Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Studies in Nonlinear Dynamics & Econometrics

Ed. by Mizrach, Bruce


IMPACT FACTOR 2017: 0.855

CiteScore 2017: 0.76

SCImago Journal Rank (SJR) 2017: 0.668
Source Normalized Impact per Paper (SNIP) 2017: 0.894

Mathematical Citation Quotient (MCQ) 2017: 0.02

Online
ISSN
1558-3708
See all formats and pricing
More options …
Volume 18, Issue 3

Issues

The effects of the monetary policy stance on the transmission mechanism

Ana Beatriz Galvao / Massimiliano Marcellino
Published Online: 2013-10-30 | DOI: https://doi.org/10.1515/snde-2012-0027

Abstract

This paper contributes to the literature on changes in the transmission mechanism of monetary policy by introducing a model whose parameter evolution explicitly depends on the stance of monetary policy. The model, a structural break endogenous threshold VAR, also captures changes in the variance of shocks, and allows for a break in the parameters at an estimated time. We show that the transmission is asymmetric depending on the extention of the deviation of the actual policy rate from the one required by the Taylor rule. When the policy stance is tight – actual rate is higher than the one implied by the Taylor rule – contractionary shocks have stronger negative effects on output and prices.

This article offers supplementary material which is provided at the end of the article.

Keywords: asymmetries; great moderation; monetary policy transmission; threhold models; time-varying models

JEL classification: E52; C51

References

  • Altissimo, F., and V. Corradi. 2002. “Bounds for Inference with Nuisance Parameters Present Only Under the Alternative.” Econometrics Journal 5: 494–519.Google Scholar

  • Andrews, D. W. K. 1993. “Tests for Parameter Instability and Structural Change with Unknown Change Point.” Econometrica 61: 821–856.Google Scholar

  • Benati, L., and P. Surico. 2009. “VAR Analysis and the Great Moderation.” American Economic Review 99: 1636–1652.Google Scholar

  • Bianchi, F. 2012. “Regime Switches, Agents’ Beliefs, and Post-WORLD War II US Macroeconomic Dynamics.” Duke University Working Papers 12–14.Google Scholar

  • Boivin, J., and M. P. Giannoni. 2006. “Has Monetary Policy Become More Effective?” The Review of Economics and Statistics 88: 445–462.Web of ScienceGoogle Scholar

  • Boivin, J., M. T. Kiley, and F. S. Mishkin. 2011. “How has the Monetary Transmission Mechanism Evolved Over Time?” In Handbook of Monetary Economics, edited by B. Friedman and M. Woodford, 3A, 369–422. San Diego: Elsevier.Google Scholar

  • Canova, F. 2007. Methods for Applied Macroeconomic Research. Princeton University Press.Google Scholar

  • Cecchetti, S. G., P. Hooper, B. C. Kasman, K. L. Schoenholtz, and M. W. Watson. 2007. “Understanding the Evolving Inflation Process.” Monetary Policy Forum Report n. 1 2007.Google Scholar

  • Cogley, T., and T. J. Sargent. 2005. “Drifts and Volatilities: Monetary Policies and Outcomes in the Post WWII US.” Review of Economic Dynamics 8: 262–302.Google Scholar

  • Cukierman, A., and A. Muscatelli. 2008. “Nonlinear Taylor Rules And Asymmetric Preferences In Central Banking: Evidence from the United Kingdom and the United States. B.E. Journal of Macroeconomics (Contributions), 8: article 7.Google Scholar

  • Davig, T., and T. Doh. 2009. “Monetary Policy Regime Shifts and Inflation Persistence. The Federal Reserve Bank of Kansas City Research Working Paper 8–16.Google Scholar

  • Davig, T., and E. Leeper. 2008. “Endogenous Monetary Policy Regime Change.” In NBER International Seminar on Macroeconomics 2006, edited by L. Reichlin and K. West, 345–391. Chicago: Chicago University Press.Google Scholar

  • Demertzis, M., M. Marcellino, and N. Viegi. 2012. “A Measure for Credibility: Tracking the US ‘Great Moderation’.” B.E. Journal of Macroeconomics (Topics) 12(1).Google Scholar

  • Fernandez-Villaverde, J., P. Guerron-Quintana, and J. Rubio-Ramirez. 2010. “Fortune or Virtue: Time-Variant Volatilities Versus Parameter Drifting in US Data.” NBER Working Paper, No. 15928.Google Scholar

  • Galvão, A. B. 2006. “Structural Break Threshold Vars for Predicting US Recessions using the Spread.” Journal of Applied Econometrics 21: 463–487.Google Scholar

  • Gonzalo, J., and J. I. Pitarakis. 2002. “Estimation and Model Selection Based Inference in Single and Multiple Threshold Models.” Journal of Econometrics 110: 319–352.Google Scholar

  • Goodfriend, M., and R. King. 2005. “The Incredible Volcker Disinflation.” Journal of Monetary Economics 52: 981–1015.Google Scholar

  • Hamilton, J. 1994. Time Series Analysis. Princeton: Princeton University Press.Google Scholar

  • Hansen, B. E. 1996. “Inference when a Nuisance Parameter is not Identified under the Null Hypothesis.” Econometrica 64: 413–430.Google Scholar

  • Hansen, B. E. 1999. “Testing for linearity.” Journal of Economic Surveys 13: 551–576.CrossrefGoogle Scholar

  • Hansen, B. E. 2000. “Sample Splitting and Threshold Estimation.” Econometrica 68: 573–603.Google Scholar

  • Inoue, A., and B. Rossi. 2011. “Identifying the Sources of Instabilities in Macroeconomic Fluctuations.” The Review of Economics and Statistics 93: 1186–1204.Web of ScienceGoogle Scholar

