Évaluation des écosystèmes en début de millénaire: conclusions et retombées

L'évaluation des écosystèmes est l'un des pivots essentiels pour l'élaboration de moyens adaptés permettant de lutter contre la diminution massive de la biodiversité. Pour la première fois, elle a fait l'objet d'une analyse à l'échelle mondiale dans le cadre de l'Evaluation des écosystèmes en début de millénaire (EM). Le rassemblement de plus d’un millier de chercheurs et de plusieurs organismes internationaux durant quatre années ont permis de dessiner la carte nécessaire à toute action efficace. L'article expose les éléments principaux de l'EM : l'évaluation des écosystèmes en tant que tels, mais surtout des services écosystémiques, dans toutes leurs dimensions, en ce que leur évolution affecte le bien-être humain. Il analyse ensuite les quatre points principaux de l'apport de l'EM, des avantages de l'utilisation croissante des services écologiques à sa non viabilité. Des scénarios, modèles et outils sont proposés pour inverser la courbe négative d'appauvrissement de la biodiversité et des services écosystémiques dans un premier bilan des retombées de l'EM.

Aspects géométriques et intégrables des modèles de matrices aléatoires

Travail réalisé en cotutelle avec l'université Paris-Diderot et le Commissariat à l'Energie Atomique sous la direction de John Harnad et Bertrand Eynard.

What is ambiguity?

The concept of Ambiguity designates those situations where the information available to the decision maker is insufficient to form a probabilistic view of the world. Thus, it has provided the motivation for departing from the Subjective Expected Utility (SEU) paradigm. Yet, the formalization of the concept is missing. This is a grave omission as it leaves non-expected utility models hanging on a shaky ground. In particular, it leaves unanswered basic questions such as: (1) Does Ambiguity exist?; (2) If so, which situations should be labeled as "ambiguous"?; (3) Why should one depart from Subjective Expected Utility (SEU) in the presence of Ambiguity?; and (4) If so, what kind of behavior should emerge in the presence of Ambiguity? The present paper fills these gaps. Specifically, it identifies those information structures that are incompatible with SEU theory, and shows that their mathematical properties are the formal counterpart of the intuitive idea of insufficient information. These are used to give a formal definition of Ambiguity and, consequently, to distinguish between ambiguous and unambiguous situations. Finally, the paper shows that behavior not conforming to SEU theory must emerge in correspondence of insufficient information and identifies the class of non-EU models that emerge in the face of Ambiguity. The paper also proposes a new comparative definition of Ambiguity, and discusses its relation with some of the existing literature.

Algal Biofuels: Challenges and Opportunities

Biodiesel production using microalgae is attractive in a number of respects. Here a number of pros and cons to using microalgae for biofuels production are reviewed. Algal cultivation can be carried out using non-arable land and non-potable water with simple nutrient supply. In addition, algal biomass productivities are much higher than those of vascular plants and the extractable content of lipids that can be usefully converted to biodiesel, triacylglycerols (TAGs) can be much higher than that of the oil seeds now used for first generation biodiesel. On the other hand, practical, cost-effective production of biofuels from microalgae requires that a number of obstacles be overcome. These include the development of low-cost, effective growth systems, efficient and energy saving harvesting techniques, and methods for oil extraction and conversion that are environmentally benign and cost-effective. Promising recent advances in these areas are highlighted.

Characterization of microalgae native to Quebec for biofuel production.

