Publication Statistics Maps 1991-

Vapaakappalekartuntaan perustuva tilasto Suomessa kustannetuista karttajulkaisuista vuodesta 1991 lähtien

Publication Statistics Sets of slides, transparents and microfiche 1991-

Vapaakappalekartuntaan perustuva tilasto Suomessa julkaistuista dia-, kalvo- ja filmikorttisarjoista vuodesta 1991 lähtien

Adolescent sexuality : an investigation into the relation between sexuality and self-concept /

The present study explored the connections among adolescents' sense of self, sexuality, and perceptions of risk. Such an exploration may help educators to further understand why adolescents engage in risk-taking behaviours such as unprotected sex. The study involved secondary analysis on the data collected from the Youth Lifestyle Choices - Community University Research Alliance 2000 (YLC - CURA) Youth Resilience Questionnaire (YRQ). Participants were 300 male and female students in Grades 9, 1 1 and OAC. Data analyses involved both descriptive and inferential statistics (correlational and multivariate analysis). Chi-square analyses were performed on the open-ended self-description question. Separate analyses were conducted on gender and age (grade levels). Correlational analyses revealed that adolescents with a more positive sense of self were more likely to perceive sexual involvement as a relatively high-risk behaviour. Specifically, results found that male adolescents were less likely than females to perceive sex to be risky. Results are discussed in relation to previous research in the area of selfcognitions and risk-taking sexual behaviour. Results are also discussed in terms of educational implications in that the current results may provide the beginnings of a framework for more holistic sexual education programs.

Statistics of Manufacturers for the year 1905 - Canadian Niagara Power Company

A census form for the year 1905. The form was approved by the Governor General in Council January 22, 1906.

Exact Tests for Contemporaneous Correlation of Disturbances in Seemingly Unrelated Regressions.

This paper proposes finite-sample procedures for testing the SURE specification in multi-equation regression models, i.e. whether the disturbances in different equations are contemporaneously uncorrelated or not. We apply the technique of Monte Carlo (MC) tests [Dwass (1957), Barnard (1963)] to obtain exact tests based on standard LR and LM zero correlation tests. We also suggest a MC quasi-LR (QLR) test based on feasible generalized least squares (FGLS). We show that the latter statistics are pivotal under the null, which provides the justification for applying MC tests. Furthermore, we extend the exact independence test proposed by Harvey and Phillips (1982) to the multi-equation framework. Specifically, we introduce several induced tests based on a set of simultaneous Harvey/Phillips-type tests and suggest a simulation-based solution to the associated combination problem. The properties of the proposed tests are studied in a Monte Carlo experiment which shows that standard asymptotic tests exhibit important size distortions, while MC tests achieve complete size control and display good power. Moreover, MC-QLR tests performed best in terms of power, a result of interest from the point of view of simulation-based tests. The power of the MC induced tests improves appreciably in comparison to standard Bonferroni tests and, in certain cases, outperforms the likelihood-based MC tests. The tests are applied to data used by Fischer (1993) to analyze the macroeconomic determinants of growth.

Simulation-Based Finite-and Large-sample Inference Methods in Multivariate Regressions and Seemingly Unrelated Regressions

In the context of multivariate regression (MLR) and seemingly unrelated regressions (SURE) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. in this paper, we propose finite-and large-sample likelihood-based test procedures for possibly non-linear hypotheses on the coefficients of MLR and SURE systems.

Finite-Sample Diagnostics for Multivariate Regressions with Applications to Linear Asset Pricing Models

In this paper, we propose several finite-sample specification tests for multivariate linear regressions (MLR) with applications to asset pricing models. We focus on departures from the assumption of i.i.d. errors assumption, at univariate and multivariate levels, with Gaussian and non-Gaussian (including Student t) errors. The univariate tests studied extend existing exact procedures by allowing for unspecified parameters in the error distributions (e.g., the degrees of freedom in the case of the Student t distribution). The multivariate tests are based on properly standardized multivariate residuals to ensure invariance to MLR coefficients and error covariances. We consider tests for serial correlation, tests for multivariate GARCH and sign-type tests against general dependencies and asymmetries. The procedures proposed provide exact versions of those applied in Shanken (1990) which consist in combining univariate specification tests. Specifically, we combine tests across equations using the MC test procedure to avoid Bonferroni-type bounds. Since non-Gaussian based tests are not pivotal, we apply the “maximized MC” (MMC) test method [Dufour (2002)], where the MC p-value for the tested hypothesis (which depends on nuisance parameters) is maximized (with respect to these nuisance parameters) to control the test’s significance level. The tests proposed are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995. Our empirical results reveal the following. Whereas univariate exact tests indicate significant serial correlation, asymmetries and GARCH in some equations, such effects are much less prevalent once error cross-equation covariances are accounted for. In addition, significant departures from the i.i.d. hypothesis are less evident once we allow for non-Gaussian errors.

