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This paper presents multilevel models that utilize the Coxian phase-type distribution in order to be able to include a survival component in the model. The approach is demonstrated by modeling patient length of stay and in-hospital mortality in geriatric wards in Italy. The multilevel model is used to provide a means of controlling for the existence of possible intra-ward correlations, which may make patients within a hospital more alike in terms of experienced outcome than patients coming from different hospitals, everything else being equal. Within this multilevel model we introduce the use of the Coxian phase-type distribution to create a covariate that represents patient length of stay or stage (of hospital care). Results demonstrate that the use of the multilevel model for representing the in-patient mortality is successful and further enhanced by the inclusion of the Coxian phase-type distribution variable (stage covariate). [ABSTRACT FROM PUBLISHER]

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]

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]