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A Technical appendix
In this appendix, we first provide a complete description of the simple two-period model and macroprudential tightening in that model. We then present additional details of the quantitative model, including all the first order conditions.
The authors are grateful to our discussants Lorenzo Burlon, Stefan Gebauer and Anton Korinek. We are also indebted to Simon Gilchrist and Mathias Trabandt for very helpful feedback. We would like to thank seminar and conference participants at Bank of Israel, Norges Bank, Berlin School of Economics, Federal Reserve Bank of Cleveland, the Fifth NYU Alumni conference, Greater Stockholm Macro Group, Second Annual Workshop of the ESCB Research Cluster on Monetary Economics, Sveriges Riksbank, the Third Research Conference of MMCN, and the ECB’s Fourth Macroprudential Conference. The paper was previously circulated with a different title. The views expressed in this paper are solely the responsibility of the authors and should not be interpreted as rejecting the views of the IMF, Sveriges Riksbank or any other person associated with these organizations. Jacob Ewertzh and Anna Shchekina provided excellent research assistance.
See e.g. IMF (2015) and the references therein. Some research have even suggested that leaning against the wind policy may be counterproductive and increase indebtedness in the near-term, see e.g. Gelain et al. (2017).
In the following, we call lower mortgage interest deductibility a macroprudential tool because the way we implement the policy avoids any re-distributional effects between borrowers and savers (borrowers fully pay for the mortgage interest rate deductions by lump-sum transfers). Our definition of macroprudential policy is consistent with the one adopted by the ECB, which defines it as any tool which prevents the excessive build-up of risks and smooths the financial cycle over time.
Mian and Sufi (2009) reports U.S. microdata evidence in favor of the role of credit supply factors behind the surge in mortgage debt before the crisis. In our experiment, we assume an increase in LTV ratios from 0.75 to 0.85.
See Mian and Sufi (2011) for microdata evidence on the importance of home equity extraction for the surge in U.S. mortgage debt.
Our parameter restrictions (θ < β and β > 1/ (1 + R)) provide sufficient conditions for consumption to be positive and to simply characterize the eects of MPP. Both restrictions are in line with plausible values for the LTV limits, interest rates and discount factors.
This is equivalent to imposing the constraints
The complete set of first-order conditions is reported in the Appendix.
This last condition follows from
The scaling factor Γc = (1 – ε)/(1 – β) ensures that the marginal utility of consumption is 1/c in the steady state.
Iacoviello and Neri (2010) did not allow for investment adjustment costs. We decided to include them to ensure a conventional monetary policy transmission mechanism, which is important in our ZLB environment.
Specifically, our model implies an aggregate LTI ratio of 83 percent whereas the model which keeps α at 0:79 has an aggregate LTI of 53 percent. The median aggregate LTI of the countries in Figure 1 is close to 200 percent in 2017.
As we have access to granular data from Sweden about the distribution of debt, this part of the calibration focuses on the Swedish case. Furthermore, Sweden appears representative of the countries included in Figure 1.
Source: Sveriges Riksbank’s own calculations using data from the Swedish FSA mortgage survey. This interest rate fixation period is likely a bit shorter than in other countries, and we have therefore checked that our results are robust with respect to this choice (especially the short-term results in Section 5; the long-term effects in Section 4.3 are unaffected by this parameter).
Source: Swedish FSA mortgage survey 2015–2017.
The model has no government consumption, so before presenting any ratio involving GDP we adjust for
Regarding the dynamics, Figure A.1 in the Appendix documents the monetary policy shock impulse responses across models (i.e. for different borrowing constraints) in a high debt environment. The main take-away from this figure is that the monetary transmission mechanism is quite similar across models, with the exception that in the LTV model borrowers’ consumption contracts slightly more.
In the low debt environment, mortgage deductibility is only reduced to 6.35% to obtain a 10.2% reduction in the aggregate loan-to-income ratio.
See Erceg and Lindé (2014) for an detailed discussion of the difference between marginal and average impulses in a liquidity trap.
That is, a reduction in borrowers’ LTI from 433 to about 390 percentage points in the high debt economy.
The strength in the decrease in residential investment might appear extreme. But properly accounting for the volatility of this variable moderates this impression. There are many occasions is the recent history when residential investment has fallen by more than 10 percent within a year, see Figure 1.
We are not describing optimal monetary policy, but simply noting that the Taylor rule used seem appropriate in the sense that it trades off deviations in inflation against deviations in output in response to the LTV tightening.
The figure also includes MID removal which will be discussed in section 5.2.4.