We construct a Bayesian vector autoregressive model with three layers of information: the key drivers of inflation, crosscountry dynamic interactions, and country-specific variables. The model provides good forecasting accuracy with respect to the popular benchmarks used in the literature. We perform a step-by-step analysis to shed light on which layer of information is more crucial for accurately forecasting euro area inflation. Our empirical analysis reveals the importance of including the key drivers of inflation and taking into account the multi-country dimension of the euro area. The results show that the complete model performs better overall in forecasting inflation excluding energy and unprocessed food, while a model based only on aggregate euro area variables works better for headline inflation.

We construct a Bayesian vector autoregressive model with three layers of information: the key drivers of inflation, crosscountry dynamic interactions, and country-specific variables. The model provides good forecasting accuracy with respect to the popular benchmarks used in the literature. We perform a step-by-step analysis to shed light on which layer of information is more crucial for accurately forecasting euro area inflation. Our empirical analysis reveals the importance of including the key drivers of inflation and taking into account the multi-country dimension of the euro area. The results show that the complete model performs better overall in forecasting inflation excluding energy and unprocessed food, while a model based only on aggregate euro area variables works better for headline inflation.

The uncertainty surrounding economic forecasts is generally related to multiple sources of risks, of both domestic and foreign origin. This paper studies the predictive distribution of Italian GDP growth as a function of selected risk indicators, relating to both financial and real economic developments. The conditional distribution is characterized by expectile regressions. Expectiles are closely related to the Expected Shortfall, a well-known measure of risk with desirable properties. Here a decomposition of Expected Shortfall in terms of the contributions of different indicators is proposed, which allows the main drivers of risk to be tracked over time. Our analysis of the predictive distribution of GDP confirms that financial conditions are relevant for the left tail of the distribution but it also highlights that indicators of global trade and uncertainty have strong explanatory power for both the left and the right tail. Their usefulness is also supported in a pseudo real-time predictive context. Overall, our findings suggest that Italian GDP risks have been driven mostly by foreign developments throughout the Great Recession, by the domestic financial conditions at the time of the sovereign debt crisis and by economic policy uncertainty in more recent years.

In this paper we study the business cycle dating formulated by the CEPR committee for the euro area. We first compare recessions as defined by the CEPR to those obtained using alternative methodologies (e.g. Bry-Boschan algorithm) and we find that the CEPR dating is not fully in line with other dating rules that are based only on GDP dynamics, thus confirming that the committee considers a broader set of variables. We then evaluate the classification of economic activity in recessions and expansions; the underlying business cycle is either based on a single variable or estimated as a latent factor that captures the comovements of several macroeconomic series. We find that the CEPR chronology is more consistent with the estimated common factor than with what is implied by methods solely based on GDP. Finally, we analyze which real variables drive the classification of economic activity by the CEPR and we find that the properties of the CEPR chronology are mainly related to the dynamics of demand components, especially final consumption, and employment.