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Modelování měnově politické úrokové míry ČNB neuronovými sítěmi
[Modeling the CNB's Monetary Policy Interest Rate by Artificial Neural Networks]

Author

Listed:
  • Jaromír Kukal
  • Tran Van Quang

Abstract

Knowledgeability about interest rates set by a central bank is very important for all participants in an economy. In this paper we have used publicly available data to model how Czech National Bank manipulates its 2W repo rate when conducts its monetary policy. For this purpose, eight indicators are chosen. They are the Consumer Price Index (CPI), GDP growth rate (HDP), the monthly exchange rate EURCZK (KURZ), the monthly growth rate of monetary aggregate M2 (M2), the monthly unemployment rate (NEZAM), the monetary policy interest rate of the European Central Bank (EBC), the two-week Prague Interbank Interest rate PRIBOR14 and Economic Sentiment Indicator (IES). First, they are used as explanatory variables and then as the input signals to two different artificial neural network types with different architecture: the multilayer perceptron (MLP) and radial basis function (RBF) nets with different number of hidden neurons to model 2W repo rate of CNB. As a result, we fi nd that while the RBF network fails to provide stable results superior to the one of the linear model, the MLP network always can deliver better results than the one of the linear model. The best results are achieved with a network with only two hidden neurons. Further, these results are relatively stable with minimum time needed to complete the calculation. The MLP network therefore seems to be a promising tool for modeling the 2W repo rate of CNB.

Suggested Citation

  • Jaromír Kukal & Tran Van Quang, 2011. "Modelování měnově politické úrokové míry ČNB neuronovými sítěmi [Modeling the CNB's Monetary Policy Interest Rate by Artificial Neural Networks]," Politická ekonomie, Prague University of Economics and Business, vol. 2011(6), pages 810-829.
    • Handle: RePEc:prg:jnlpol:v:2011:y:2011:i:6:id:823:p:810-829
    DOI: 10.18267/j.polek.823
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    References listed on IDEAS

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    6. Michal Andrle & Tibor Hledik & Ondra Kamenik & Jan Vlcek, 2009. "Implementing the New Structural Model of the Czech National Bank," Working Papers 2009/2, Czech National Bank.
    7. Taylor, John B., 1993. "Discretion versus policy rules in practice," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 39(1), pages 195-214, December.
    8. Frank Smets & Raf Wouters, 2003. "An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area," Journal of the European Economic Association, MIT Press, vol. 1(5), pages 1123-1175, September.
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    Citations

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    Cited by:

    1. Ondřej Čížek, 2017. "Reakční funkce Evropské centrální banky [The Reaction Function of the European Central Bank]," Politická ekonomie, Prague University of Economics and Business, vol. 2017(4), pages 424-439.
    2. Josef Arlt & Martin Mandel, 2014. "The Reaction Function of Three Central Banks of Visegrad Group," Prague Economic Papers, Prague University of Economics and Business, vol. 2014(3), pages 269-289.
    3. Josef Arlt & Martin Mandel, 2012. "Je možné předpovídat repo sazbu ČNB na základě zpět hledícího měnového pravidla? [Is it Possible to Predict the CNB Repo Rate on the Basis of the Backward-Looking Monetary Rule?]," Politická ekonomie, Prague University of Economics and Business, vol. 2012(4), pages 484-504.

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    More about this item

    Keywords

    CNB; 2W repo rate; linear model; Artificial neural network; multilayer perceptron; RBF;
    All these keywords.

    JEL classification:

    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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