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The aim of this paper is to achieve a reliable estimate of the output gap for Italy through the development of several models within the class of the unobserved component time series models. These formulations imply the decomposition of output into a trend component (the ‘potential output’) and a cycle component (the ‘output gap’). Both univariate and multivariate methods will be explored. In the former, only one measure of aggregate activity, such as GDP, is considered; in the latter, unemployment and industrial production are introduced. A comparison with alternative measures of output gap, mainly those published by international organisations, will conclude. [ABSTRACT FROM AUTHOR]

This paper compares the forecasting performance of three alternative factor models based on business survey data for the industrial production in Italy. The first model uses static principal component analysis, while the other two apply dynamic principal component analysis in frequency domain and subspace algorithms for state-space representation, respectively. Once the factors are extracted from the business survey data, then they are included into a single equation to predict the industrial production index. The forecast results show that the three factor models have a better performance than that of a simple autoregressive benchmark model regardless of the specification and estimation methods. Furthermore, the state-space model yields superior forecasts amongst the factor models. [ABSTRACT FROM AUTHOR]