222 research outputs found

    Annex A5 : A model of the stochastic convergence between euro area business cycles.

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    A new non-linear parametric model, the Stochastic Cyclical Convergence Model (SCCM), is used for measuring the convergence of business cycles between euro area countries and the euro area aggregate. The model combines unobserved component models with time-varying parameter models. The convergence between the two cycles is characterised by two time-varying parameters, the phase-shift and a weight, which is related to the phase-adjusted correlation. A Kalman filter-based iterative procedure is used for the model estimation. SCCM models are applied to the GDP of euro area countries, the United Kingdom and of the euro area aggregate over the period 1963:1-2002:4. When the euro was launched, the convergence was already achieved for most of euro area countries, but Finland, Greece and Ireland had still not converged in 2002:4. The British cycle is also divergent with a lead equal to 3 quarters in 2002:4 and a weight equal to 0.6 in 2002:4. UK shocks have asynchronous asymmetric effects and this suggests that it would be delicate for the UK to join the euro area.convergence;synchronisation;business cycles;multivariate unobserved components models;time-varying parameter models;Kalman filter;

    Unity and Plurality of the European Cycle

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    We apply uni- and multivariate unobserved components models to the study of European growth cycles. The multivariate dimension enables to search similar or, more strongly, common components among national GDP series (quarterly data from 1960 to 1999). Three successive ways to exhibit the European cycle satisfactorily converge: the direct decomposition of the aggregate European GDP; the aggregation of the member countries' national cycles; the search for common components between these national cycles. The European aggregate fluctuations reveal two distinct cyclical components, assimilated to the classical Juglar (decennial, related to investment) and Kitchin (triennial, related to inventories) cycles. The European Juglar cycle cannot be reduced to a single common component of the national cycles. It has at least a dimension of "three": it can be understood as the interference of three elementary and independent sequences of stochastic shocks, that correspond to the European geographical division. The euro-zone is not yet an optimal currency area, as the shocks generating the European cycles are not completely symmetrical. Studying the sequences of innovations extracted from the models shows that euro-zone vulnerability to strong shocks and asymmetry of these shocks tend to decrease during the last decades, but this trend is neither regular, nor irreversible.(A)symmetrical shocks, Common factors, European integration, Growth cycles, Stochastic trends, Structural time series model.

    ChÎmage : débattre de la mesure.

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    Cette publication n'a pas de résuméchomage;emploi;unemployment;

    Écart de production dans la zone euro:Une estimation par le filtre de Hodrick-Prescott multivariĂ©

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    Cet article applique le filtre de Hodrick-Prescott multivariĂ© (HPMV) Ă  l’estimation de l’écart de production de la zone euro. Le filtre HPMV est reformulĂ© comme un modĂšle espace-Ă©tat, qui peut ensuite s’estimer avec le filtre de Kalman et l’algorithme EM. À la diffĂ©rence des autres Ă©tudes empiriques sur le filtre HPMV, cette mĂ©thode d’estimation prĂ©sente l’avantage d’estimer tous les paramĂštres des Ă©quations Ă©conomiques plutĂŽt que de les calibrer, ce qui augmente la prĂ©cision des estimations de l’écart de production. Lorsque sont ajoutĂ©es une loi d’Okun et une Ă©quation de capacitĂ©s, le filtre HPMV modifie sensiblement le diagnostic conjoncturel pour la zone euro par rapport aux Ă©valuations fournies par un filtre HP univariĂ©. Les rĂ©sultats du filtre HPMV prĂ©sentent un certain nombre d’avantages relativement Ă  ceux du filtre HP univariĂ©. L’écart de production connaĂźt des rĂ©visions nettement moins importantes avec un filtre HPMV, ce qui est utile dans un contexte d’analyse conjoncturelle. De plus, la capacitĂ© prĂ©dictive de l’écart de production sur l’inflation est meilleure avec le filtre HPMV

    Introduction aux modĂšles espace-Ă©tat et au filtre de Kalman

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    Nous dĂ©taillons ici les principaux concepts et problĂšmes liĂ©s aux modĂšles espace-Ă©tat, ainsi que leurs applications. Nous prĂ©sentons d’abord ces modĂšles dans leur gĂ©nĂ©ralitĂ©. Ensuite, nous explicitons les algorithmes utilisĂ©s afin de procĂ©der Ă  l’estimation par le maximum de vraisemblance, c’est-Ă -dire fondamentalement le filtre de Kalman et l’algorithme EM. Nous considĂ©rons enfin quatre applications : les dĂ©compositions tendance-cycle, l’extraction d’indicateurs coĂŻncidents d’activitĂ©, l’estimation d’un taux de chĂŽmage d’équilibre pouvant varier avec le temps (TV-Nairu) et l’évaluation du contenu informatif de la courbe des taux sur l’inflation future

    Introduction aux modĂšles espace Ă©tat et au filtre de Kalman.

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    Cette publication n'a pas de résumé

    Real time estimation of potential output and output gap for theeuro-area: comparing production function with unobserved componentsand SVAR approaches

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    We develop a new version of the production function (PF) approach usually used for estimating the output gap of the euro area. Our version does not call for any (often imprecise) measure of the capital stock and improves the estimation of the trend total factor productivity. We asses this approach by comparing it with two other multivariate methods mostly used for output gap estimates, a multivariate unobserved components (MUC) model and a Structural Vector Auto-Regressive (SVAR) model. The comparison is conducted by relying on assessment criteria such as the concordance of the turning points chronology with a reference one, the inflation forecasting power and the real-time consistency of the estimates. Two contributions are achieved. Firstly, we take into account data revisions and their impact on the output gap estimates by using vintage datasets coming from the Euro Area Business Cycle (EABCN) Real-Time Data-Base (RTDB). Secondly, the PF approach, generally employed by policy-makers despite of its difficult implementation, is assessed. We thus improve on previous papers which limited their assessment on other multivariate methods, e.g. MUC or SVAR models. The different methods show different ranks in relation to the three criteria. This new PF estimate appears highly concordant with the reference chronology. Its forecasting power appears favourable only for the shortest horizon (1 month). Finally, the SVAR model appears more consistent in real-time.potential output, production function, state-space models, structural VARs
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