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Neural Networks for Macroeconomic Forecasting: A Complementary Approach to Linear Regression Models

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  • Steven Gonzalez

Abstract

In recent years, neural networks have received an increasing amount of attention among macroeconomic forecasters because of their potential to detect and reproduce linear and nonlinear relationships among a set of variables. This paper provides a highly accessible introduction to neural networks and establishes several parallels with standard econometric techniques. To facilitate the presentation, an empirical example is developed to forecast Canada's real GDP growth. For both the in-sample and out-of-sample periods, the forecasting accuracy of the neural network is found to be superior to a well-established linear regression model developed in the Department, with the error reduction ranging from 13 to 40 per cent. However, various tests indicate that there is little evidence that the improvement in forecasting accuracy is statistically significant. A thorough review of the literature suggests that neural networks are generally more accurate than linear models for out-of-sample forecasting of economic output and various financial variables such as stock prices. However, the literature should still be considered inconclusive due to the relatively small number of reliable studies on the topic. Despite these encouraging results, neural networks should not be viewed as a panacea, as this method also presents various weaknesses. Contrary to many researchers in the field, who tend to adopt an all-or-nothing approach to this issue, we argue that neural networks should be considered as a powerful complement to standard econometric methods, rather than a substitute. The full potential of neural networks can probably be exploited by using them in conjunction with linear regression models. Hence, neural networks should be viewed as an additional tool to be included in the toolbox of macroeconomic forecasters. Depuis quelques années, les prévisionnistes macro-économiques ont de plus en plus recours aux réseaux neuronaux en raison de leur capacité de repérer et de reproduire des relations linéaires et non linéaires dans un ensemble de variables. Le présent document se veut une introduction conviviale aux réseaux neuronaux, qui dresse des parallèles avec les techniques économétriques les plus répandues. Pour mieux illustrer nos propos, nous avons procédé à l’élaboration d’un exemple pratique sur les prévisions de croissance réelle du PIB au Canada. Qu’il s’agisse d’une période échantillonée ou non, on constatera que l’exactitude des prévisions du réseau neuronal est supérieur à celle du modèle de régression linéaire que le Ministère a élaboré et institué, avec diminution de la marge d’erreur, qui peut fluctuer entre 13 et 40 pour cent. Divers essais ont cependant permis d’établir que des prévisions plus exactes n’ont pas grande incidence sur le plan statistique. Une révision approfondie de la littérature nous a permis de conclure que les réseaux neuronaux sont dans l’ensemble plus exacts que les modèles linéraires pour la prévision des extrants économiques non échantillonés et pour certaines variables financières, tel le cours du marché. Cela dit, on ne saurait accorder une importance décisive à ce qui a été écrit en la matière, étant donné que les études fiables sont peu nombreuses pour le moment. Malgré des résultats encourageants, les réseaux neuronaux ne doivent pas être pris pour la panacée, car cette méthode présente également des faiblesses. Contrairement à nombre de chercheurs dans le domaine, qui ont tendance à adopter une approche absolutiste (soit tout, soit rien) à ce sujet, nous avançons que les réseaux neuronaux doivent être considérés comme un puissant complément aux méthodes économétriques en usage, plutôt que comme un substitut. Pour tirer le plus grand parti des réseaux neuronaux, il faudrait s’en servir en conjonction avec des modèles de régression linéaire. En somme, les réseaux neuronaux doivent être perçus comme un outil d’appoint à placer dans la boîte à outils des prévisionnistes macro-économiques.

Suggested Citation

  • Steven Gonzalez, "undated". "Neural Networks for Macroeconomic Forecasting: A Complementary Approach to Linear Regression Models," Working Papers-Department of Finance Canada 2000-07, Department of Finance Canada.
  • Handle: RePEc:fca:wpfnca:2000-07
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    Cited by:

    1. A. Nazif Çatik & Mehmet Karaçuka, 2011. "A comparative analysis of alternative univariate time series models in forecasting Turkish inflation," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 13(2), pages 275-293, April.
    2. Haider, Adnan & Hanif, Muhammad Nadeem, 2007. "Inflation Forecasting in Pakistan using Artificial Neural Networks," MPRA Paper 14645, University Library of Munich, Germany.
    3. Virág, Miklós & Kristóf, Tamás, 2005. "Az első hazai csődmodell újraszámítása neurális hálók segítségével
      [Recalculation of the first Hungarian bankruptcy-prediction model using neural networks]
      ," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(2), pages 144-162.
    4. Nakamura, Emi, 2005. "Inflation forecasting using a neural network," Economics Letters, Elsevier, vol. 86(3), pages 373-378, March.
    5. Jagric Timotej, 2003. "A Nonlinear Approach to Forecasting with Leading Economic Indicators," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 7(2), pages 1-20, July.
    6. Timotej Jagric & Sebastjan Strasek, 2005. "A Nonlinear Extension Of The Nber Model For Short-Run Forecasting Of Business Cycles," South African Journal of Economics, Economic Society of South Africa, vol. 73(3), pages 435-448, September.
    7. Dan Farhat, 2014. "Information Processing, Pattern Transmission and Aggregate Consumption Patterns in New Zealand:," Working Papers 1405, University of Otago, Department of Economics, revised Mar 2014.
    8. Dan Farhat, 2012. "Artificial Neural Networks and Aggregate Consumption Patterns in New Zealand," Working Papers 1205, University of Otago, Department of Economics, revised Dec 2012.
    9. María Clara Aristizábal Restrepo, 2006. "Evaluación asimétrica de una red neuronal artificial:Aplicación al caso de la inflación en Colombia," Borradores de Economia 377, Banco de la Republica de Colombia.
    10. Carla Salinas & Jon Mendieta, 2013. "Mitigation and adaptation investments for desertification and climate change: an assessment of the socioeconomic return," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 18(5), pages 659-672, June.
    11. Dan Farhat, 2014. "Artificial Neural Networks and Aggregate Consumption Patterns in New Zealand:," Working Papers 1404, University of Otago, Department of Economics, revised Mar 2014.

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