47 research outputs found

    Bayesian nonparametric sparse VAR models

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    High dimensional vector autoregressive (VAR) models require a large number of parameters to be estimated and may suffer of inferential problems. We propose a new Bayesian nonparametric (BNP) Lasso prior (BNP-Lasso) for high-dimensional VAR models that can improve estimation efficiency and prediction accuracy. Our hierarchical prior overcomes overparametrization and overfitting issues by clustering the VAR coefficients into groups and by shrinking the coefficients of each group toward a common location. Clustering and shrinking effects induced by the BNP-Lasso prior are well suited for the extraction of causal networks from time series, since they account for some stylized facts in real-world networks, which are sparsity, communities structures and heterogeneity in the edges intensity. In order to fully capture the richness of the data and to achieve a better understanding of financial and macroeconomic risk, it is therefore crucial that the model used to extract network accounts for these stylized facts.Comment: Forthcoming in "Journal of Econometrics" ---- Revised Version of the paper "Bayesian nonparametric Seemingly Unrelated Regression Models" ---- Supplementary Material available on reques

    Sistemas de alarme ótimos e sua aplicação a séries financeiras

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    Doutoramento em MatemáticaThis thesis focuses on the application of optimal alarm systems to non linear time series models. The most common classes of models in the analysis of real-valued and integer-valued time series are described. The construction of optimal alarm systems is covered and its applications explored. Considering models with conditional heteroscedasticity, particular attention is given to the Fractionally Integrated Asymmetric Power ARCH, FIAPARCH(p; d; q) model and an optimal alarm system is implemented, following both classical and Bayesian methodologies. Taking into consideration the particular characteristics of the APARCH(p; q) representation for financial time series, the introduction of a possible counterpart for modelling time series of counts is proposed: the INteger-valued Asymmetric Power ARCH, INAPARCH(p; q). The probabilistic properties of the INAPARCH(1; 1) model are comprehensively studied, the conditional maximum likelihood (ML) estimation method is applied and the asymptotic properties of the conditional ML estimator are obtained. The final part of the work consists on the implementation of an optimal alarm system to the INAPARCH(1; 1) model. An application is presented to real data series.Esta tese centra-se na aplicação de sistemas de alarme ótimos a modelos de séries temporais não lineares. As classes de modelos mais comuns na análise de séries temporais de valores reais e de valores inteiros são descritas com alguma profundidade. É abordada a construção de sistemas de alarme ótimos e as suas aplicações são exploradas. De entre os modelos com heterocedasticidade condicional é dada especial atenção ao modelo ARCH Fraccionalmente Integrável de Potência Assimétrica, FIAPARCH(p; d; q), e é feita a implementação de um sistema de alarme ótimo, considerando ambas as metodologias clássica e Bayesiana. Tomando em consideração as características particulares do modelo APARCH(p; q) na aplicação a séries de dados financeiros, é proposta a introdução do seu homólogo para a modelação de séries temporais de contagens: o modelo ARCH de valores INteiros e Potência Assimétrica, INAPARCH(p; q). As propriedades probabilísticas do modelo INAPARCH(1; 1) são extensivamente estudadas, é aplicado o método da máxima verosimilhança (MV) condicional para a estimação dos parâmetros do modelo e estudadas as propriedades assintóticas do estimador de MV condicional. Na parte final do trabalho é feita a implementação de um sistema de alarme ótimo ao modelo INAPARCH(1; 1) e apresenta-se uma aplicação a séries de dados reais

    The Marketing-Entrepreneurship Interface: A Contextual and Practical Critique of the Role of Entrepreneurship

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    In the late nineteen eighties, Hills proposed that marketing scholars should pay far more attention to entrepreneurship and the smaller enterprise. He founded an annual research symposium and associated proceedings published under the title of Research at the Marketing/Entrepreneurship Interface. The symposia and proceedings still flourish and both the Academy of Marketing in the UK and the American Marketing Association have special interest groups for this area. This thesis is concerned with the contribution that entrepreneurship can make to understanding this interface. Without a robust definition of entrepreneurship, the interface simply becomes a study of a very common and disparate organisational form - Small to Medium Sized Enterprises (SMEs). There is no shame in this for they deserve our interest, support and help. Without an understanding of the entrepreneurship component of the interface that help and support might be less effective than we, and they, would desire. Small business is not a little large business, they operate in very different circumstances with very much fewer resources to hand, and, because of who they are may have very different motivations and skill sets. Not necessarily worse but different. So entrepreneurial marketing might offer different insights, and help, compared to a standard academic approach to small business. This is a PhD by published work and twenty-three submissions are organised into four themes and form a core for discussion. The first theme considers appropriate definitions of entrepreneurship and the role they play in conceptualising the interface. The second theme considers how adopting an entrepreneurial marketing approach could guide and inform the SME in two particular respects: addressing critical situations and developing and maintaining appropriate relationships. This theme is considering entrepreneurial marketing within the SME. The third theme considers firstly entrepreneurial marketing extended away from the SME to larger organisations in both public and private ownership and to a particular form of public art where participants can be small or large and in either public or private ownership. Secondly the experience of organisations within a cluster and SMEs within a conflict zone are considered. The distinguishing focus of this third theme is that it extends the interface away from the traditional focus on SMEs. Whilst it was natural for the interface to arise out of a desire to understand a neglected organisational form in marketing – it can be applied in other contexts. The final theme considers how the author’s conceptualisation of the interface has informed their teaching and the implications for practical business support. A fundamental argument that is made in respect of understanding the role of entrepreneurship within entrepreneurial marketing is that we should not treat entrepreneurship as an absolute attribute which would direct us into classifying people simply into entrepreneurs as opposed to non-entrepreneurs. Entrepreneurs range from the exceptional ‘stellar’ entrepreneur to those who are imitative of current market offerings and we should work across this range appropriately. Having discussed both an appropriate definition and role for entrepreneurship within the marketingentrepreneurship interface the implications of such a view are illustrated through considering the different contexts discussed in themes two and three above and reflecting upon the delivery of teaching programmes based partly or wholly on the notion of the marketing-entrepreneurship interface. The work is a critique of the role of entrepreneurship within the interface. The contexts selected and discussed draw out practical lessons for a wide range of individuals from undergraduates through SMEs to larger organisations in either private or public ownership

    A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

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    When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

    A Statistical Approach to the Alignment of fMRI Data

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    Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods
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