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Nonlinearities in the dynamics of the euro area demand for M1

Author

Listed:
  • Alessandro Calza

    (European Central Bank)

  • Andrea Zaghini

    (Bank of Italy, Economics, Research and International Relations Area)

Abstract

The paper finds evidence of non-linearities in the dynamics of the euro-area demand for the narrow aggregate M1. A long-run money demand relationship is first estimated over a sample period covering the last three decades. While the parameters of the relationship are jointly stable, there are indications of non-linearity in the residuals of the error-correction model. This non-linearity is explicitly modelled using a fairly general Markov switching error-correction model with satisfactory results. The empirical findings of the paper are consistent with theoretical predictions of non-linearities in the dynamics of adjustment to equilibrium stemming from "buffer stock" and "target-threshold" models and with analogous empirical evidence for European countries and the US.

Suggested Citation

  • Alessandro Calza & Andrea Zaghini, 2008. "Nonlinearities in the dynamics of the euro area demand for M1," Temi di discussione (Economic working papers) 690, Bank of Italy, Economic Research and International Relations Area.
    • Handle: RePEc:bdi:wptemi:td_690_08
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    Cited by:

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    3. Lieb Lenard & Candelon Bertrand, 2015. "Testing for short-run threshold effects in a vector error-correction framework: a reappraisal of the stability of the US money demand," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(3), pages 355-376, June.
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    7. Jung, Alexander, 2016. "A portfolio demand approach for broad money in the euro area," Working Paper Series 1929, European Central Bank.

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    More about this item

    Keywords

    Euro area; cointegration; non-linear error-correction; demand for money;
    All these keywords.

    JEL classification:

    • E41 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Demand for Money
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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