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dc.contributor.authorAlaminos Aguilera, David
dc.contributor.authorSalas Compás, María Belén
dc.date.accessioned2022-06-23T07:21:36Z
dc.date.available2022-06-23T07:21:36Z
dc.date.created2022-06-23
dc.date.issued2022
dc.identifier.urihttps://hdl.handle.net/10630/24468
dc.description.abstractDynamic Stochastic General Equilibrium (DSGE) and Vector Autoregressive (VAR) models allow for probabilistic estimations to formulate macroeconomic policies and monitor them. One of the objectives of creating these models is to explain and understand financial fluc tuations through a consistent theoretical framework. In the tourism sector, stock price and sys temic risk are key financial variables in the international transmission of business cycles. Ad vances in Bayesian theory are providing an increasing range of tools that researchers can employ to estimate and evaluate DSGE and VAR models. One area of interest in previous literature has been to design a Bayesian robust filter, that performs well concerning an uncertainty class of possible models compatible with prior knowledge. In this study, we propose to apply the Bayes ian Kalman Filter with Prior Update (BKPU) in a tourism field to increase the robustness of DSGE and VAR models built for small samples and with irregular data. Our results indicate that BKPU improves the estimation of these models in two aspects. Firstly, the accuracy levels of the computing of the Markov Chain Monte Carlo model are increased, and secondly, the cost of the resources used is reduced due to the need for a shorter run time. Our model can play an essential role in the monetary policy process, as central bankers could use it to investigate the relative importance of different macroeconomic shocks and the effects of tourism stock prices and achieve a country´s international competitiveness and trade balance for this sectores_ES
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.es_ES
dc.language.isoenges_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectMacroeconomíaes_ES
dc.subjectTurismoes_ES
dc.subjectModelos macroeconométricoses_ES
dc.subjectEstadística bayesianaes_ES
dc.subjectTurismo - Precioses_ES
dc.subjectInvestigación científica - Finanzases_ES
dc.subject.otherDynamic Stochastic General Equilibriumes_ES
dc.subject.otherBayesian Kalman Filter Prior Updatees_ES
dc.subject.otherMarkov Chain Monte Carloes_ES
dc.subject.otherTourism Stock Prices,es_ES
dc.subject.otherSystemic Risk,es_ES
dc.subject.otherVolatilityes_ES
dc.titleTourism stock prices, systemic risk and tourism growth: a kalman filter with prior update DSGE-VAR modeles_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.centroFacultad de Ciencias Económicas y Empresarialeses_ES
dc.relation.eventtitleICAISC 2022 The 21st International Conference on Artificial Intelligence and Soft Computinges_ES
dc.relation.eventplaceZakopane, Poloniaes_ES
dc.relation.eventdate19 junio 2022es_ES


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