Latent Variable Models for Stochastic Discount Factors.

Latent variable models in finance originate both from asset pricing theory and time series analysis. These two strands of literature appeal to two different concepts of latent structures, which are both useful to reduce the dimension of a statistical model specified for a multivariate time series of asset prices. In the CAPM or APT beta pricing models, the dimension reduction is cross-sectional in nature, while in time-series state-space models, dimension is reduced longitudinally by assuming conditional independence between consecutive returns, given a small number of state variables. In this paper, we use the concept of Stochastic Discount Factor (SDF) or pricing kernel as a unifying principle to integrate these two concepts of latent variables. Beta pricing relations amount to characterize the factors as a basis of a vectorial space for the SDF. The coefficients of the SDF with respect to the factors are specified as deterministic functions of some state variables which summarize their dynamics. In beta pricing models, it is often said that only the factorial risk is compensated since the remaining idiosyncratic risk is diversifiable. Implicitly, this argument can be interpreted as a conditional cross-sectional factor structure, that is, a conditional independence between contemporaneous returns of a large number of assets, given a small number of factors, like in standard Factor Analysis. We provide this unifying analysis in the context of conditional equilibrium beta pricing as well as asset pricing with stochastic volatility, stochastic interest rates and other state variables. We address the general issue of econometric specifications of dynamic asset pricing models, which cover the modern literature on conditionally heteroskedastic factor models as well as equilibrium-based asset pricing models with an intertemporal specification of preferences and market fundamentals. We interpret various instantaneous causality relationships between state variables and market fundamentals as leverage effects and discuss their central role relative to the validity of standard CAPM-like stock pricing and preference-free option pricing.

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-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.

Logique et tests d'hypotheses: reflexions sur les problemes mal poses en econometrie

Dans ce texte, nous analysons les développements récents de l’économétrie à la lumière de la théorie des tests statistiques. Nous revoyons d’abord quelques principes fondamentaux de philosophie des sciences et de théorie statistique, en mettant l’accent sur la parcimonie et la falsifiabilité comme critères d’évaluation des modèles, sur le rôle de la théorie des tests comme formalisation du principe de falsification de modèles probabilistes, ainsi que sur la justification logique des notions de base de la théorie des tests (tel le niveau d’un test). Nous montrons ensuite que certaines des méthodes statistiques et économétriques les plus utilisées sont fondamentalement inappropriées pour les problèmes et modèles considérés, tandis que de nombreuses hypothèses, pour lesquelles des procédures de test sont communément proposées, ne sont en fait pas du tout testables. De telles situations conduisent à des problèmes statistiques mal posés. Nous analysons quelques cas particuliers de tels problèmes : (1) la construction d’intervalles de confiance dans le cadre de modèles structurels qui posent des problèmes d’identification; (2) la construction de tests pour des hypothèses non paramétriques, incluant la construction de procédures robustes à l’hétéroscédasticité, à la non-normalité ou à la spécification dynamique. Nous indiquons que ces difficultés proviennent souvent de l’ambition d’affaiblir les conditions de régularité nécessaires à toute analyse statistique ainsi que d’une utilisation inappropriée de résultats de théorie distributionnelle asymptotique. Enfin, nous soulignons l’importance de formuler des hypothèses et modèles testables, et de proposer des techniques économétriques dont les propriétés sont démontrables dans les échantillons finis.

Wage Policy of Firms: an Empirical Investigation.

This paper analyzes the dynamics of wages and workers' mobility within firms with a hierarchical structure of job levels. The theoretical model proposed by Gibbons and Waldman (1999), that combines the notions of human capital accumulation, job rank assignments based on comparative advantage and learning about workers' abilities, is implemented empirically to measure the importance of these elements in explaining the wage policy of firms. Survey data from the GSOEP (German Socio-Economic Panel) are used to draw conclusions on the common features characterizing the wage policy of firms from a large sample of firms. The GSOEP survey also provides information on the worker's rank within his firm which is usually not available in other surveys. The results are consistent with non-random selection of workers onto the rungs of a job ladder. There is no direct evidence of learning about workers' unobserved abilities but the analysis reveals that unmeasured ability is an important factor driving wage dynamics. Finally, job rank effects remain significant even after controlling for measured and unmeasured characteristics.

