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Conference on Macroeconomics of Housing Markets 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: Ø Assess the vulnerability of the economy to Housing markets (Hence the importance of finding cross correlations) Ø 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) Ø 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? q 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 q 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 Ø 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 Ø 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 Ø 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 Ø 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) Ø 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 ? Ø Which variables were at play during the last 10 years to explain the booming in the housing investment and the upward price trend? q q 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) Ø 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. Ø 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) Ø Now, when the bubbles burst, there are two indications: w 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 (!) w 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 w Selection bias if the period includes episodes of huge price decrease (may explain the strong positive correlations that are found) w Concerning the lead and lags effects: the approaches do not handle the house price gap
Second unsolved puzzle (1) Spurious short-term cross-correlation ? w Except in Spain and Ireland, the residential investment does not account for a large share of the economies. w Ratio of housing construction in % of GDP : 6, 5% for the advanced economies and over the past 3 decades : 5%. w 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 w Again a problem of selection bias : the correlation found include periods of huge -downward - corrections in the samples w 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 w The papers argue that cyclical componenst of the housing variables affect the cyclical upturns and downturns of the GDP. w 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 w 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 w 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? w 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) w There is one filter that may overperform the ones described : wavelet for several reasons (simultaneous description of the high frequency and low-frequency components) w Compared with the butterworth filter, you do not need to eliminate some « high » or « low » frequencies, because the filter is « multiscale » w 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) w One problem : your empirical results sometimes contradict theoretical findings. w 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) : w 1/ obtain simulated series from the DSGE models, for a given set of calibrated parameters w 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) w 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 w 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 ? ). w 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 « 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