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Can Recurrent Neural Networks Predict Inflation in Euro Zone as Good as Professional Forecasters?

Author

Listed:
  • Tea Šestanović

    (Faculty of Economics, Business and Tourism, University of Split, 21000 Split, Croatia
    Current address: Cvite Fiskovića 5, 21000 Split, Croatia.
    Both authors contributed equally to this work.)

  • Josip Arnerić

    (Faculty of Economics and Business, University of Zagreb, 10000 Zagreb, Croatia
    Both authors contributed equally to this work.)

Abstract

This paper investigates whether a specific type of a recurrent neural network, in particular Jordan neural network (JNN), captures the expected inflation better than commonly used feedforward neural networks and traditional parametric time-series models. It also considers competing survey-based and model-based expected inflation towards ex-post actual inflation to find whose predictions are more accurate; predictions from survey respondents or forecasting modelers. Further, it proposes neural network modelling strategy when dealing with nonstationary time-series which exhibit long-memory property and nonlinear dependence with respect to lagged inputs and exogenous inputs as well. Following this strategy, overfitting problem was reduced until no improvement in forecasting accuracy of expected inflation is achieved. The main finding is that JNN predicts inflation in euro zone quite accurately within forecasting horizon of 2 years. Regarding rational expectation principle we have found a set of demand-pull and cost-push inflation characteristics as exogenous inputs which helps in reducing overfitting problem of recurrent neural network even more. The sample includes euro zone aggregated monthly observations from January 2000 to December 2019. The results also confirm that inflation expectations obtained from JNN are consistent with Survey of professional forecasters (SPF), and thus, monetary policy makers can use JNN as a complementary tool in shortcomings of other inflation expectations measures.

Suggested Citation

  • Tea Šestanović & Josip Arnerić, 2021. "Can Recurrent Neural Networks Predict Inflation in Euro Zone as Good as Professional Forecasters?," Mathematics, MDPI, vol. 9(19), pages 1-13, October.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:19:p:2486-:d:649726
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    References listed on IDEAS

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