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.

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.

Identification, Weak Instruments and Statistical Inference in Econometrics

We discuss statistical inference problems associated with identification and testability in econometrics, and we emphasize the common nature of the two issues. After reviewing the relevant statistical notions, we consider in turn inference in nonparametric models and recent developments on weakly identified models (or weak instruments). We point out that many hypotheses, for which test procedures are commonly proposed, are not testable at all, while some frequently used econometric methods are fundamentally inappropriate for the models considered. Such situations lead to ill-defined statistical problems and are often associated with a misguided use of asymptotic distributional results. Concerning nonparametric hypotheses, we discuss three basic problems for which such difficulties occur: (1) testing a mean (or a moment) under (too) weak distributional assumptions; (2) inference under heteroskedasticity of unknown form; (3) inference in dynamic models with an unlimited number of parameters. Concerning weakly identified models, we stress that valid inference should be based on proper pivotal functions —a condition not satisfied by standard Wald-type methods based on standard errors — and we discuss recent developments in this field, mainly from the viewpoint of building valid tests and confidence sets. The techniques discussed include alternative proposed statistics, bounds, projection, split-sampling, conditioning, Monte Carlo tests. The possibility of deriving a finite-sample distributional theory, robustness to the presence of weak instruments, and robustness to the specification of a model for endogenous explanatory variables are stressed as important criteria assessing alternative procedures.

Methods to Estimate Dynamic Stochastic General Equilibrium Models

This paper employs the one-sector Real Business Cycle model as a testing ground for four different procedures to estimate Dynamic Stochastic General Equilibrium (DSGE) models. The procedures are: 1 ) Maximum Likelihood, with and without measurement errors and incorporating Bayesian priors, 2) Generalized Method of Moments, 3) Simulated Method of Moments, and 4) Indirect Inference. Monte Carlo analysis indicates that all procedures deliver reasonably good estimates under the null hypothesis. However, there are substantial differences in statistical and computational efficiency in the small samples currently available to estimate DSGE models. GMM and SMM appear to be more robust to misspecification than the alternative procedures. The implications of the stochastic singularity of DSGE models for each estimation method are fully discussed.

La zone fictive de l'infra-droit : l'intégration des règles administratives dans la catégories des textes réglementaires

Un résumé en anglais est également disponible.

Globin Gene Expression: Role of Transcription Factors

La dérégulation de l'expression génétique est une base pathophysiologique de plusieurs maladies. On a utilisé le locus du gène β-globine humain comme modèle pour élucider le mécanisme de régulation de la transcription génétique et évaluer son expression génétique durant l'érythropoïèse. La famille des protéines 'E' est composée de facteurs de transcription qui possèdent plusieurs sites de liaison au sein de locus du gène β-globine, suggérant leur rôle potentiel dans la régulation de l’expression de ces gènes. Nous avons montré que les facteurs HEB, E2A et ETO2 interagissent d’une manière significative avec la région contrôle du Locus (LCR) et avec les promoteurs des gènes de la famille β-globine. Le recrutement de ces facteurs au locus est modifié lors de l'érythropoïèse dans les cellules souches hematopoitiques et les cellules erythroides de souris transgéniques pour le locus de la β-globine humain, ainsi que dans les cellules progénitrices hématopoïétiques humaines. De plus par cette étude, nous démontrons pour la première fois que le gène β-globine humain est dans une chromatine active et qu’il interagit avec des facteurs de transcriptions de type suppresseurs dans les cellules progénitrices lymphoïdes (voie de différentiation alternative). Cette étude a aussi été faite dans des souris ayant une génétique mutante caractérisée par l'absence des facteurs de transcription E2A ou HEB.

