Download Gilles - Banque de France

Comments on the Papers of session 1 A
By Gilles Dufrénot (University of Aix-Marseille and Banque de France)
Plan of the discussion
Le cours s’articule autour de trois points:
I.-GENERAL COMMENTS
The questions, the methodology and the main results
 Still unsolved puzzles (!)
II.-SPECIFIC COMMENTS (methodological aspects)
 Paper by Bulligan
 Paper by Alvarez and Cabrero
 Paper by Ferrara and Vigna
GENERAL COMMENTS
Le cours s’articule autour de trois points:
Why the « agnostic »
approach leaves us with
unsolved puzzles
What are we interested in?
Le cours s’articule autour de trois points:
Three important goals:
 Assess the vulnerability of the economy to Housing markets
(Hence the importance of finding cross correlations)
 Try to identify the main channels of transmission of the shocks
from the housing sector to the economy (hence the importance of
using several indicators of the housing activity)
 Prospective approach : find leads and lags dynamics to say whether
housing variables can be chosen as advanced indicators of the GDP
How can we do that?
 Theoretical models : typically Dynamic Stochastic
General Equilibrium models  growing literature
(appropriate to study how productivity shocks,
technological shocks, monetary shocks, etc… affect the
agents’ decisions of investment in the housing sectors
and the impact on the GDP
 Statistical models (« agnostic » about the main
transmission channels)  only want to detect
correlations, leads and lags dynamics, common factors
in the series
The three papers adopt the second
approach (1)
1.- Country-specific studies based on time
series

The paper by Bulligan deals with the case of Italy and
use a structural VAR framework to study the effects of
monetary policy shocks  What’s new : the sign
restriction approach in the long-run matrix to
define the sequance of shocks

The paper by Alvarez and Cabreo deals with the case
of Spain  What’s new : butterwoth filters to study
the cross –correlation between variables of the
housing sector and macroeconomic variables
The three papers adopt the
second approach (2)
1.- Country-specific studies based on time series

The paper by Ferrara and Vigna deals with the case of France.
What’s new : The second part of the paper (!) that
discusses the long-run cross correlations betwwen the
housing markets and the economic variables; this allows
them to explain why France was less affected by the sharp
decrease in the housing prices
The three papers adopt the
second approach (3)
2.- All three papers yields similar conclusions

The find strong correlations between the business cycle and
the cycle of the housing activity (whichever variables are used
to capture the dynamics in either sector or the other)

They conclude that the activity in the housing sector can be
considered as a leading indicator of the business cycles.
First unsolved puzzle (1)
1.- What about the house price gap ?

Which variables were at play during the last 10 years to explain
the booming in the housing investment and the upward price
trend?

Fundamental variables : growth of disposable income per –
capita, long-term and short-term interest rates, inward
immigration flows (Spain), credit growth, changes in equity
prices, etc..

