The forecasting uncertainty around point macroeconomic forecasts is usually measured by the historical performance of the forecasting model, using measures such as root mean squared forecasting errors (RMSE). This measure, however, has the major drawback that it is constant over time and hence...

In this paper we estimate a Bayesian vector autoregressive model with factor stochastic volatility in the error term to assess the effects of an uncertainty shock in the Euro area. This allows us to treat macroeconomic uncertainty as a latent quantity during estimation. Only a limited number of...

Using variance decompositions in vector auto-regressions (VARs) we model a high-dimensional network of European CDS spreads to assess the transmission of credit risk to the non-financial corporate sector. Our findings suggest a sectoral clustering in the CDS network, where financial institutions...

The effect of a firm's size is analysed in relation with the probability of applying for a patent. We worked on the identification of a firm's minimum size, a threshold needed to formally protect an innovation by legal means. Below this minimum size the costs associated with protection are so...

The present paper develops Adaptive Trees, a new machine learning approach specifically designed for economic forecasting. Economic forecasting is made difficult by economic complexity, which implies non-linearities (multiple interactions and discontinuities) and unknown structural changes (the...

We define tail interdependence as a situation where extreme outcomes for some variables are informative about such outcomes for other variables. We extend the concept of multiinformation to quantify tail interdependence, decompose it into systemic and residual interdependence and measure the...

We investigate how often replication studies are published in empirical economics and what types of journal articles are eventually replicated. We find that from 1974 to 2014 0.10% of publications in the Top 50 economics journals were replications. We take into account the results of replication...

Computational methods to gauge investor sentiment from commonly used online data sources that rely on machine learning classifiers and lexicons have shown considerable promise, but suffer from measurement and classification errors. In our work, we develop a simple, direct and unambiguous...

This paper investigates in a non-linear setting the impact on the real economy of frictions stemming from the financial sector. We develop a medium scale DSGE model with a banking sector where an occasionally binding constraint on banks' capital induces a relevant non-linearity. The model -...

Hungarian debt level has steadily increased since 2001, with the debt-to-GDP ratio reaching about 84% at end-2011. This high level combined with significant volatility of macroeconomic variable influencing potential future debt paths – GDP growth, exchange rate and interest spreads – put...