  • Ireland, P. 2007. “Changes in the Federal Reserve’s Inflation Target: Causes and Consequences.” Journal of Money, Credit and Banking 39: 1851–1882.Google Scholar

  • Justiniano, A., and G. Primiceri. 2008. “The Time Varying Volatility of Macroeconomic Fluctuations.” American Economic Review 98: 604–641.Web of ScienceGoogle Scholar

  • Kapetanios, G. 2000. “Small Sample Properties of the Conditional Least Squares Estimator In Setar Models.” Economics Letters 69: 267–276.Google Scholar

  • Kilian, L. 1998. “Small Sample Confidence Intervals for Impulse Response Functions.” The Review of Economics and Statistics 80: 218–230.Google Scholar

  • Koop, G., M. H. Pesaran, and S. M. Potter. 1996. “Impulse Reponse Analysis in Nonlinear Multivariate Models.” Journal of Econometrics 74: 119–147.CrossrefGoogle Scholar

  • Laxton, D., and P. N. Diaye. 2002. “Monetary Policy Credibility and the Unemployment-Inflation Nexus: Some Evidence from Seventeen Oecd Countries.” IMF Working Paper.Google Scholar

  • Levin, A., and J. B. Taylor. 2010. “Falling Behind the Curve: A Positive Analysis of Stop-Start Monetary Policy and the Great Inflation.” NBER working paper, n. 15630.Google Scholar

  • Lo, M. C., and J. Piger. 2005. “Is the Response of Output to Monetary Policy Asymmetric? Evidence from a Regime-Switching Coefficients Model.” Journal of Money, Credit, and Banking 37: 865–886.Google Scholar

  • Orphanides, A. 2011. “Monetary Policy Rules Based on Real-Time Data. American Economic Review 92: 115–120.Google Scholar

  • Primiceri, G. 2005. “Time Varying Structural Vector Autoregressions and Monetary Policy.” The Review of Economic Studies 72: 821–852.Google Scholar

  • Ravn, M. O., and M. Sola. 2004. “Asymmetric Effects of Monetary Policy in the United States.” Federal Reserve Bank of St. Louis Review 86: 41–60.Google Scholar

  • Sims, C., and T. Zha. 2006. “Were there Regime Switches in US Monetary Policy?” American Economic Review 96: 54–81.CrossrefGoogle Scholar

  • Taylor, J. B. 1993. “Discretion Versus Policy Rules in Practice.” Carnegie-Rochester Conference Series on Public Policy 39: 195–214.Google Scholar

  • Taylor, J. B. 2007. “Housing and Monetary Policy.” Proceedings, Federal Reserve Bank of Kansas City 463–476.Google Scholar

  • Tong, H. 1990. Non-linear Time Series: A Dynamical System Approach. Oxford: Oxford University Press.Google Scholar

  • Tsay, R. S. 1998. “Testing and Modeling Multivariate Threshold Models.” Journal of American Statistical Association 93: 1188–1202.Google Scholar

  • Van Norden, S. 1995. “Why is it so Hard to Measure the Current Output Gap?” Macroeconomics, EconWPA, 9506001.Google Scholar

  • Weise, C. L. 1999. “The Asymmetric Effects of Monetary Policy: A Nonlinear Vector Autoregression Approach.” Journal of Money, Credit and Banking 31: 85–108.Google Scholar

About the article

Corresponding author: Dr. Ana Beatriz Galvao, University of Warwick, Warwick Business School, CV4 7AL, Coventry, UK, Phone: +44-24-76528202, e-mail:


Published Online: 2013-10-30

Published in Print: 2014-05-01


Our model includes an equation for the policy rate with own lags, output and prices on the right-hand side, similar to a reduced-form Taylor rule, and their parameters may change with the monetary policy stance, which is related with economic conditions.

The one-step-at-time approach estimates first one threshold, then conditional on this value, a second threshold is estimated. Then using the second estimated threshold, a new threshold is estimated. And finally, this procedure is repeated one more time conditional on the new threshold computed in the previous step to deliver the estimates of both thresholds.

Note, however, that Taylor (1993) suggested the rule using output deviations from a linear trend, instead of annual growth as in equation (8). Because a constant growth rate may not be adequate to detrend output over a long sample, as we do, and the problems arising from using filtering methods in real time [as explained by Orphanides (2001)], we consider the use of annual growth as a good proxy for a measure of current economic activity, see also Van Norden (1995).

Specifically, we require at least 30% of observations in each regime. In the case of an SB-ET-VAR model, this restriction applies separately for each subsample. Other papers in the literature normally set the proportion equal to 10 or 15%. However, because of the relative short sample size and the impact that parameter estimates have on impulse responses, we prefer to consider at least 30% of observations in each regime.

Results available on request.

Results are not shown to save space, but are available on request.

We only present results for positive shocks. Preliminary results with the chosen model indicate no significant asymmetries in the dynamic responses from the sign of the shocks even when comparing increases with decreases of 100 basis points.


Citation Information: Studies in Nonlinear Dynamics & Econometrics, Volume 18, Issue 3, Pages 217–236, ISSN (Online) 1558-3708, ISSN (Print) 1081-1826, DOI: https://doi.org/10.1515/snde-2012-0027.

Export Citation

©2014 by Walter de Gruyter Berlin/Boston.Get Permission

Supplementary Article Materials

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[1]
ANA BEATRIZ GALVÃO and MICHAEL T. OWYANG
Journal of Money, Credit and Banking, 2018

Comments (0)

Please log in or register to comment.
Log in