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.

Essays on numerically efficient inference in nonlinear and non-Gaussian state space models, and commodity market analysis.

The first two articles build procedures to simulate vector of univariate states and estimate parameters in nonlinear and non Gaussian state space models. We propose state space speci fications that offer more flexibility in modeling dynamic relationship with latent variables. Our procedures are extension of the HESSIAN method of McCausland[2012]. Thus, they use approximation of the posterior density of the vector of states that allow to : simulate directly from the state vector posterior distribution, to simulate the states vector in one bloc and jointly with the vector of parameters, and to not allow data augmentation. These properties allow to build posterior simulators with very high relative numerical efficiency. Generic, they open a new path in nonlinear and non Gaussian state space analysis with limited contribution of the modeler. The third article is an essay in commodity market analysis. Private firms coexist with farmers' cooperatives in commodity markets in subsaharan african countries. The private firms have the biggest market share while some theoretical models predict they disappearance once confronted to farmers cooperatives. Elsewhere, some empirical studies and observations link cooperative incidence in a region with interpersonal trust, and thus to farmers trust toward cooperatives. We propose a model that sustain these empirical facts. A model where the cooperative reputation is a leading factor determining the market equilibrium of a price competition between a cooperative and a private firm

Markovian Processes, Two-Sided Autoregressions and Finite-Sample Inference for Stationary and Nonstationary Autoregressive Processes.

In this paper, we develop finite-sample inference procedures for stationary and nonstationary autoregressive (AR) models. The method is based on special properties of Markov processes and a split-sample technique. The results on Markovian processes (intercalary independence and truncation) only require the existence of conditional densities. They are proved for possibly nonstationary and/or non-Gaussian multivariate Markov processes. In the context of a linear regression model with AR(1) errors, we show how these results can be used to simplify the distributional properties of the model by conditioning a subset of the data on the remaining observations. This transformation leads to a new model which has the form of a two-sided autoregression to which standard classical linear regression inference techniques can be applied. We show how to derive tests and confidence sets for the mean and/or autoregressive parameters of the model. We also develop a test on the order of an autoregression. We show that a combination of subsample-based inferences can improve the performance of the procedure. An application to U.S. domestic investment data illustrates the method.

Simulation-Based Finite-Sample Tests for Heteroskedasticity and ARCH Effects.

A wide range of tests for heteroskedasticity have been proposed in the econometric and statistics literature. Although a few exact homoskedasticity tests are available, the commonly employed procedures are quite generally based on asymptotic approximations which may not provide good size control in finite samples. There has been a number of recent studies that seek to improve the reliability of common heteroskedasticity tests using Edgeworth, Bartlett, jackknife and bootstrap methods. Yet the latter remain approximate. In this paper, we describe a solution to the problem of controlling the size of homoskedasticity tests in linear regression contexts. We study procedures based on the standard test statistics [e.g., the Goldfeld-Quandt, Glejser, Bartlett, Cochran, Hartley, Breusch-Pagan-Godfrey, White and Szroeter criteria] as well as tests for autoregressive conditional heteroskedasticity (ARCH-type models). We also suggest several extensions of the existing procedures (sup-type of combined test statistics) to allow for unknown breakpoints in the error variance. We exploit the technique of Monte Carlo tests to obtain provably exact p-values, for both the standard and the new tests suggested. We show that the MC test procedure conveniently solves the intractable null distribution problem, in particular those raised by the sup-type and combined test statistics as well as (when relevant) unidentified nuisance parameter problems under the null hypothesis. The method proposed works in exactly the same way with both Gaussian and non-Gaussian disturbance distributions [such as heavy-tailed or stable distributions]. The performance of the procedures is examined by simulation. The Monte Carlo experiments conducted focus on : (1) ARCH, GARCH, and ARCH-in-mean alternatives; (2) the case where the variance increases monotonically with : (i) one exogenous variable, and (ii) the mean of the dependent variable; (3) grouped heteroskedasticity; (4) breaks in variance at unknown points. We find that the proposed tests achieve perfect size control and have good power.

Tests of Joint Hypotheses for Time Series Regression with a Unit Root

This Paper Studies Tests of Joint Hypotheses in Time Series Regression with a Unit Root in Which Weakly Dependent and Heterogeneously Distributed Innovations Are Allowed. We Consider Two Types of Regression: One with a Constant and Lagged Dependent Variable, and the Other with a Trend Added. the Statistics Studied Are the Regression \"F-Test\" Originally Analysed by Dickey and Fuller (1981) in a Less General Framework. the Limiting Distributions Are Found Using Functinal Central Limit Theory. New Test Statistics Are Proposed Which Require Only Already Tabulated Critical Values But Which Are Valid in a Quite General Framework (Including Finite Order Arma Models Generated by Gaussian Errors). This Study Extends the Results on Single Coefficients Derived in Phillips (1986A) and Phillips and Perron (1986).

