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46 Deleveraging, What Deleveraging decline in the asset-to-income ratio, housing prices and residential investment. Household deleveraging has also been associated with (and possibly made possible by) an unusually aggressive fiscal policy stance in 2009-10 (as shown by the path of public saving in the chart) which is matched by an ‘exceptional’ increase in private saving. Interestingly, the exceptional deleveraging of the financial sector has not implied an exceptional deleveraging of non-financial corporations, which have behaved in line with their past correlation with business cycle dynamics. Monetary policy, although it has been very aggressive, was constrained by the zero lower bound for a few years after the crisis, which is clearly shown by the fact that the counterfactual path of the three-month interest rate has been below zero. The results also suggest that quantitative easing in 2009-10 was more effective in supporting stock prices. The quick rebound in stock prices is likely to be the explanation for the recovery of household assets relative to personal income. An interpretation of these findings is that the exceptional deleveraging of households has been associated with a shock to the value of assets, driven by a collapse in housing prices, and cannot be explained solely by the shock on income. We elaborate more on this point in the next sub-section, when we compare the US and the Eurozone cases. Facing that, policy has been supportive of the process of adjustment – via income through the fiscal tool, and via asset prices through quantitative easing. Clearly, the output shock, and even its persistence, cannot alone explain the deleveraging process of households and the financial sector since the crisis. Specific features of the last crisis, such as the pre-crisis high level of debt and the exceptional collapse of housing prices affecting the value of household assets, are a big part of the story.
Case studies 47 Box 4.1 The model Let X t be the vector including the n variables (all variables are in log-levels, except for variables expressed in rates or with negative levels that are in levels). We estimate a VAR model with p (=4) lags: X t = A 0 + A 1 X t-1 + A 2 X t-2 +…+ A p X t-p +e t where e t is a normally distributed multivariate white noise with covariance matrix Σ. The large dimension (n=18 and p=4) of our VAR model implies that we face an issue of over-fitting due to the large number of parameters (‘curse of dimensionality’). We address this issue by shrinking the model’s coefficients toward those of the naïve and parsimonious random walk with drift model, X it = δ i + X i,t-1 + e it . De Mol et al. (2008) and Banbura et al. (2010) have shown that this approach reduces estimation uncertainty without introducing substantial bias. The counterfactual (scenario) The counterfactual is performed as follows. We simulate the model’s parameters from their full posterior density, hence accounting for the estimation uncertainty of the hyperparameters. This approach is implemented using a simple Markov chain Monte Carlo algorithm. In particular, we use a Metropolis step to draw the low dimensional vector of hyperparameters (for details, see Giannone et al., 2010). Conditional on a value of the hyperparameters, the VAR coefficients can then be drawn from their posterior, which is normalinverse-Wishart. Since we are interested in conditioning on the economic relationships prevailing before the last recession, the posterior is computed using the data until 2007:Q4. For any given draw of the model’s parameters from their posterior density, the draws from the counterfactual exercise are computed as conditional forecasts in which the conditioning information is given by the 1983:Q1-2007:Q4 history of all variables in the model and the observed outcomes of the level of real GDP and CPI until the end of the sample in 2013:Q3.
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Geneva Reports on the World Economy
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International Center for Monetary a
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International Center for Monetary a
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Acknowledgements We thank Susan Lun
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List of Conference Participants Edm
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Discussions 99 In response to Laure
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Data Appendix Debt typically expres
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References Adrian, T., E. Moench an
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References 105 Jorda, O., M. Schula
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It is widely accepted that high lev
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