La production de biodiésel par des microalgues est intéressante à plusieurs niveaux. Dans le premier chapitre, un éventail de pour et contres concernant l’utilisation de microalgues pour la production de biocarburant sont ici révisés. La culture d’algues peut s'effectuer en utilisant des terres non-arables, de l’eau non-potable et des nutriments de base. De plus, la biomasse produite par les algues est considérablement plus importante que celle de plantes vasculaires. Plusieurs espèces on le contenu lipidique en forme de triacylglycérols (TAGs), qui peut correspondre jusqu'à 30% - 40% du poids sec de la biomasse. Ces proportions sont considérablement plus élevées que celui des huiles contenues dans les graines actuellement utilisées pour le biodiésel de première génération. Par contre, une production pratique et peu couteuse de biocarburant par des microalgues requiert de surpasser plusieurs obstacles. Ceci inclut le développement de systèmes de culture efficace à faible coût, de techniques de récupération requérant peu d’énergie, et de méthodes d’extraction et de conversion de l’huile non-dommageables pour l’environnement et peu couteuses. Le deuxième chapitre explore l'une des questions importantes soulevées dans le premier chapitre: la sélection d'une souche pour la culture. Une collection de souches de microalgues d'eau douce indigène au Québec a été établi et examiné au niveau de la diversité physiologique. Cette collection est composée de cent souches, que apparaissaient très hétérogènes en terme de croissance lorsque mises en culture à 10±2 °C ou 22±2 °C sur un effluent secondaire d’une usine municipale de traitement des eaux usées (EU), défini comme milieu Bold's Basal Medium (BBM). Des diagrammes de dispersion ont été utilisés pour étudier la diversité physiologique au sein de la collection, montrant plusieurs résultats intéressants. Il y avait une dispersion appréciable dans les taux de croissance selon les différents types de milieux et indépendamment de la température. De manière intéressante, en considérant que tous les isolats avaient initialement été enrichis sur milieu BBM, la distribution était plutôt symétrique autour de la ligne d’iso-croissance, suggérant que l’enrichissement sur BBM n’a pas semblé biaiser la croissance des souches sur ce milieu par rapport aux EU. Également, considérant que les isolats avaient d’abord été enrichis à 22°C, il est assez surprenant que la distribution de taux de croissance spécifiques soit aussi symétrique autour de la ligne d’iso-croissance, avec grossièrement des nombres égaux d’isolats de part et d’autre. Ainsi, l’enrichissement à 22°C ne semble pas biaiser les cellules vers une croissance à cette température plutôt que vers 10°C. Les diagrammes de dispersion obtenus lorsque le pourcentage en lipides de cultures sur BBM ont été comparées à des cultures ayant poussé sur EU soit à 10°C ou 22°C rendent évident que la production de lipides est favorisée par la culture sur EU aux deux températures, et que la production lipidique ne semble pas particulièrement plus favorisée par l’une ou l’autre de ces températures. Lorsque la collection a été examinée pour y déceler des différences avec le site d’échantillonnage, une analyse statistique a montré grossièrement que le même degré de diversité physiologique était retrouvé dans les échantillons des deux différents sites. Le troisième chapitre a poursuivi l'évaluation de la culture d'algues au Québec. L’utilisation de déchets industriels riches en nutriments minéraux et en sources de carbone pour augmenter la biomasse finale en microalgues et le produit lipidique à faible coût est une stratégie importante pour rendre viable la technologie des biocarburants par les algues. Par l’utilisation de souches de la collection de microalgues de l’Université de Montréal, ce rapport montre pour la première fois que des souches de microalgues peuvent pousser en présence de xylose, la source de carbone majoritairement retrouvée dans les eaux usées provenant des usines de pâte et papier, avec une hausse du taux de croissance de 2,8 fois par rapport à la croissance photoautotrophe, atteignant jusqu’à µ=1,1/jour. En présence de glycérol, les taux de croissance atteignaient des valeurs aussi élevées que µ=1,52/jour. La production lipidique augmentait jusqu’à 370% en présence de glycérol et 180% avec le xylose pour la souche LB1H10, démontrant que cette souche est appropriée pour le développement ultérieur de biocarburants en culture mixotrophe. L'ajout de xylose en cultures d'algues a montré certains effets inattendus. Le quatrième chapitre de ce travail a porté à comprendre ces effets sur la croissance des microalgues et la production de lipides. Quatre souches sauvages indigènes ont été obersvées quotidiennement, avant et après l’ajout de xylose, par cytométrie en flux. Avec quelques souches de Chlorella, l’ajout de xylose induisait une hausse rapide de l’accumulation de lipide (jusqu’à 3,3 fois) pendant les premières six à douze heures. Aux temps subséquents, les cellules montraient une diminution du contenu en chlorophylle, de leur taille et de leur nombre. Par contre, l’unique membre de la famille des Scenedesmaceae avait la capacité de profiter de la présence de cette source de carbone sous culture mixotrophe ou hétérotrophe sans effet négatif apparent. Ces résultats suggèrent que le xylose puisse être utilisé avant la récolte afin de stimuler l’augmentation du contenu lipidique de la culture d’algues, soit en système de culture continu ou à deux étapes, permettant la biorestauration des eaux usées provenant de l’industrie des pâtes et papiers. Le cinquième chapitre aborde une autre déché industriel important: le dioxyde de carbone et les gaz à effet de serre. Plus de la moitié du dioxyde de carbone qui est émis dans l'atmosphère chaque jour est dégagé par un processus stationnaire, soit pour la production d’électricité ou pour la fabrication industrielle. La libération de CO2 par ces sources pourrait être atténuée grâce à la biorestauration avec microalgues, une matière première putative pour les biocarburants. Néanmoins, toutes les cheminées dégagent un gaz différent, et la sélection des souches d'algues est vitale. Ainsi, ce travail propose l'utilisation d’un état de site particulier pour la bioprospection de souches d'algues pour être utilisé dans le processus de biorestauration. Les résultats montrent que l'utilisation d'un processus d'enrichissement simple lors de l'étape d'isolement peut sélectionner des souches qui étaient en moyenne 43,2% mieux performantes dans la production de biomasse que les souches isolées par des méthodes traditionnelles. Les souches isolées dans ce travail étaient capables d'assimiler le dioxyde de carbone à un taux supérieur à la moyenne, comparées à des résultats récents de la littérature.