Exact Skewness-Kurtosis Tests for Multivariate Normality and Goodness-of-fit in Multivariate Regressions with Application to Asset Pricing Models

We study the problem of testing the error distribution in a multivariate linear regression (MLR) model. The tests are functions of appropriately standardized multivariate least squares residuals whose distribution is invariant to the unknown cross-equation error covariance matrix. Empirical multivariate skewness and kurtosis criteria are then compared to simulation-based estimate of their expected value under the hypothesized distribution. Special cases considered include testing multivariate normal, Student t; normal mixtures and stable error models. In the Gaussian case, finite-sample versions of the standard multivariate skewness and kurtosis tests are derived. To do this, we exploit simple, double and multi-stage Monte Carlo test methods. For non-Gaussian distribution families involving nuisance parameters, confidence sets are derived for the the nuisance parameters and the error distribution. The procedures considered are evaluated in a small simulation experi-ment. Finally, the tests are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995.

Exact Multivariate Tests of Asset Pricing Models with Stable Asymmetric Distributions

In this paper, we propose exact inference procedures for asset pricing models that can be formulated in the framework of a multivariate linear regression (CAPM), allowing for stable error distributions. The normality assumption on the distribution of stock returns is usually rejected in empirical studies, due to excess kurtosis and asymmetry. To model such data, we propose a comprehensive statistical approach which allows for alternative - possibly asymmetric - heavy tailed distributions without the use of large-sample approximations. The methods suggested are based on Monte Carlo test techniques. Goodness-of-fit tests are formally incorporated to ensure that the error distributions considered are empirically sustainable, from which exact confidence sets for the unknown tail area and asymmetry parameters of the stable error distribution are derived. Tests for the efficiency of the market portfolio (zero intercepts) which explicitly allow for the presence of (unknown) nuisance parameter in the stable error distribution are derived. The methods proposed are applied to monthly returns on 12 portfolios of the New York Stock Exchange over the period 1926-1995 (5 year subperiods). We find that stable possibly skewed distributions provide statistically significant improvement in goodness-of-fit and lead to fewer rejections of the efficiency hypothesis.

Analyse des facteurs institutionnels associés à la mortalité maternelle : Une étude nationale dans les maternités chirurgicales au Sénégal

Résumé Objectif : Identifier les facteurs institutionnels qui influencent la mortalité maternelle (MM) hospitalière dans les maternités chirurgicales au Sénégal. Méthode : cette étude est une analyse secondaire des données de la troisième Enquête Nationale sur la Couverture Obstétrico-chirurgicale au Sénégal en 2001. Les données analysées, issues des fiches d'activité des maternités, comptaient pour 38,239 admissions en obstétrique dans 19 hôpitaux et 450 décès maternels. Les taux de mortalité maternelle hospitalière (TMMH) brut et ajusté ont été utilisés comme variables dépendantes. Le TMMH ajusté sur les caractéristiques de la clientèle ('cases-mix') a été estimé pour chaque établissement de santé par la méthode de standardisation directe. Les indicateurs de la qualité des structures, de la gestion des ressources, et un score de qualité ont été utilisés comme variables indépendantes pour prédire la MM hospitalière. Les tests de Mann-Whitney et de Kruskal-Wallis ont été utilisés pour analyser l’association entre les variables indépendantes, le score de qualité et la MM. Une analyse multivariée a été utilisée pour estimer l’impact du score de qualité sur la MM, en tenant compte de la situation géographique (Dakar versus autre région).Résultats: En analyse bivariée, la présence d'anesthésiste, la disponibilité de boîtes de césarienne complète et la supervision de tous les accouchements par du personnel qualifié sont les facteurs institutionnels associés significativement à une réduction du TMMH brut. Quant au TMMH ajusté ce sont la présence de scialytique, la disponibilité du sulfate de magnésium, l'utilisation des guides de pratiques cliniques (GPC) pour la prise en charge des complications obstétricales. Le score de qualité est associé significativement au TMMH brut, y compris en analyse multivariée, mais pas au TMMH ajusté. Conclusion : La disponibilité du Sulfate de magnésium, et du scialytique pourrait contribuer à la réduction de la MM. En complément, une réorganisation adéquate des ressources pour réduire la disparité géographique rurale/urbaine est essentielle ainsi qu’une sensibilisation du personnel à l’usage des GPC. De plus, l’assistance par un personnel qualifié de tous les accouchements est nécessaire pour améliorer la qualité des soins et la prise en charge des complications obstétricales.