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The effects of the monetary policy stance on the transmission mechanism

  • Ana Beatriz Galvao EMAIL logo und Massimiliano Marcellino
Veröffentlicht/Copyright: 30. Oktober 2013
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Studies in Nonlinear Dynamics & Econometrics
Aus der Zeitschrift Studies in Nonlinear Dynamics & Econometrics Band 18 Heft 3

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.

Keywords: asymmetries; great moderation; monetary policy transmission; threhold models; time-varying models
JEL classification: E52; C51
Corresponding author: Dr. Ana Beatriz Galvao, University of Warwick, Warwick Business School, CV4 7AL, Coventry, UK, Phone: +44-24-76528202, e-mail:
  1. 1

    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.

  2. 2

    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.

  3. 3

    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).

  4. 4

    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.

  5. 5

    Results available on request.

  6. 6

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

  7. 7

    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.

Acknowledgement

We would like to thank the Editor Bruce Mizrach, two anonymous Referees, and seminar participants at Manchester, Queen Mary, the 2010 World Meeting of the Econometric Society, and the 2011 SNDE Symposium for useful comments and suggestions.

Appendix A: Computation of impulse response functions

In this Appendix, we describe how we compute both conditional means required for the computation of gr, j, s (equation 5). Based on the estimates of Φi(r) and of the thresholds, we can draw an s×m matrix from each N(0, Σ(r)) (for r=1, 2, 3) such that sequences of yt+1∗, ..., yt+s∗ can be computed using one row of Ωt(r) as initial value. The gr, j,s will be the difference between two average sequences of yt+1∗, ..., yt+s∗: one with vj, t+1(r)=aj(r) and the other with vj, t+1(r)=0. By using the same draws from each N(0, Σ(r)) to compute both conditional means, we guarantee that the only difference between them is the effect of the structural shock at t+1. Note also that the gr, j, s is the average across all vector of histories in Ωt(r). This means that if Tr=50 and we draw 1000 times from N(0, Σ(r)), the gr, j, s is computed using the average across 50*1000 replications.

Appendix B: Confidence intervals for the parameters and impulse response functions

Let us label g^r, j, s the impulse response function based on the conditional maximum likelihood estimates Φ^i(r),Σ^(r),c^1 and c^2. As described by Canova (2007, p. 134), a typical issue in applying the bootstrap to compute confidence intervals for impulse responses obtained from linear models is that the bootstrapped distributions are not scale invariant, implying that standard error bands may not include the point estimates. In addition, VAR estimates using small samples are severely downward biased. Unfortunately, techniques of bias correction such as those described in Kilian (1998) cannot be applied since the uncertainty in the ET-VAR parameters depends strongly on the uncertainty about the threshold estimation, while the empirical distribution of the threshold estimates may be quite asymmetric (Kapetanios 2000).

Our bootstrap approach attempts to solve some of these issues. The first step is to draw with replacement sequences of length T–p from all εt(r) (r=1, 2, 3), and use the estimates Φ^i(r),Σ^(r),c^1 and c^2 and initial values of yt (t=1, …, p) to generate bootstrapped sequences of yp+1∗∗, . . ., yT∗∗. For each of these sequences, conditional maximum likelihood is applied to obtain estimates of all the parameters, that is, Φ^i∗∗(r),Σ^∗∗(r),c^1∗∗ and c^2∗∗. Using these parameters and the specific bootstrapped sequence, we compute gr, j, s∗∗. using the simulation procedure described previously. By repeating the bootstrapped procedure B times, an empirical distribution for the gr, j, s is obtained.

Using the B values of gr, j, s∗∗, we compute μgrfr.j.s∗∗=1/B∑b=1Bgr, j, s, b∗∗ and the empirical quantiles qgr.j.s∗∗α/2 and qgr.j.s∗∗(1−α/2) for 100(1–α)% confidence intervals. Using the empirical quantiles and the empirical mean of the impulse response function, the range of the 100(1–α)% confidence intervals is computed, that is, rLO=abs(qgr.j.s∗∗α/2−μgr.j.s∗∗) and rUP=abs(qgr.j.s∗∗(1−α/2)−μgr.j.s∗∗). As a result, centred confidence intervals, but potentially asymmetric and skewed, can be computed as {g^r, j, s−rLO; g^r, j, s+rUP}. We emphasize that these intervals consider uncertainty on both coefficient and threshold estimates.

Using the B values of Σ^∗∗(r),c^1∗∗ and c^2∗∗, we compute empirical quantiles to define the range of confidence intervals for the estimates of the standard deviations of the shock (elements of Λ^(r)) and thresholds.

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Published Online: 2013-10-30
Published in Print: 2014-5-1

©2014 by Walter de Gruyter Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. The effects of the monetary policy stance on the transmission mechanism
  3. Inequality-growth nexus along the development process
  4. Estimating the Wishart Affine Stochastic Correlation Model using the empirical characteristic function
  5. Inventories, business cycles, and variable capital utilization
  6. Factor-based forecasting in the presence of outliers: Are factors better selected and estimated by the median than by the mean?
  7. Estimating VAR-MGARCH models in multiple steps
https://doi.org/10.1515/snde-2012-0027
Schlagwörter für diesen Artikel
asymmetries; great moderation; monetary policy transmission; threhold models; time-varying models

Artikel in diesem Heft

  1. Frontmatter
  2. The effects of the monetary policy stance on the transmission mechanism
  3. Inequality-growth nexus along the development process
  4. Estimating the Wishart Affine Stochastic Correlation Model using the empirical characteristic function
  5. Inventories, business cycles, and variable capital utilization
  6. Factor-based forecasting in the presence of outliers: Are factors better selected and estimated by the median than by the mean?
  7. Estimating VAR-MGARCH models in multiple steps
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