Non-fundamental variables (% of increase in the housing
prices , not explained by the fundamentals)
First unsolved puzzle (2)
First unsolved puzzle (3)
 If non-fundamentals are at play during the booms, then
we can expect a sharp correction when the prices and
activity in the housing sector decline.
 So, during upwards, when the house price are over-
valued, we expect to find a weaker correlation with the
GDP (because you have a bubble)
First unsolved puzzle (4)
 Now, when the bubbles burst, there are two indications:
 Firstly, they do so when some macroeconomic fundamentals
begin to deteriorate (income, unemployment, credit
conditions, etc….  In this case the GDP growth may be a
leading indicator of the activity in the housing sector (!)
 Secondly, deteriorating macroeconomic conditions are the
source of downward revisions in expectations  Stronger
correlations betweeen the GDP and the activity in the
housing sector
First unsolved puzzle (5)
Implications for the time series-based approaches
 Selection bias if the period includes episodes of huge
price decrease (may explain the strong positive
correlations that are found)
 Concerning the lead and lags effects: the approaches do
not handle the house price gap
Second unsolved puzzle (1)
Spurious short-term cross-correlation ?
 Except in Spain and Ireland, the residential investment
does not account for a large share of the economies.
 Ratio of housing construction in % of GDP : 6,5% for
the advanced economies and over the past 3 decades :
5%.
 Accordingly, for the correlation between the housing
sector and the GDP to be strong, there must be large
corrections in the housing construction!!
Second unsolved puzzle (2)
Two consequences for the time series models
 Again a problem of selection bias : the correlation found
include periods of huge -downward - corrections in the
samples
 Choice of the variables: even if we accept the idea that
the housing sector activity leads the GDP, the variable of
interest should be the investment rate in this sector (real
residential investment to GDP)
Third unsolved puzzle
The papers focus too much on the short-term, but the
long-run correlation may also be important
 The papers argue that cyclical componenst of the
housing variables affect the cyclical upturns and
downturns of the GDP.
 However, for downturns in the GDP, it is known that
they occur in the industrialized countries when the
ratio of housing investment to GDP evolve below its
historical trend !
 This implies that the trend components of the housing
variables affect the cyclical components of the GDP
SPECIFIC COMMENTS
Le cours s’articule autour de trois points:
Paper by Guido Bulligan
Cyclical andtrend growth
analyses
Business approach
 The old methodology by Burns and Mitchell has been
updated by Harding and Pagan (2002), Journal of
Monetary Economics  link between the turning points
and the moments of the series + cycles are obtained as
regards their contribution to volatility, trend growth,
correlation and non-linear effects.
Missing nonlinearities in the series + structural
breaks
 Use non-parametric filters such as polyspectra
(bispectraum, trispectrum) + evolutionary sprectrum
VAR models (1)
VAR analysis to study the implication of monetary
policy shocks
 Isn’t there a problem of « multiscale »: housing markets
are characterized by long cycles with a persitent
dynamics, as compared with the other macroeconomic
variables in the VAR? Is it possible to estimate the effects
of a shock by considering a VECM?
 There is a similar study as yours done by Carlos Vargas-
Silva (2009) for the US (forthcoming in the Journal of
Macroeconomics), showing that
VAR models (2)
VAR analysis to study the implication of monetary
policy shocks
1/ the magnitude of monetary shocks on the housing
markets is very dependent on the selection of the
horizon for which the restrictions hold in the VAR ;
2/ as compared with classical choleschi decomposition ,,
the impact of monetary policy on the housing market is
much less certain with the sign restriction approach.
Do you find similar things for Italy?
SPECIFIC COMMENTS
Le cours s’articule autour de trois points:
Paper by Luis Alvarez and
Alberto Cabrero
Filters(1)
 There is one filter that may overperform the ones
described : wavelet for several reasons (simultaneous
description of the high frequency and low-frequency
components)
 Compared with the butterworth filter, you do not need
to eliminate some « high » or « low » frequencies,
because the filter is « multiscale »
 Compared with the Kernel regression, Wavelet
decomposition is also non-parametric, but the analysis
is done in the « frequency domain » which is more
appropriate for the study of business cycles then time
domain methodologies
Comparison with DSGE models (1)
 One problem : your empirical results sometimes
contradict the theoretical findings.
 But, it does not mean that the DSGE models are wrong!
The negative or positive response to shocks depends upon
1/ the interval of variation of the parameters that serve to
calivrate the models and
2/ upon the nature of the technological, productivity,
monetary shocks.
How can you use your time-series based filters to see
whether your findings do indeed contradict the
conclusions of the models?
Comparison with DSGE models (2)
Three steps (Monte Carlo) :
 1/ obtain simulated series from the DSGE models, for a
given set of calibrated parameters
 2/ Apply the Butterworth and Kernel filters . Do this a
number of times (example 1000 times); because, the
effects of shocks may be nonlinear, you must look at the
Generalized impulse response functions (GIRF)
 3/ Compare the population of cross-correlations
between the housing/residential investment and GDP
with the cross-correlation you find when using the
statistical data.
Alternative methodologies for
asymmetry
 Problem with the measures of brevity, violence,
steepness : you do not know which variables are at play
to account for the observed assymetries (credit
constraints? Capacity constraints, labour markets ?).
 Alternative models including transition variables
- Deterministic models such as TAR or STAR models
- Stochastic models such as Markov Switching models
with endogenous probability of transition.
SPECIFIC COMMENTS
Le cours s’articule autour de trois points:
Paper by Laurent Ferrara
and Olivier Vigna
Choice of the 2-step version of the
HP filter
Motivation : why using this filter if other filters yield
similar turning points? Are there any robustness study
elsewhere in the literature?
One question : to which extend can we say that turning
point in the housing sector are causing those observed
in the GDP? May be we are simply detecting common
factors
Something original : the use of Confidence indicator in
the building sector (perception of the activity by the
housing industrials)  capture the supply side of the
housing market
Long-run analysis (1)
Most interesting part of the paper, but no rigourous
statistical analysis to test the arguments. This seems a
promising area of research because it relies on the
fundamentals of then housing markets
The conclusions challenges those of the IMF. While the
authors argue that the movements in the housing prices
and investment were smaller in magnitude as compared
with the other european countries (Spain, the UK,
Ireland), the IMF finds that France was among the
countries with highest overvalued house price (20%)
and a housing ratio investment significantly above the
historical trend.
Long-run analysis (2)
The IMF concludes that France was among the countries
that should experience the largest decrease in the
housing prices due to the the « greatest exuberance » in
the house price.
It would be interesting to see how the authors’ arguments
can be corroborate by a statistical analysis and why their
depart from the IMF findings.
Thanks for your attention