Testing Normality : A GMM Approach

In this paper, we consider testing marginal normal distributional assumptions. More precisely, we propose tests based on moment conditions implied by normality. These moment conditions are known as the Stein (1972) equations. They coincide with the first class of moment conditions derived by Hansen and Scheinkman (1995) when the random variable of interest is a scalar diffusion. Among other examples, Stein equation implies that the mean of Hermite polynomials is zero. The GMM approach we adopted is well suited for two reasons. It allows us to study in detail the parameter uncertainty problem, i.e., when the tests depend on unknown parameters that have to be estimated. In particular, we characterize the moment conditions that are robust against parameter uncertainty and show that Hermite polynomials are special examples. This is the main contribution of the paper. The second reason for using GMM is that our tests are also valid for time series. In this case, we adopt a Heteroskedastic-Autocorrelation-Consistent approach to estimate the weighting matrix when the dependence of the data is unspecified. We also make a theoretical comparison of our tests with Jarque and Bera (1980) and OPG regression tests of Davidson and MacKinnon (1993). Finite sample properties of our tests are derived through a comprehensive Monte Carlo study. Finally, three applications to GARCH and realized volatility models are presented.

Testing Mean-Variance Efficiency in CAPM with Possibly Non-Gaussian Errors : An Exact Simulation-Based Approach

In this paper we propose exact likelihood-based mean-variance efficiency tests of the market portfolio in the context of Capital Asset Pricing Model (CAPM), allowing for a wide class of error distributions which include normality as a special case. These tests are developed in the frame-work of multivariate linear regressions (MLR). It is well known however that despite their simple statistical structure, standard asymptotically justified MLR-based tests are unreliable. In financial econometrics, exact tests have been proposed for a few specific hypotheses [Jobson and Korkie (Journal of Financial Economics, 1982), MacKinlay (Journal of Financial Economics, 1987), Gib-bons, Ross and Shanken (Econometrica, 1989), Zhou (Journal of Finance 1993)], most of which depend on normality. For the gaussian model, our tests correspond to Gibbons, Ross and Shanken’s mean-variance efficiency tests. In non-gaussian contexts, we reconsider mean-variance efficiency tests allowing for multivariate Student-t and gaussian mixture errors. Our framework allows to cast more evidence on whether the normality assumption is too restrictive when testing the CAPM. We also propose exact multivariate diagnostic checks (including tests for multivariate GARCH and mul-tivariate generalization of the well known variance ratio tests) and goodness of fit tests as well as a set estimate for the intervening nuisance parameters. Our results [over five-year subperiods] show the following: (i) multivariate normality is rejected in most subperiods, (ii) residual checks reveal no significant departures from the multivariate i.i.d. assumption, and (iii) mean-variance efficiency tests of the market portfolio is not rejected as frequently once it is allowed for the possibility of non-normal errors.

Testing for a Unit Root in Panels with Dynamic Factors

This paper studies testing for a unit root for large n and T panels in which the cross-sectional units are correlated. To model this cross-sectional correlation, we assume that the data is generated by an unknown number of unobservable common factors. We propose unit root tests in this environment and derive their (Gaussian) asymptotic distribution under the null hypothesis of a unit root and local alternatives. We show that these tests have significant asymptotic power when the model has no incidental trends. However, when there are incidental trends in the model and it is necessary to remove heterogeneous deterministic components, we show that these tests have no power against the same local alternatives. Through Monte Carlo simulations, we provide evidence on the finite sample properties of these new tests.

Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing

Statistical tests in vector autoregressive (VAR) models are typically based on large-sample approximations, involving the use of asymptotic distributions or bootstrap techniques. After documenting that such methods can be very misleading even with fairly large samples, especially when the number of lags or the number of equations is not small, we propose a general simulation-based technique that allows one to control completely the level of tests in parametric VAR models. In particular, we show that maximized Monte Carlo tests [Dufour (2002)] can provide provably exact tests for such models, whether they are stationary or integrated. Applications to order selection and causality testing are considered as special cases. The technique developed is applied to quarterly and monthly VAR models of the U.S. economy, comprising income, money, interest rates and prices, over the period 1965-1996.

