We map ECB policy communication into yield curve changes and study the information flow on policy dates. A byproduct is the publicly available Euro Area Monetary Policy Event- Study Database (EA-MPD), containing intraday asset price changes. We find that Policy Target, Forward Guidance and Quantitative Easing factors capture about all the variation in the yield curve, with different factors appearing in the windows covering the policy decision announcement and the press conference, and having time-varying variance shares. We study sovereign yields, exchange rates, stock prices, persistence of effects and response asymmetry. Our methodology can be implemented for any policy-related event.
I develop a two-step subset selection procedure to extract the best-performing predictors from a large dataset and combine them to identify a set of best-performing models. I apply the methodology to build an index to forecast the probability of having the euro area year-on-year inflation below the 2% level in a medium-term horizon--i.e., the Deflationary Pressure Index (DPI). I compare the index with the probabilities reported in the European Central Bank Survey of Professional Forecasters (ECB SPF) and show that, although the indexes are comoving, the DPI is more operationally convenient and timely in catching the inflation turning points. As a final exercise, in a real out-of-sample forecast, the index shows that having medium-term inflation above the 2% level before 2019 is unlikely. .
I compare the performance of the VAR impulse response function (IRF) estimator with the Jordà (2005) local projection (LP) methodology. I show by a Monte Carlo exercise that when the data generating process (DGP) is a well-specified vector autoregressive model (VAR), the standard estimator is a better alternative. However, in the general case in which the sample size is small, and the lag length of the model is misspecified, the local projection estimator is a competitive alternative to the standard VAR impulse response function estimator. Along the way, I highlight some lack in the local projection literature, which can lead to potential improvement in the estimation procedure.
We analyse the macroeconomic effects of a debt consolidation policy in the Euro Area mimicking the Fiscal Compact Rule (FCR). The rule requires the signatory states to target a debt-to-GDP ratio below 60%. Within the context of Dynamic Stochastic General Equilibrium models (DSGE), we augment a fully micro-founded New-Keynesian model with a parametric linear debt consolidation rule, and we analyse the effects on the main macroeconomic aggregates. To fully understand its implications on the economy, we study different debt consolidation scenarios, allowing the excess debt to be re-absorbed with different timings. We show that including a debt consolidation rule can exacerbate the effects of the shocks in the economy by imposing a constraint on the public debt process. Secondly, we note that the effect of loosening or tightening the rule in response to a shock is heterogeneous. Shocks hitting nominal variables (monetary policy shock) are not particularly sensitive. On the contrary, we prove that the same change has a more pronounced effect in case of shock hitting real variables (productivity and public spending shocks). Finally, we show that the macroeconomic framework worsens as a function of the rigidity of the debt consolidation rule. As a limiting case, we show that the effects on output, employment, real wages, inflation, and interest rates are sizable.
We analyze the two macroeconomic releases that mostly impact the US market. The Bureau of the National Statistics (BNS) non-farm-payroll (NFP) and the manufacturing index published by the Institute for Supply Management (ISM). We examine the unexpected component of these two, as measured as deviation from the Bloomberg economist consensus. We label it as the market surprise, and we investigate whether its structure is partially predictable and in which cases. Secondly, we explore the effect of the surprises on different asset classes using intraday data. We show in a regression framework that although the in-sample fit is sufficiently good, the out-of-sample prediction hardly beat a simple autoregressive model. Finally, we present an out-of-sample analysis in a short period after the release (thirty minutes). We demonstrate that under certain circumstances there is some structure that can be exploited.