A standard model-based trend-cycle decomposition of Italian GDP yields a likelihood function that is relatively flat and has two local maxima. A Bayesian estimation of the model identifies output gap and trend components that match the features of the Italian business cycle well. In a bivariate...

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...

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....

type="main" xml:id="jtsa12081-abs-0001"A time-series model in which the signal is buried in noise that is non-Gaussian may throw up observations that, when judged by the Gaussian yardstick, are outliers. We describe an observation-driven model, based on an exponential generalized beta...

Algorithms are presented for computing mean square errors in a misspecified unobserved components model when the true model is known. It is assumed that both the true and misspecified models can be put in linear state space form. The algorithm for filtering is based on the Kalman filter while...