Whither Pediatric Research and Predisposition Genetic Testing?

Research in which children undergo genetic testing for predisposition to adult-onset diseases or disorders can lead to a better understanding of these conditions. It can possibly also help encourage early detection and the development of clinical and preventive interventions for those found to be at increased hereditary risk. Increasingly, predisposition testing is becoming part of pediatric genetic research. However, the paucity of normative texts about the conduct of pediatric research using predisposition genetic testing generates complex legal and ethical issues. Drawing on the current texts that govern predisposition genetic testing in research and the norms of pediatric research, we outline points of consensus and divergence as well as recommendations regarding predisposition genetic testing in pediatric research.

Providing Genetic Testing Through the Private Sector: A View From Canada

Genetic testing technologies are rapidly moving from the research laboratory to the market place. Very little scholarship considers the implications of private genetic testing for a public health care system such as Canada’s. It is critical to consider how and if these tests should be marketed to, and purchased by, the public. It is also imperative to evaluate the extent to which genetic tests are or should be included in Canada’s public health care system, and the impact of allowing a two-tiered system for genetic testing. A series of threshold tests are presented as ways of clarifying whether a genetic test is morally appropriate, effective and safe, efficient and appropriate for public funding and whether private purchase poses special problems and requires further regulation. These thresholds also identify the research questions around which professional, public and policy debate must be sustained: What is a morally acceptable goal for genetic services? What are the appropriate benefits? What are the risks? When is it acceptable that services are not funded under health care? And how can the harms of private access be managed?

Analysis of the Ethical, Social and Legal Issues Related to the Delivery of the Prenatal Genetic Testing in Argentina

L'introduction de nouvelles biotechnologies dans tout système de soins de santé est un processus complexe qui est étroitement lié aux facteurs économiques, politiques et culturels, et, par conséquent, demande de remettre en cause plusieurs questions sociales et éthiques. Dans la situation particulière de l’Argentine - c’est-à-dire: de grandes inégalités sociales entre les citoyens, la rareté des ressources sanitaires, l’accès limité aux services de base, l’absence de politiques spécifiques - l'introduction de technologies génétiques pose de sérieux défis qui doivent impérativement être abordés par les décideurs politiques. Ce projet examine le cas des tests génétiques prénataux dans le contexte du système de santé argentin pour illustrer comment leur introduction peut être complexe dans une nation où l’accès égale aux services de santé doit encore être amélioré. Il faut également examiner les restrictions légales et les préceptes religieux qui influencent l'utilisation des technologies génétiques, ce qui souligne la nécessite de développer un cadre de référence intégral pour le processus d'évaluation des technologies afin d’appuyer l’élaboration de recommandations pour des politiques cohérentes et novatrices applicables au contexte particulier de l’Argentine.

Ethic of Care versus Ethic of Justice? The Gender- Corruption Nexus: Testing the New Conventional Wisdom

Ce papier utilise des données empiriques sur le Ghana afin d’examiner comment, le genre, en tant que système social, génère des dilemmes moraux dans le secteur public. Les hommes et les femmes se sentent obligées de choisir des conditions privées de moralité dans le secteur de l’éthique publique. Ce papier démontre que les références qui délimitent les personnalités comportementales sexuées et qui sont utilisées pour justifier le plus haut degré de standard éthique des femmes peut aussi être potentiellement source de corruption, si les femmes essaient de respecter les attentes en matière de genre dans la conduite des obligations publiques. Fondamentalement, le papier argumente que l’éthique sexuée- supposant la division entre éthique de la compassion et éthique de la justice- pourrait perpétuer des comportements qui nient l’éthique du secteur public, mais se conforme à l’éthique sociale. En utilisant les travaux de Carol Gilligan (1982) sur la théorie du développement moral, il conclut, inter alia, que le recrutement des femmes dans le secteur public devrait être promu en tant que droit plutôt qu’à partir de leur probité morale présumée supérieure. Promouvoir les femmes dans le services publics sur la base de leur éthique supérieure pourrait s’avérer contre-productif si les espoirs étaient déçus.