On the estimation of non linear functions in stochastic volatility models

Abstract

This paper focuses on the inference of suitable generally non linear functions in stochastic volatility models. In this context, in order to estimate the variance of the proposed estimators, a moving block bootstrap (MBB) approach is suggested and discussed. Under mild assumptions, we show that the MBB procedure is weakly consistent. Moreover, a methodology to choose the optimal length block in the MBB is proposed. Some examples and simulations on the model are also made to show the performance of the proposed procedure.     

A comment on “An analysis of global warming in the Alpine Region based on nonlinear nonstationary time series models” by F. Battaglia and M. K. Protopapas

We discuss the paper by Battaglia and Protopapas concerning the analysis of global warming phenomenon in the Alpine Region. The Authors consider a nonlinear model which takes into account regimes in time and levels. In this contribution some of the statistical results presented in the paper are commented and a different approach to the problem is proposed. It is based on a temporal aggregation analysis and it can help to highlight some features in the data.     

Re-bounding EU citizenship from below: practices of healthcare for ‘(Il)legitimate EU Migrants’ in Italy

Since multiple crises are currently affecting Europe, interest on changes in intra-EU mobility patterns, policies and EU movers’ strategies of integration has re-emerged in academic debates. What seems to still lack to date is a focus on the chaining actors linking the macro level of policies and the micro level of individual strategies, that is, civil servants who are in charge of implementing national policies in their daily encounters with EU citizens. Through an in-depth qualitative analysis of formal policies and daily practices in the field of healthcare for EU citizens in Piedmont, in the North of Italy, this contribution analyses how EU citizens’ right to free movement and equal access to social protection is officially framed and concretely enacted within the boundaries of the Italian National Healthcare System and the role of health workers as de facto citizenship-makers. It suggests that, along with managerial orientations, different evaluations of the Italian economic and financial situation, and of EU citizens’ root motivations behind their decisions to move across Europe play a crucial role in shaping health workers’ assessments of EU citizens’ deservingness of healthcare.     

Migrant Health Policies. Actors and Levels in a Multi‐Level Perspective

Until recently, migrant health policies have been overlooked as a topic of policy analysis. Although interest in the issue has developed in parallel with the progressive acknowledgement of the presence of unhealthy migrants and the transformation of welfare states and policy dynamics in Europe, studies on migrant health policies have often focused on the state as a unique unit of observation while hindering the role played by other institutional and non‐institutional actors taking part in the migrant health multi‐level governance. This contribution will bridge this gap, deconstructing the various actors and levels involved in migrant health policymaking from a multi‐level perspective. By critically reviewing migrant health policy research and discussing it with the new MIPEX Health Strand, this contribution suggests a more encompassing perspective in the analysis of migrant health policies and processes, looking at the different players at stake in this multifaceted policy field.     

A DEA Approach to Measure the Quality-of-Life in the Municipalities of the Canary Islands

The notion of the quality of life has always intrigued economists, sociologists and other researchers in the area of social science. Since the genesis of the definition of Gross Domestic Product (GDP) as a truthful measure of well-being and economic development, other sophisticated methodologies have been proposed in the literature to measure the quality-of-life (QOL) that extend in a multidimensional way this complex concept. Measuring QOL in municipalities consists in finding a set of comparable attributes that can be weighted by some metric in order to construct a synthetic index. Thus, the narrow vision obtained by a single measure as the GDP, in which differences in the QOL cannot be fully analyzed, is overcome. Based upon a refinement of data envelopment analysis (DEA)—the cross-efficiency method, the current paper develops a synthetic QOL index that is based in 19 partial indicators which present the tradeoffs of different dimension for the 87 municipalities of the Canary Islands in Spain. Marginal rates of substitution are calculated to evaluate the tradeoffs on QOL dimensions. A method is also proposed to determine the scores chart of each municipality which can be used as a tool to policy makers in order to establish a program of improving the ranking position of the municipality identifying the critical QOL factors.     

Input Variable Selection in Neural Network Models

One of the most important issues in using neural networks for the analysis of real-world problems is the input variable selection problem. This article connects input variable selection with multiple testing in the neural network regression models. In the proposed procedure, the number and the type of input neurons are selected by means of a testing scheme, based on appropriate measures of relevance of a given input variable to the model. In order to avoid the data snooping problem, family-wise error rate is controlled by using the StepM method proposed by Romano and Wolf (2005 Romano , J. P. , Wolf , M. ( 2005 ). Exact and approximate stepdown methods for multiple hypothesis testing . J. Amer. Statist. Assoc. 100 : 94 – 108 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]). The testing procedure is calibrated by using the subsampling, which is shown to deliver consistent results under weak assumptions on the data generating process and on the structure of the neural network model.     

Estimation of a nonlinear discriminant function from a mixture of two GEV distributions

In this paper, the identifiability of finite mixture of generalized extreme value (GEV) distributions is proved. Next, a procedure for finding maximum likelihood estimates (MLEs) of the parameters of a finite mixture of two generalized extreme value (MGEV) distributions is presented by using classified and unclassified observations. Then, a nonlinear discriminant function for a mixture of two GEV distributions is derived and the performance of the corresponding estimated discriminant function is investigated through a series of simulation experiments. Finally, the methodology is applied to real data.