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Essays on econometrics. Multivariate Markov chains

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Detalhes bibliográficos
Resumo:This Dissertation is about Markov chains and their role in economics and in econometrics theory. Four essays on the Markov chain ap- proach are presented. We start by illustrating the analytical potential of multivariate Markov chains in the field of economic history, in particular with regard to a test of the Schumpeterian hypothesis of creative destruction. Then, we ilustrate the flexibility of Markov chains, and their per- tinence to situations that go beyond their traditional applicability: i) how can a Markov chain play the role of covariates; ii) how can a Markov chain representation be useful to compute expected hitting times. Finally, we present a new methodology for testing and detecting multiple structural breaks in multivariate Markov chains, where the dates at which the structural breaks occur are unknown.
Autores principais:Damásio, Bruno
Assunto:Cadeias de Markov Econometria Matemática aplicada à economia
Ano:2018
País:Portugal
Tipo de documento:tese de doutoramento
Tipo de acesso:acesso aberto
Instituição associada:Universidade de Lisboa
Idioma:inglês
Origem:Repositório da Universidade de Lisboa
Descrição
Resumo:This Dissertation is about Markov chains and their role in economics and in econometrics theory. Four essays on the Markov chain ap- proach are presented. We start by illustrating the analytical potential of multivariate Markov chains in the field of economic history, in particular with regard to a test of the Schumpeterian hypothesis of creative destruction. Then, we ilustrate the flexibility of Markov chains, and their per- tinence to situations that go beyond their traditional applicability: i) how can a Markov chain play the role of covariates; ii) how can a Markov chain representation be useful to compute expected hitting times. Finally, we present a new methodology for testing and detecting multiple structural breaks in multivariate Markov chains, where the dates at which the structural breaks occur are unknown.