Finite-Sample Inference Methods for Simultaneous Equations and Models with Unobserved and Generated Regressors

We propose finite sample tests and confidence sets for models with unobserved and generated regressors as well as various models estimated by instrumental variables methods. The validity of the procedures is unaffected by the presence of identification problems or \"weak instruments\", so no detection of such problems is required. We study two distinct approaches for various models considered by Pagan (1984). The first one is an instrument substitution method which generalizes an approach proposed by Anderson and Rubin (1949) and Fuller (1987) for different (although related) problems, while the second one is based on splitting the sample. The instrument substitution method uses the instruments directly, instead of generated regressors, in order to test hypotheses about the \"structural parameters\" of interest and build confidence sets. The second approach relies on \"generated regressors\", which allows a gain in degrees of freedom, and a sample split technique. For inference about general possibly nonlinear transformations of model parameters, projection techniques are proposed. A distributional theory is obtained under the assumptions of Gaussian errors and strictly exogenous regressors. We show that the various tests and confidence sets proposed are (locally) \"asymptotically valid\" under much weaker assumptions. The properties of the tests proposed are examined in simulation experiments. In general, they outperform the usual asymptotic inference methods in terms of both reliability and power. Finally, the techniques suggested are applied to a model of Tobin’s q and to a model of academic performance.

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 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.

Méthodes d’inférence exactes pour un modèle de régression avec erreurs AR(2) gaussiennes

Ce texte propose des méthodes d’inférence exactes (tests et régions de confiance) sur des modèles de régression linéaires avec erreurs autocorrélées suivant un processus autorégressif d’ordre deux [AR(2)], qui peut être non stationnaire. L’approche proposée est une généralisation de celle décrite dans Dufour (1990) pour un modèle de régression avec erreurs AR(1) et comporte trois étapes. Premièrement, on construit une région de confiance exacte pour le vecteur des coefficients du processus autorégressif (φ). Cette région est obtenue par inversion de tests d’indépendance des erreurs sur une forme transformée du modèle contre des alternatives de dépendance aux délais un et deux. Deuxièmement, en exploitant la dualité entre tests et régions de confiance (inversion de tests), on détermine une région de confiance conjointe pour le vecteur φ et un vecteur d’intérêt M de combinaisons linéaires des coefficients de régression du modèle. Troisièmement, par une méthode de projection, on obtient des intervalles de confiance «marginaux» ainsi que des tests à bornes exacts pour les composantes de M. Ces méthodes sont appliquées à des modèles du stock de monnaie (M2) et du niveau des prix (indice implicite du PNB) américains

Short run and long run causality in time series: Inference

We propose methods for testing hypotheses of non-causality at various horizons, as defined in Dufour and Renault (1998, Econometrica). We study in detail the case of VAR models and we propose linear methods based on running vector autoregressions at different horizons. While the hypotheses considered are nonlinear, the proposed methods only require linear regression techniques as well as standard Gaussian asymptotic distributional theory. Bootstrap procedures are also considered. For the case of integrated processes, we propose extended regression methods that avoid nonstandard asymptotics. The methods are applied to a VAR model of the U.S. economy.

Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression

Affiliation: Institut de recherche en immunologie et en cancérologie, Université de Montréal

Regards des praticiens en design industriel sur les dimensions identitaires de la pratique du design au Québec

Cette recherche pose un regard sur l’articulation des changements sociétaux émergeant de la négociation entre les mondes globaux et locaux et leurs impacts sur la sphère professionnelle du design industriel au Québec. Nous proposons de mettre en lumière les dimensions identitaires qui caractérisent la pratique du design industriel. Nous référons aux théories de l’identité, aux théories de la globalisation et au contexte particulier de la pratique du design québécois à travers ses aspects socioculturel, politique et économique. Le concept d’identité nous permet d’explorer l’interprétation des designers de leur pratique professionnelle dans un contexte désormais glocal (Robertson, 1995). Suivant une démarche qualitative basée sur les entretiens en profondeur, nous explorons l’interprétation du caractère identitaire de l’activité professionnelle auprès de trois générations de designers. Nous examinons également le sens qu’ils prêtent au concept de communauté du design, à leur système de valeurs et à l’avenir du design industriel québécois.

