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Financial Market Conditions, Real Time, Nonlinearity and European Central Bank Monetary Policy: In-Sample and Out-of-Sample Assessment

Author

Listed:
  • Costas Milas

    (Economics Group, Keele Management School, Keele University, UK and Rimini Centre for Economic Analysis, Rimini, Italy)

  • Ruthira Naraidoo

    (Department of Economics, University of Pretoria)

Abstract

We explore how the ECB sets interest rates in the context of policy reaction functions. Using both real-time and revised information, we consider linear and nonlinear policy functions in inflation, output and a measure of financial conditions. We find that amongst Taylor rule models, linear and nonlinear models are empirically indistinguishable within sample and that model specifications with real-time data provide the best description of in-sample ECB interest rate setting behavior. The 2007-2009 financial crisis witnesses a shift from inflation targeting to output stabilisation and a shift, from an asymmetric policy response to financial conditions at high inflation rates, to a more symmetric response irrespectively of the state of inflation. Finally, without imposing an a priori choice of parametric functional form, semiparametric models forecast out-of-sample better than linear and nonlinear Taylor rule models.

Suggested Citation

  • Costas Milas & Ruthira Naraidoo, 2009. "Financial Market Conditions, Real Time, Nonlinearity and European Central Bank Monetary Policy: In-Sample and Out-of-Sample Assessment," Working Papers 200923, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:200923
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    Cited by:

    1. Christina Christou & Ruthira Naraidoo & Rangan Gupta & Christis Hassapis, 2022. "Monetary policy reaction to uncertainty in Japan: Evidence from a quantile‐on‐quantile interest rate rule," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2041-2053, April.
    2. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen Miller, 2013. "Forecasting Nevada gross gaming revenue and taxable sales using coincident and leading employment indexes," Empirical Economics, Springer, vol. 44(2), pages 387-417, April.
    3. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2012. "Was the Recent Downturn in US GDP Predictable?," Working Papers 1210, University of Nevada, Las Vegas , Department of Economics.
    4. William Miles & Samuel Schreyer, 2014. "Is monetary policy non-linear in Latin America? a quantile regression approach to Brazil, Chile, Mexico and Peru," Journal of Developing Areas, Tennessee State University, College of Business, vol. 48(2), pages 169-183, April-Jun.

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    Keywords

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    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

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