La recessione mondiale innescata dalla crisi finanziaria si è ripercossa con straordinaria violenza sull'attività economica dell'Italia. Qual è stato il contributo dei diversi canali mediante i quali la crisi si è trasmessa alla nostra economia? Quali sono stati gli effetti delle reazioni delle politiche economiche? Per dare risposta a questi interrogativi, in questo lavoro viene realizzata un'indagine controfattuale dell'evoluzione dell'economia italiana nell'arco temporale 2008-2010, esplorando scenari coerenti con l'ipotesi di "assenza di crisi". Si valuta che gli eventi seguiti alle turbolenze finanziarie abbiano sottratto 6,5 punti percentuali alla crescita del PIL nel triennio. In particolare, i fattori di crisi avrebbero gravato per quasi 10 punti percentuali, prevalentemente nel 2009; le politiche economiche e gli stabilizzatori automatici ne avrebbero mitigato l'impatto per circa 3,5 punti percentuali. La maggior parte degli effetti della crisi sarebbe attribuibile all'evoluzione del contesto internazionale; un ruolo meno rilevante, sia pure non trascurabile, avrebbero avuto il peggioramento delle condizioni di finanziamento delle imprese e la crisi da sfiducia che si è accompagnata alla recessione.
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 distribution of the second kind (EGB2), in which the signal is a linear function of past values of the score of the conditional distribution. This specification produces a model that is not only easy to implement but which also facilitates the development of a comprehensive and relatively straightforward theory for the asymptotic distribution of the maximum‐likelihood (ML) estimator. Score‐driven models of this kind can also be based on conditional t distributions, but whereas these models carry out what, in the robustness literature, is called a soft form of trimming, the EGB2 distribution leads to a soft form of Winsorizing. An exponential general autoregressive conditional heteroscedastic (EGARCH) model based on the EGB2 distribution is also developed. This model complements the score‐driven EGARCH model with a conditional t distribution. Finally, dynamic location and scale models are combined and applied to data on the UK rate of inflation.
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We describe observation driven time series models for Student-t and EGB2 conditional distributions in which the signal is a linear function of past values of the score of the conditional distribution. These specifications produce models that are easy to implement and deal with outliers by what amounts to a soft form of trimming in the case of t and a soft form of Winsorizing in the case of EGB2. We show how a model with trend and seasonal components can be used as the basis for a seasonal adjustment procedure. The methods are illustrated with US and Spanish data.
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