dg economics, research, and statistics discussion of: housing cycles in the major euro area...

DG ECONOMICS, RESEARCH, AND STATISTICS

Discussion of:

Housing Cycles in the Major Euro Area Countries

Common Business and Housing Market Cycles in the Euro area from a Multivariate Decomposition

The International Transmission of House Price Stocks

Gabriel Pérez-Quirós, Bank of Spain

International Research Conference “The Macroeconomics of Housing Markets” Paris, 3-4 December 2009

DG ECONOMICS, RESEARCH, AND STATISTICS2

1. General Impression

Three good papers :

Very timely. They do not need to justify their interest

State of the art econometric techniques. I prefer some to others but

they are all well done.

Big effort in constructing comparable datasets

Even though it is a big task what you asked me to do here, I enjoyed

reading all of them …..and most of the authors are friends and that

helps

I expected similar conclusions….


1. General Impression

However, they should read each other (this is supposed to be a joint

effort):

One doctor, cure, two doctors doubt, three doctors, sure death

Alvarez, Bulligan, Cabrero, Ferrara and Stahl (ABCFS) and Ferrara and Koopman

(FK) conclude: “Housing prices are not synchronized across countries”

De Brandt, Barhoumi and Bruneau (BBB) conclude: “The US real house price

unidirectionally causes the international house price factor, which in turn causes

the domestic real house price growth for each country”

But even ABCFS and FK do not always send the same message: “In Housing

Prices there is a strong relation between Germany and Spain” “Germany and

Spain are strongly but negatively correlated”


2. General concerns (Some papers more than others)

Lack of robustness of the results. Some specifications produce some

results and others produce others. Value of p in ABCFS, trend

specification in KF or the FAVAR specification in BBB.

Out of sample forecasting exercise?

Lack of standard errors- There is no standard errors in any of the

three papers!

Lack of focus. The papers analyze comovements across economies

and comovements between sectors in the economies, (FK),

comovements across economies and leading and lagging behaviors of

a big set of variables (ABCFS), non linearities, causal relations and

different types of propragations (BBB) ….One paper is one message


Lack of a message with policy implications. Just an example:

We find that, in the four major euro area countries, GDP cycles show a high

degree of comovement, most likely due to trade linkages, although

idiosyncratic factors play a larger role in Germany than in the other countries.

Cross country comovements are mostly contemporaneous, but developments in

Spain tend to lead those in Germany, Italy and France

by 1 or 2 quarters. In contrast, comovements are substantially weaker for

housing market cycles, where country-specific or local variables, [..] Again,

residential investment developments in Spain precede those in the other

countries. Nominal prices are weakly related across countries, but

developments in France tend lead those in the other countries.

2. General concerns


Lack of a similar exercise that we can use as a baseline

Correlation between prices and output is not new in the literature

(Den Haan, Cooley and Ohanian, etc….) At the end of the day, this is

the exercise that the three papers try to do. I would have liked to see

first the exercise of GDP-Inflation and then, with the same

specification, GDP-Housing prices.

2. General concerns


Lack of clarity to send the message through:

Strong relation between HP in Germany and Spain (CI equal to 0.69)?

One common unidirectional cause of HP in all countries?

2. General concerns


Lack of clarity to send the message through:

Spain presents the strongest relationship between business and

housing cycles for both short-term and long-term cycles, pointing out

the contribution of the

housing sector to the Spanish economic growth

2. General concerns


3. Detailed comments

On ABCFS:

Why do you filter the data? Growth rates or den Haan (2000) are

easier to interpret

Or if you need to filter, filter based on models as in FK.

How much your results depend on the value of “p”? Why this should

be equal for all variables and all countries?

What is the gain of using the statistic proposed by Peña and

Rodriguez?

Combine measures and produce just one table of summary of the

results

Why Spain leads Germany in GDP?

Leading and lagging behavior of filtered data is misleading

Concordance analysis misleading. When low number of recession,

positively biased. Parametric measures have advantages.



Ilustration on the misleading behavior of filtering data:

Correlation: original series -0.02. Transformed series 0.79

Series, 0.79

0

0.2

0.4

0.6

0.8

1

1.2

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51

-0.01

0

0.01

0.02

0.03

0.04

0.05

0.06

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51



Ilustration on Harding and Pagan statistic: Both pair of countries miss

the same proportion of correlations. However, CI first pair of

countries is 0.57, for the second, 0.43

0

0.5

1

1.5

1 8 15 22 29 36 43 50 57 64 71 78 85 92 99 106

113

120

127

134

141

148

00.51

1.5

1 8 15 22 29 36 43 50 57 64 71 78 85 92 99 106

113

120

127

134

141

148

0

0.5

1

1.5

1 8 15 22 29 36 43 50 57 64 71 78 85 92 99 106

113

120

127

134

141

148

00.51

1.5

1 8 15 22 29 36 43 50 57 64 71 78 85 92 99 106

113

120

127

134

141

148


3. Detailed comments On FK:

Why do you need to enforce the restrictions for the value of the

parameters? Why the same restrictions to all series and countries?

Why GDP and HP are much more correlated in Germany than in Italy

while the picture gives a totally different impression?

How robust are your results to different trends (although I like your

trend)?

Is your trend compatible with ABCFS specification?

Timing of things….this morning we learnt that prices in France

lead GDP and the leading nature of housing in Spain!

I miss the covariance rank reduction analysis that you propose at the

beginning.



On BBB:

Two definitions of contagion. One is “contagion occurs when the

transmission is different, (more pronounced) in crisis events”. Not

clear, but the testing procedures, “dummy for recessions” and LSTAR

are seriously misleading.

Why do you change the number of countries in the non-linear

model?

The second approach, the contagion effect, more standard. However

serious lack of robustness. First, Australia, Spain and UK, later

Autralia, UK and France,

The strategy used by Grocer should be more carefully explained

From the reading of the text, I was not able to figure out how do you

get the conclusion that it is the market in the US what cause all the

movements in markets in Europe


4. To sum up

From my point of view, three interesting papers that try to put some

light on the relation between housing prices and GDP but…


4. To sum up



…..still to messy to send a clear message that can go through (in

some papers more than in others)


4. To sum up



…..still to messy to send a clear message that can go through (in

some papers more than in others)

….more research is needed. I do not sympathize with the message

that housing prices move on their own. My own experience with soft

indicators and lessons that we have learnt from oil prices have shown

us that the design of the filter is key to identify comovements

dg economics, research, and statistics discussion of: housing cycles in the major euro area...

Documents