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Optimal rules for central bank interest rates subject to zero lower bound

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  • Singh, Ajay Pratap
  • Nikolaou, Michael

Abstract

The celebrated Taylor rule provides a simple formula that aims to capture how the central bank interest rate is adjusted as a linear function of inflation and output gap. However, the rule does not take explicitly into account the zero lower bound on the interest rate. Prior studies on interest rate selection subject to the zero lower bound have not produced rigorous derivations of explicit rules. In this work, Taylor-like rules for central bank interest rates bounded below by zero are derived rigorously using a multi-parametric model predictive control (mpMPC) framework. Rules with or without inertia are included in the derivation. The proposed approach is illustrated through simulations on US economy data. A number of issues for future study are proposed.

Suggested Citation

  • Singh, Ajay Pratap & Nikolaou, Michael, 2013. "Optimal rules for central bank interest rates subject to zero lower bound," Economics Discussion Papers 2013-49, Kiel Institute for the World Economy (IfW).
  • Handle: RePEc:zbw:ifwedp:201349
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    Cited by:

    1. Ioannis N. Kallianiotis, 2015. "Economic Crises and the Substitution of Fiscal Policy by Monetary Policy," International Journal of Economics and Financial Issues, Econjournals, vol. 5(1), pages 44-68.
    2. Ioannis N. Kallianiotis, 2014. "The Optimal Interest Rates and the Current Interest Rate System," Eurasian Journal of Economics and Finance, Eurasian Publications, vol. 2(3), pages 1-25.

    More about this item

    Keywords

    Taylor rule; zero lower bound; liquidity trap; model predictive control; multiparametric programming;

    JEL classification:

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

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