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Correlations Versus Causality Approaches to Economic Modeling

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Book cover Economic Modeling Using Artificial Intelligence Methods

Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

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Abstract

This chapter explores the issue of treating a predictive system as a missing data problem i.e. correlation exercise and compares it to treating as a cause and effect exercise, that is, feed-forward network. An auto-associative neural network is combined with genetic algorithm and then applied to missing economic data estimation. The algorithm is used on data that contain ten economic variables. The results of the missing data imputation approach are compared to those from a feed-forward neural network.

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Authors and Affiliations

  1. Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa

    Tshilidzi Marwala

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  1. Tshilidzi Marwala
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Marwala, T. (2013). Correlations Versus Causality Approaches to Economic Modeling. In: Economic Modeling Using Artificial Intelligence Methods. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-4471-5010-7_8

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  • DOI: https://doi.org/10.1007/978-1-4471-5010-7_8

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-5009-1

  • Online ISBN: 978-1-4471-5010-7

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