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Non-Stationary Interest Rate Differentials and the Role of Monetary Policy

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  • Philipp Matros

    (Universität Regensburg)

  • Enzo Weber

    (Osteuropa-Institut, Regensburg (Institut for East European Studies))

Abstract

The present work deals with a frequently detected failure of the uncovered interest rate parity (UIP) - the absence of bivariate cointegration between domestic and foreign interest rates. We explain non-stationarity of the interest differential via central bank reactions to exchange rate variations. Thereby, the exchange rate in levels introduces an additional stochastic trend into the system. Trivariate cointegration between the interest rates and the exchange rate accounts for the missing stationarity property of the interest differential. We apply the concept to the case of Turkey and Europe,where we can validate the theoretical considerations by multivariate time series techniques.

Suggested Citation

  • Philipp Matros & Enzo Weber, 2011. "Non-Stationary Interest Rate Differentials and the Role of Monetary Policy," Working Papers 293, Leibniz Institut für Ost- und Südosteuropaforschung (Institute for East and Southeast European Studies).
    • Handle: RePEc:ost:wpaper:293
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    Cited by:

    1. Georgi MARINOV, 2016. "Small Sample Properties Of Panel Cointegration Tests In The Presence Of Structural Change," Journal of Social and Economic Statistics, Bucharest University of Economic Studies, vol. 5(1), pages 35-41, JULY.
    2. Jana Riedel, 2020. "On real interest rate convergence among G7 countries," Empirical Economics, Springer, vol. 59(2), pages 599-626, August.

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    More about this item

    Keywords

    Uncovered Interest Rate Parity; Monetary Policy Rules; Cointegration; Vector-Error Correction Model;
    All these keywords.

    JEL classification:

    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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