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Workshop on resilience

Paris14 June 2007

SVAR analysis of short-term resilience: A summary of the methodological issues and the results for the US and Germany

Alain de SerresOECD Economics Department

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Stylised factsR eal G D P grow th

0 .0

1 .0

2 .0

3 .0

4 .0

5 .0

1998 99 2000 01 02 03 04 05 06 07 2008

O utput gap

-2 .0

-1 .5

-1 .0

-0 .5

0.0

0.5

1.0

1.5

2.0

1998 99 2000 01 02 03 04 05 06 07 2008

G row th dom estic sectors2

0 .0

1 .0

2 .0

3 .0

4 .0

5 .0

6 .0

1998 99 2000 01 02 03 04 05 06 07 2008

G row th in g lobalized sectors3

-6

-4

-2

0

2

4

6

8

10

12

1998 99 2000 01 02 03 04 05 06 07 2008

Euro area U nited S tates O ther English-speaking countries 1

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Empirical examination of resilience

• Defined resilience as the capacity to absorb shocks and to recover quickly following an adverse one.

• Considered SVAR methodology as a well-suited framework to analyse cross-country differences in the response of output and key components of demand to various shocks that can be identified under certain conditions.

• First step: estimation of individual system for G7 countries plus Spain

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SVAR model

• 8 variables VARs including a mixture of foreign and domestic variables

– Foreign real GDP, oil prices, 2 components of aggregate demand (externally- and internally-focussed), real GDP, CPI inflation, government net lending, nominal interest rates

• Shocks identified on basis of restrictions on the matrix of contemporaneous feedback effects on the variables

– Bernanke (1986), Blanchard and Watson (1986), Sims (1986)• Identification scheme allows for “structural” interpretation of

shocks, though interpretation not always straightforward.

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SVAR model: additional restrictions

• Block exogeneity assumption: domestic variables assumed to have no impact – neither contemporaneously nor with lags – on the set of foreign variables (exception for US model)

• Approach used in earlier studies:– Cushman and Zha, 1997: Canada: variables entered in log-

levels with introduction of common linear time trend

– Dungey and Pagan, 2000: Australia: variables de-trended a priori with a linear trend

– Buckle et al., 2002: New Zealand: Data HP-filtered

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Adjustment time for impulse response profiles:average response of output across shocks

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Adjustment time for impulse response profiles:average response of internal demand across

shocks

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Methodological issues • Uniform application of HP filter to de-trend all

variables:– Distortions in the time-series properties led to risk of

spurious regression / correlation results

• Main findings regarding differences in degree of resilience questioned on two grounds– Criterion used : number of periods before IRF crosses

the zero line following a shock– Absence of formal statistical test : could not say

whether differences were statistically significant

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Problems with the HP-filter

• Biased and inconsistent estimates

• Uniform smoothness parameter

• HP filter distorts time-series properties of the variables with risk of spurious correlations

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Alternative treatment of data

– Specification in levels, first-differences or VECM form?

• Even in case of non-stationary variables, estimated coefficients are consistent and the asymptotic distribution of individual parameter is standard, i.e. normal distribution (Sims, Stock and Watson, 1990).

• Impulse response functions are also consistent estimators of the true IRF except in the long run (where they do not converge with a probability of one (Phillips, 1996)).

• In presence of I(1) variables, specification in first-diff. or VECM may lead to more efficient estimates in a finite sample…if restrictions imposed are the correct ones. If not, then system is mis-specified with high risk of biases.

• Bottom line: common practice of transforming models into stationary representations by firs-differencing or using co-integration operators is often unnecessary even if data are likely to be integrated.

• Argument made by Dungey and Pagan (2000) to justify use of linear trend

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Re-estimation of the SVAR system with

linear time trend

• Guay and Pelgrin (2006) re-estimate SVAR for G7 countries with similar specification (same 8 variables)

– Quarterly data over period 1975q1-2004q4

– Variables de-trended on the basis of a linear time trend: common trend and prior de-trending as in Dungey and Pagan (2000)

– Block exogeneity restriction is dropped

– Lag selection on basis of Schwartz criteria: • Two lags chosen in almost all countries

– Bootstrapping method used to correct for small-sample biases (due to presence of persistent series)

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Results for United States and Germany:

2 polar cases

• For both countries, IRFs are generally more protracted than those estimated on the basis of HP-filtered data

• Structural shocks induce more persistent effects on key variables for Germany than for the United States

• Difference in resilience observed in original study not purely artefact of HP-filtering

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Main results for GDP: United States

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Main results: Germany

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Number of quarters before IRF for output crosses the zero line: four shocks

SHOCK TO:

World output External demand

Domestic demand

Oil prices

United States 5 11 11 26 Germany 7 40 20 30

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Are the differences statistically significant?

• 1st test: Evaluate the difference between the impulse responses of the two countries to a given shock and for a given horizon (k, k+1, k+2,…)

• 2nd test: Also based on the difference in the IRFs of the two countries but compares the cumulative impact of a given shock in two countries over a given horizon

• Both cases: Null hypothesis of no difference between the impulse responses of both countries.

– No difference in resilience

• Comparison of IRFs as a test of relative resilience can be rationalised under the assumption that the exogenous dynamic process of the structural shocks disturbing economies is the same for the two countries

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Difference in GDP IRFs: Germany – USimpact over different horizons (first test)

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Difference in GDP IRFs: Germany - US

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Difference in cumulated impulse responses (4Q) Output and domestic demand

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Difference in cumulated impulse responses (4Q) Lower confidence level

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Difference in cumulated impulse responses (12Q) Lower confidence level

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Conclusions

• SVAR systems estimated on data using alternative de-trending methodologies point to differences in resilience: particularly the case if compare US and Germany (but also vis-à-vis Japan)

• Using two statistical tests based on differences in IRFs between countries, difficult to find differences that are statistically significant at conventional confidence levels (Guay and Pelgrin, 2006)

• Possible explanations:– SVAR estimated over (relatively) long period: countries seen as resilient today

(US, UK, Canada) were not so resilient in the early 1980s (and vice-versa for Germany)

– 8 variables SVAR implies a large number of estimated parameters, even with relatively short lags: affects precision.

• Is VAR modelling approach well-suited for investigating the issue of resilience?