces misleading results. This study explored the reliability and accuracy of the use of artificial neural networks (ANNs) for modelling and predicting producer price index (PPI) trend in New Zealand. The study also compared ANNs results with those produced by the autoregressive integrated moving average (ARIMA) as an alternative. Results showed that the ANNs model outperformed the ARIMA model as a more reliable and accurate tool for time series data prediction. The method developed could guide economists and macroeconomic policymakers in making more accurate forecasts. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Internet Manufacturing and Services Inderscience Publishers

Using artificial neural networks to forecast producer price index for New Zealand

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1751-6048
eISSN
1751-6056
DOI
10.1504/IJIMS.2020.107944
Publisher site
See Article on Publisher Site

Abstract

Trend in the producer price is of much value to the central bank authorities in identifying the cost-push inflation that can improve their understanding of future directions of inflation in the aggregate economy and informulating sound policies and macroeconomic plans. Forecasting of the producer price movement is complex; the popular use of conventional methods is fraught with inaccuracies which often produces misleading results. This study explored the reliability and accuracy of the use of artificial neural networks (ANNs) for modelling and predicting producer price index (PPI) trend in New Zealand. The study also compared ANNs results with those produced by the autoregressive integrated moving average (ARIMA) as an alternative. Results showed that the ANNs model outperformed the ARIMA model as a more reliable and accurate tool for time series data prediction. The method developed could guide economists and macroeconomic policymakers in making more accurate forecasts.

Journal

International Journal of Internet Manufacturing and ServicesInderscience Publishers

Published: Jan 1, 2020

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