We estimate the natural rate of interest for the US and the euro area in a semi-structural model comprising a Taylor rule. Our estimates feature key elements of Laubach and Williams (2003), but are more consistent with using conventional policy rules: we model inflation to be stationary, with...

This paper aims to illustrate how weight matrices that are needed to construct foreign variable vectors in Global Vector Autoregressive (GVAR) models can be estimated jointly with the GVAR's parameters. An application to real GDP and consumption expenditure price inflation as well as a...

This paper proposes mixed-frequency distributed-lag (MFDL) estimators of impulse response functions (IRFs) in a setup where (i) the shock of interest is observed, (ii) the impact variable of interest is observed at a lower frequency (as a temporally aggregated or sequentially sampled variable),...

We propose to treat survey-based density expectations as compositional data when testing either for heterogeneity in density forecasts across different groups of agents or for changes over time. Monte Carlo simulations show that the proposed test has more power relative to both a bootstrap...

This paper develops a rigorous econometric framework to investigate the structure of codependence between random variables and to test whether it changes over time. Our approach is based on the computation - over both a test and a benchmark period - of the conditional probability that a random...

We compare the degree of anchoring of inflation expectations in the euro area, the United States and the United Kingdom, focusing on the post-crisis period. First of all, we estimate a set of measures of average and tail correlation using inflation swaps and options, following Natoli and...

We identify a set of “rules of thumb” that characterise economic, financial and structural conditions preceding the onset of banking and currency crises in 36 advanced economies over 1970-2010. We use the Classification and Regression Tree methodology (CART) and its Random Forest (RF)...