Growing up expectations. Better understanding the expectations of community partners in participatory action research projects

This paper challenges the assumption that youth and youth agencies are in a condition of equality when entering a participatory action research (PAR). By asserting that it is not a state of equality that practitioners nor youth should assume nor be immediately striving for, but a consistently equitable process, this article draws from and reflects on the relationship between young people and researchers who have used a PAR methodology in action oriented projects. Using the UNESCO Growing up in Cities Canada project as a case example, this review extrapolates from and reflects on challenges faced by the project as a whole. Using semi-structured interviews to explore the roles of adults and youth, a number of strategies are highlighted as the techniques used to overcome these challenges. The discussion concludes with further reflection on the complexities of equality and equity, recommending a number of actions that have the potential to create an equitable environment in PAR projects similar to the one examined.

Recherche qualitative des enjeux de la mise en lumière urbaine : création d’un modèle opératoire pour la conception des projets d’éclairage

Cette étude traite de la complexité des enjeux de la mise en lumière urbaine et de sa conception. Le but est de déceler les mécanismes opératoires du projet d’éclairage afin de générer une analyse et une compréhension de ce type d’aménagement. Cette recherche met à jour les enjeux lumineux à différents niveaux comme l’urbanisme, l’environnement, la culture, la communication, la vision et la perception mais aussi au niveau des acteurs et de leurs pratiques sur le terrain. En utilisant une approche qualitative déductive, cette recherche théorique cherche à mieux comprendre les différentes significations du phénomène lumineux : comment dans la réalité terrain ces enjeux de la lumière sont compris, interprétés et traduits au travers de la réalisation des projets et des processus mis en place pour répondre aux besoins d’éclairage ? La pertinence de cette recherche est de questionner les enjeux complexes de la mise en lumière afin de savoir comment concevoir un « bon éclairage ». Comment se déroule un projet d’éclairage de sa conception à sa réalisation ? Quels sont les différents acteurs, leurs modes d’intervention et leurs perceptions du projet d’éclairage ? Le but est de vérifier comment ces enjeux se concrétisent sur le terrain, notamment au travers de l’activité et de l’interprétation des professionnels. Nous souhaitons créer un modèle opératoire qui rende compte des enjeux et du processus de ce type de projet. Modèle qui servira alors de repère pour la compréhension des mécanismes à l’œuvre comme le contexte, les acteurs, les moyens et les finalités des projets. Une étude des recherches théoriques nous permettra de comprendre la polysémie du phénomène lumineux afin d’en déceler la complexité des enjeux et de créer une première interprétation de ce type de projet. Nous déterminerons théoriquement ce que recouvre la notion de « bon éclairage » qui nous permettra de créer une grille analytique pour comparer notre approche avec la réalité sur le terrain. Ces recherches seront ensuite confrontées au recueil des données des études de cas, des stages en urbanisme et en conception lumière, et des interviews de professionnels dans le domaine. Nous confronterons les enjeux définis théoriquement aux collectes de données issues du terrain. Ces données seront collectées à partir de projets réalisés avec les professionnels durant la recherche immersive. La recherche-action nous permettra de collaborer avec les professionnels pour comprendre comment ils sélectionnent, déterminent et répondent aux enjeux des projets d’éclairage. Nous verrons grâce aux entretiens semi-dirigés comment les acteurs perçoivent leurs propres activités et nous interprèterons les données à l’aide de la « théorisation ancrée » pour dégager le sens de leurs discours. Nous analyserons alors les résultats de ces données de manière interprétative afin de déterminer les points convergeant et divergent entre les enjeux théoriques définis en amont et les enjeux définis en aval par la recherche-terrain. Cette comparaison nous permettra de créer une interprétation des enjeux de la mise en lumière urbaine dans toutes leurs complexités, à la fois du point de vue théorique et pratique. Cette recherche qualitative et complexe s’appuie sur une combinaison entre une étude phénoménologique et les méthodologies proposées par la « théorisation ancrée ». Nous procéderons à une combinaison de données issues de la pratique terrain et de la perception de cette pratique par les acteurs de l’éclairage. La recherche d’un « bon éclairage » envisage donc par une nouvelle compréhension l’amélioration des outils de réflexion et des actions des professionnels. En termes de résultat nous souhaitons créer un modèle opératoire de la mise en lumière qui définirait quels sont les différents éléments constitutifs de ces projets, leurs rôles et les relations qu’ils entretiennent entre eux. Modèle qui mettra en relief les éléments qui déterminent la qualité du projet d’éclairage et qui permettra de fournir un outil de compréhension. La contribution de ce travail de recherche est alors de fournir par cette nouvelle compréhension un repère méthodologique et analytique aux professionnels de l’éclairage mais aussi de faire émerger l’importance du phénomène de mise en lumière en suscitant de nouveaux questionnements auprès des activités liées au design industriel, à l’architecture et à l’urbanisme.