Alain de Serres OECD Economics · PDF fileAlain de Serres OECD Economics ... • Even in...
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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?