The effects of economic policy uncertainty on European economies: evidence from a TVP-FAVAR

Abstract

We use a time-varying parameter FAVAR model to investigate the effects of economic policy uncertainty (EPU) on a wide range of macroeconomic variables for eleven European Monetary Union (EMU) countries. First, we are able to distinguish between a group of fragile countries (GIIPS countries) and a group of stable countries (northern countries), where the former suffered the most due to EPU shocks. Second, we find that EPU shocks affect financial markets as well as the real economy and that private investors and financial market participants react more sensitively than consumers to EPU shocks. Third, we discover that the transmission of EPU shocks is quite stable over time.

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Notes

  1. 1.

    Other measures of uncertainty exist, e.g., the measure of Jurado et al. (2015). That measure captures macroeconomic uncertainty, based on non-European data, and has no obvious political component. In contrast, the measure of Baker et al. (2016) includes both components, a macroeconomic and a political, as also suggested by our historical decomposition (see Sect. 4.5), and therefore mirrors the type of uncertainty we want to capture. Baker et al. (2016) document robustness and reliability of their index by a comparison of their algorithm-based index with an index constructed from human reading of the same articles. Both indices are very similar.

  2. 2.

    Notable exceptions are Benati (2014), Caggiano et al. (2014) and Popp and Zhang (2016).

  3. 3.

    The empirical importance of this channel is underpinned by a statement of the FOMC of October 2001: “Several [survey] participants reported that uncertainty about the economic outlook was leading firms to defer spending projects until prospects for economic activity became clearer.” A similar statement can be found in the Minutes of the FOMC from December 15 to 16, 2009.

  4. 4.

    In order to provide further evidence that households’ decisions are affected by EPU we extend the data set by the consumer confidence indicator.

  5. 5.

    Expected future cash flow also depends on consumption which itself is adversely affected by EPU.

  6. 6.

    The same prior specification can be found in Gambetti and Musso (2016).

  7. 7.

    For more details see Amir-Ahmadi et al. (2018).

  8. 8.

    For a discussion of these aspects see Korobilis (2013).

  9. 9.

    Popp and Zhang (2016) use the same strategy to identify uncertainty shocks in a FAVAR model.

  10. 10.

    We also estimate the model with two different orderings. First, \({\varvec{y}}_t =[ {\varvec{f}}'_t, \mathrm{epu}_t, R_t]'\). Second, \({\varvec{y}}_t =[\mathrm{epu}_t, {\varvec{f}}'_t, R_t]'\). Our main findings are qualitatively similar for both orderings.

  11. 11.

    For the implementation of sign restriction in a FAVAR model, see Ellis et al. (2014) or Prüser and Schlösser (2018).

  12. 12.

    In this setup, the shock is only set-identified. Therefore, we follow a suggestion in Fry and Pagan (2011) and keep for each draw from the posterior 100 candidates which satisfy the assumption and out of this set of “admissible models” we selection the one with elements closest to the median across these 100 candidates.

  13. 13.

    These 99 macroeconomic variables form the data matrix \({\varvec{x}}_t\) from which we extract the factors.

  14. 14.

    The principal components are constructed by using constant weights. Furthermore, we applied the same factor rotation as suggested by Bernanke et al. (2005) to eliminate the influence of the observed variables \(R_t\) and \(\mathrm{epu}_t\) on the unobserved factors \({\varvec{f}}_t\).

  15. 15.

    We estimate the autocorrelation of the error term for each equation and find no evidence for serious autocorrelation.

  16. 16.

    A detailed explanation of the Metropolis-within-Gibbs step as well as the whole algorithm can be found in Section B of the supplementary appendix.

  17. 17.

    The decline of the median volatility of EPU since 2008 may at first come as a surprise. However, this is due to the fact that a large amount of variation in EPU is explained by other shocks in the model, in particular the increase of EPU since 2008. We will discuss this issue further in the historical decomposition of EPU in Sect. 4.6.

  18. 18.

    Estimation results with the hyperparameter values chosen by Primiceri (2005) are available upon request. The major difference appears in the stochastic volatility of R which becomes time invariant. This suggests that inappropriate benchmark values erroneously suppress or increase time variation of the model parameters.

  19. 19.

    Estimating the model with constant coefficients yields the same result, but again cannot deliver insight into whether the transmission of EPU shock has changed over time. Results are available upon request.

  20. 20.

    The Bayes p-value is calculated as the frequentists’ p-value, but has a Bayesian interpretation. For this reason we use the expression “credible” instead of “significant.”

  21. 21.

    Whether the effect is due to financial reforms as raised by Bordo et al. (2016) or a consequence of the decrease in investment and consumption needs to be investigated in future research and is beyond the scope of this paper.

  22. 22.

    This period was characterized by sluggish growth of the global economy in 2003 and passed over into the Iraq war, which further affected the global economy. With the onset of the Iraq war, the ECB reduced its main interest rate by 0.5% because of expected adverse effects.

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Correspondence to Alexander Schlösser.

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Jan Prüser and Alexander Schlösser declare that they have no conflict of interest.

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We thank the two anonymous referees, Joscha Beckmann, Volker Clausen, Herman van Dijk, Christoph Hanck, Alexander Kriwoluzky as well as the participants of the European Seminar on Bayesian Econometrics in Maastricht, the audience of the Le Studium Conference in Orléans for valuable comments and the participants of the Jahrestagung des Verein für Socialpolitik in Freiburg im Breisgau.

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Supplementary material 1 (pdf 583 KB)

Appendix A. IRFs to EPU shock

Appendix A. IRFs to EPU shock

See Figs. 6, 7, 8, 9, 10, 11, 12 and 13.

Fig. 6
figure6

Response of investment growth to one standard deviation shock in EPU. For details see Fig. 3

Fig. 7
figure7

Response of consumption growth to one standard deviation shock in EPU. For details see Fig. 3

Fig. 8
figure8

Response of inflation to one standard deviation shock in EPU. For details see Fig. 3

Fig. 9
figure9

Response of the change in the unemployment rate to one standard deviation shock in EPU. For details see Fig. 3

Fig. 10
figure10

Response of credit growth to one standard deviation shock in EPU. For details see Fig. 3

Fig. 11
figure11

Response of LTI to one standard deviation shock in EPU. For details see Fig. 3

Fig. 12
figure12

Response of stock market return to one standard deviation shock in EPU. For details see Fig. 3

Fig. 13
figure13

Response of consumer confidence percentage change to one standard deviation shock in EPU. For details see Fig. 3

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Prüser, J., Schlösser, A. The effects of economic policy uncertainty on European economies: evidence from a TVP-FAVAR. Empir Econ 58, 2889–2910 (2020). https://doi.org/10.1007/s00181-018-01619-8

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Keywords

  • TVP-FAVAR
  • Economic policy uncertainty
  • Fat data
  • Hyperparameter
  • European Monetary Union
  • Hierarchical prior

JEL Classification

  • C11
  • C32
  • E20
  • E60