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Erschienen in: Empirical Economics 2/2018

11.01.2017

House prices, credit and the effect of monetary policy in Norway: evidence from structural VAR models

verfasst von: Ørjan Robstad

Erschienen in: Empirical Economics | Ausgabe 2/2018

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Abstract

This paper investigates the responses of house prices and household credit to monetary policy shocks in Norway, using Bayesian structural VAR models. The analysis indicates that the effect of a monetary policy shock on house prices is large, while the effect on household credit is muted. This is consistent with a relatively small refinancing rate (refinancing rate refers to the share of outstanding mortgages that are refinanced each period due to changes in, for example, house prices or interest rate) of the mortgage stock each quarter. Using monetary policy to guard against financial instability by mitigating property-price movements may prove effective, but trying to mitigate household credit may prove costly in terms of GDP and inflation variation.

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Fußnoten
1
Reinhart and Rogoff (2014), Bagliano and Claudio (2012), Lall et al. (2009).
 
2
For example, Svensson (2010).
 
3
For example, Borio and Zhu (2008).
 
4
Monetary Policy Report 1/14.
 
5
See Kohn (2006) for an elaboration of this argument.
 
6
Basel Committee on Banking Supervision (2010), Norges Bank (2013).
 
7
The next section describes the data transformations. The data sources are reported in “Appendix 1”.
 
8
See “Appendix 2” for a detailed description of the prior.
 
9
Christiano et al. (1999) provide an overview of identification of monetary policy shocks in structural VARs.
 
10
These restrictions are consistent with the DSGE model in Iacoviello (2005).
 
11
See Faust and Leeper (1997) for a further discussion of long-run restrictions.
 
12
Ordering: 1 GDP, 2 Inflation, 3 Credit, 4 Exchange rate, 5 House Prices, 6 Interest rate.
 
13
Suggesting that the level of real credit is integrated of order 2.
 
14
I remove growth cycles longer than 8 years as suggested in Christiano and Fitzgerald (2003).
 
15
This ratio does not change if real house price growth is also filtered using a band-pass filter, as almost all of the variation in real house price growth is business cycle variation.
 
16
Figure 4 also shows a cross-check where an HP filter with \(\hbox {lambda} = 1600\) is used instead of the band-pass filter. The choice of filtering technique does not seem to have a impact on the impulse responses.
 
17
Estimating a monetary policy shock where both the exchange rate and house prices are ordered after interest rates in the Choleski ordering does not change the impulse responses or reduce the price puzzle in this model.
 
18
See, for example, Iacoviello (2005).
 
19
Share of mortgages with fixed interest rate(approximately): Norway 10%, USA 85%, Sweden 40% [Source: Statistics Norway and Calza et al. (2013)].
 
20
I do not allow for more variables due to the curse of dimensionality problem. The number of parameters in a VAR increases exponentially with the number of variables.
 
21
OLS estimates of the parameters are used in this exercise.
 
22
In a Diebold–Mariano test, 250 of the 400 reduced form models have a significantly higher MRSE than the “best” model(at a 5% level). The main reduced form model presented in this paper has the 23rd lowest MRSE, not significantly higher than the best model.
 
23
GDP growth, residential investment growth and consumption growth.
 
24
Core inflation and domestic inflation.
 
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Metadaten
Titel
House prices, credit and the effect of monetary policy in Norway: evidence from structural VAR models
verfasst von
Ørjan Robstad
Publikationsdatum
11.01.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
Empirical Economics / Ausgabe 2/2018
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI
https://doi.org/10.1007/s00181-016-1222-1

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