Les facteurs à homéodomaine Pitx et Irx au cours du développement des membres postérieurs

Les facteurs de transcription Pitx ont été impliqués dans la croissance et la détermination de l’identité des membres postérieurs. D’abord, l’inactivation de Pitx1 chez la souris résulte en la transformation partielle des membres postérieurs en membres antérieurs. Ensuite, la double mutation de Pitx1 et de Pitx2 a montré l’activité redondante de ces facteurs pour la croissance des membres postérieurs. Ainsi, les souris mutantes Pitx1-/-;Pitx2néo/néo montrent une perte des éléments squelettiques proximaux et antérieurs. Des travaux récents ont impliqué les gènes de la famille des Iroquois dans le développement des membres. Tout particulièrement, les souris Irx3-/-;Irx5-/- montrent la perte des éléments squelettiques proximaux et antérieurs, exclusivement au niveau des membres postérieurs. Cette phénocopie entre les souris mutantes pour Pitx1/2 et Irx3/5 nous a amenés à poser trois hypothèses : (1) les Pitx sont responsables de l’expression de Irx dans les bourgeons postérieurs ; (2) à l’inverse, les Irx dirigent l’expression des Pitx ; (3) les Pitx et les Irx participent ensemble au programme génétique de croissance des bourgeons postérieurs. Nous avons pu conclure que les Pitx et les Irx font partie de cascades de régulation indépendantes l’une de l’autre et qu’ils sont capables d’interaction transcriptionnelle autant sur un promoteur générique que sur des régions conservées du locus de Tbx4. Enfin, autant l’inactivation Pitx que celle des Irx mène à un retard d’expression de Pax9 exclusivement dans les bourgeons postérieurs. Ainsi, les Pitx et les Irx semblent agir sur des programmes génétiques parallèles impliqués dans la croissance et le patterning des membres postérieurs.

La responsabilité précontractuelle, perspectives québécoise et internationale

Ce mémoire aborde la question de la responsabilité précontractuelle en regardant son incidence en droit québécois ainsi qu'en droit international. Plus précisément, il s'agit de savoir comment est traitée la rupture des négociations lorsqu'aucun avant-contrat n'a été rédigé à cet effet. Afin de pouvoir déterminer ses grands paramètres, le texte aborde dans un premier temps la nature de cette responsabilité. N'étant pas codifiée en droit québécois et ne faisant l'objet d'aucune spécification dans les grands instruments internationaux, cette nature doit être associée à l'un des deux grands régimes de responsabilité soit: contractuel ou extracontractuel. L'importance de cette détermination n'est pas simplement théorique puisqu'elle a une influence directe sur plusieurs éléments comme la prescription ou le droit international privé. Au Québec, la doctrine et la jurisprudence ont choisi d'associer la responsabilité précontractuelle au domaine extracontractuel. Ainsi, elle devra suivre le modèle classique de faute, dommage et lien causal. Cependant, en droit international, la question de la nature reste encore nébuleuse compte tenu de la diversité des membres qui composent les comités d'élaboration des normes. Tous s'entendent pourtant sur un concept fondamental entourant la responsabilité précontractuelle : la bonne foi. Elle est au centre de la faute et dicte une éthique des pourparlers. Ainsi, dans un deuxième temps, la mise en œuvre de la responsabilité est abordée. De cette notion de bonne foi découlent de nombreux devoirs que les parties négociantes se doivent de respecter. Ils sont de création jurisprudentielle et demandent une étude au cas par cas. La liberté contractuelle étant le principe de base dans la formation des contrats, les tribunaux québécois sanctionnent rarement les cas de rupture des négociations. C'est ce principe de liberté qui empêche les pays de common law d'accepter le concept de bonne foi et de responsabilité précontractuelle, même s'ils sanctionnent, par l'intermédiaire de mécanismes, les comportements fautifs dans les pourparlers. Finalement, les dommages et les intérêts pouvant être réclamés varient. Au Québec et en France, autant les pertes subies que les gains manqués sont indemnisés tandis que les instruments internationaux sont plus réticents pour accorder un montant pour le gain manqué. Bref, la responsabilité précontractuelle est en pleine construction et son utilisation devant les tribunaux est encore peu fréquente.