C22 Time-Series Models; Dynamic Quantile Regressions (Updated!)
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I have assessed changes in the monetary policy stance in the euro area since its inception by applying a Bayesian time-varying parameter framework in conjunction with the Hamiltonian Monte Carlo algorithm. I find that the estimated policy response has varied considerably over time. Most of the results suggest that the response weakened after the onset of the financial crisis and while quantitative measures were still in place, although there are also indications that the weakening of the response to the expected inflation gap may have been less pronounced. I also find that the policy response has become more forceful over the course of the recent sharp rise in inflation. Furthermore, it is essential to model the stochastic volatility relating to deviations from the policy rule as it materially influences the results.
We develop a state-space model to decompose bid and ask quotes of CDS into two components, fair default premium and liquidity premium. This approach gives a better estimate of the default premium than mid quotes, and it allows to disentangle and compare the liquidity premium earned by the protection buyer and the protection seller. In contrast to other studies, our model is structurally much simpler, while it also allows for correlation between liquidity and default premia, as supported by empirical evidence. The model is implemented and applied to a large data set of 118 CDS for a period ranging from 2004 to 2010. The model-generated output variables are analyzed in a difference-in-difference framework to determine how the default premium, as well as the liquidity premium of protection buyers and sellers, evolved during different periods of the financial crisis and to which extent they differ for financial institutions compared to non-financials.
We build a novel leading indicator (LI) for the EU industrial production (IP). Differently from previous studies, the technique developed in this paper is able to produce an ex-ante LI that is immune to “overlapping information drawbacks”. In addition, the set of variables composing the LI relies on a dynamic and systematic criterion. This ensures that the choice of the variables is not driven by subjective views. Our LI anticipates swings (including the 2007-2008 crisis) in the EU industrial production – on average – by 2 to 3 months. The predictive power improves if the indicator is revised every five or ten years. In a forward-looking framework, via a general-to-specific procedure, we also show that our LI represents the most informative variable in approaching expectations on the EU IP growth.