4 research outputs found
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Functional time series approach to analyzing asset returns co-movements
We introduce a new approach for modeling the time varying behavior and time series evolution of asset returns co-movements. Here, the co-movement in each period is captured by a trajectory of returns correlation, then a sequence of this over time and the time series evolution are studied. We rely on functional principal components to achieve dimension reduction and to construct the dynamic space of interest, while introducing a new class of information criteria in order to identify the finite dimensionality of the curve time series. Our method is able to combine two of the most applied ideas in the literature, namely economics (or finance) based and time-series based time-varying correlation models. This offers a general specification that is able to model processes of time-varying time-series correlations generated under many existing models that have dominated the financial literature for several decades. To illustrate its empirical relevance, we apply our method to model the time varying co-movement of exchange rate returns for a group of small open economies with large financial sectors. Our empirical results indicate that concepts of time varying correlation enabled by existing methods are too restrictive to accommodate fully the time varying behavior and time series evolution of the returns correlation. On the other hand, our method gives a more complete picture and is able to provide more accurate correlation forecasts
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A New Model for Agricultural Land-Use Modeling and Prediction in England Using Spatially High-Resolution Data
In this chapter, the authors contribute toward building a better understanding of farmers’ responses to behavioral drivers of land-use decision by establishing an alternative analytical procedure, which can overcome various drawbacks suffered by methods currently used in existing studies. Firstly, our procedure makes use of spatially high-resolution data, so that idiosyncratic effects of physical environment drivers, e.g., soil textures, can be explicitly modeled. Secondly, we address the well-known censored data problem, which often hinders a successful analysis of land-use shares. Thirdly, we incorporate spatial error dependence (SED) and heterogeneity in order to obtain efficiency gain and a more accurate formulation of variances for the parameter estimates. Finally, the authors reduce the computational burden and improve estimation accuracy by introducing an alternative generalized method of moments (GMM)–quasi maximum likelihood (QML) hybrid estimation procedure. The authors apply the newly proposed procedure to spatially high-resolution data in England and found that, by taking these features into consideration, the authors are able to formulate conclusions about causal effects of climatic and physical environment, and environmental policy on land-use shares that differ significantly from those made based on methods that are currently used in the literature. Moreover, the authors show that our method enables derivation of a more effective predictor of the land-use shares, which is utterly useful from the policy-making point of view
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Varying coefficient panel data models and methods under correlated error components: Application to disparities in mental health services in England
The contribution of this paper is twofold. Firstly, it introduces novel regression models that combine two important areas of the methodological development in panel data analysis, namely a varying coefficient specification and spatial error dependence. The former allows relatively flexible nonlinear interactions; the latter enables spatial correlations of the disturbance and thus differ significantly from the other random effect models in the literature. To estimate the model, a new estimation procedure is established that can be viewed as a generalization of the quasi-maximum likelihood method for a spatial panel data model to the well-known conditional local likelihood procedure. Novel inference methods, particularly variable selection and hypothesis testing of the parameter constancy, are introduced and are shown to be effective under the complex spatial error dependence. Equally importantly, this paper makes a substantial contribution to the understanding of financing and expenditure for health and social care. In particular, we empirically analyze and explain the effects of political ideologies on the local fiscal policy in England, especially the expenditure on mental health services
Recommended from our members
Varying coefficient panel data models and methods under correlated error components: Application to disparities in mental health services in England
The contribution of this paper is twofold. Firstly, it introduces novel regression models that combine two important areas of the methodological development in panel data analysis, namely a varying coefficient specification and spatial error dependence. The former allows relatively flexible nonlinear interactions; the latter enables spatial correlations of the disturbance and thus differ significantly from the other random effect models in the literature. To estimate the model, a new estimation procedure is established that can be viewed as a generalization of the quasi-maximum likelihood method for a spatial panel data model to the well-known conditional local likelihood procedure. Novel inference methods, particularly variable selection and hypothesis testing of the parameter constancy, are introduced and are shown to be effective under the complex spatial error dependence. Equally importantly, this paper makes a substantial contribution to the understanding of financing and expenditure for health and social care. In particular, we empirically analyze and explain the effects of political ideologies on the local fiscal policy in England, especially the expenditure on mental health services