This paper evaluates the use of risk-neutral probability density functions implied in 3-month interest-rate futures options to assess market perceptions regarding future monetary policy moves options allow the information content implied in simpler derivatives to be extended by providing...

In this paper we develop a mixed frequency dynamic factor model featuring stochastic shifts in the volatility of both the latent common factor and the idiosyncratic components. We take a Bayesian perspective and derive a Gibbs sampler to obtain the posterior density of the model parameters. This...

This paper presents an application of neural network models to predictive classification for data quality control. Our aim is to identify data affected by measurement error in the Bank of Italy�s business surveys. We build an architecture consisting of three feed-forward networks for...

A time series model in which the signal is buried in non-Gaussian noise may throw up observations that are outliers when judged by the Gaussian yardstick. We describe an observation-driven model, based on an exponential generalized beta distribution of the second kind (EGB2), in which the signal...

We compare two EGARCH models, which belong to a new class of models in which the dynamics are driven by the score of the conditional distribution of the observations. Models of this kind are called dynamic conditional score (DCS) models and their form facilitates the development of a...

In the past decade changes in oil prices have played a significant role in shaping inflation dynamics in the US and in the euro area, largely through their direct effect on fuels prices, reviving the controversy over whether the prices of petroleum products respond more promptly to positive than...

Business investment is a very important variable for short- and medium-term economic analysis, but it is volatile and difficult to predict. Qualitative business survey data are widely used to provide indicators of economic activity ahead of the publication of official data. Traditional...

The aim of the paper is to estimate a reliable quarterly time-series of potential output for the Italian economy, exploiting four alternative approaches: a Bayesian unobserved component method, a univariate time-varying autoregressive model, a production function approach and a structural VAR....

Two main hypotheses are usually put forward to explain the productivity advantages of larger cities: agglomeration economies and firm selection. Combes et al. (2012) propose an empirical approach to disentangle these two effects and fail to find any impact of selection on local productivity...

This paper proposes the use of Bayesian model averaging (BMA) as a tool to select the predictors' set for bridge models. BMA is a computationally feasible method that allows us to explore the model space even in the presence of a large set of candidate predictors. We test the performance of BMA...