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

Statistical Inference for Computable General Equilibrium Models with Application to a Model of the Moroccan Economy

We study the problem of measuring the uncertainty of CGE (or RBC)-type model simulations associated with parameter uncertainty. We describe two approaches for building confidence sets on model endogenous variables. The first one uses a standard Wald-type statistic. The second approach assumes that a confidence set (sampling or Bayesian) is available for the free parameters, from which confidence sets are derived by a projection technique. The latter has two advantages: first, confidence set validity is not affected by model nonlinearities; second, we can easily build simultaneous confidence intervals for an unlimited number of variables. We study conditions under which these confidence sets take the form of intervals and show they can be implemented using standard methods for solving CGE models. We present an application to a CGE model of the Moroccan economy to study the effects of policy-induced increases of transfers from Moroccan expatriates.

Computation and Analysis of Multiple Structural-Change Models

In a recent paper, Bai and Perron (1998) considered theoretical issues related to the limiting distribution of estimators and test statistics in the linear model with multiple structural changes. In this companion paper, we consider practical issues for the empirical applications of the procedures. We first address the problem of estimation of the break dates and present an efficient algorithm to obtain global minimizers of the sum of squared residuals. This algorithm is based on the principle of dynamic programming and requires at most least-squares operations of order O(T 2) for any number of breaks. Our method can be applied to both pure and partial structural-change models. Secondly, we consider the problem of forming confidence intervals for the break dates under various hypotheses about the structure of the data and the errors across segments. Third, we address the issue of testing for structural changes under very general conditions on the data and the errors. Fourth, we address the issue of estimating the number of breaks. We present simulation results pertaining to the behavior of the estimators and tests in finite samples. Finally, a few empirical applications are presented to illustrate the usefulness of the procedures. All methods discussed are implemented in a GAUSS program available upon request for non-profit academic use.

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.

Aggregations and Marginalization of GARCH and Stochastic Volatility Models

The GARCH and Stochastic Volatility paradigms are often brought into conflict as two competitive views of the appropriate conditional variance concept : conditional variance given past values of the same series or conditional variance given a larger past information (including possibly unobservable state variables). The main thesis of this paper is that, since in general the econometrician has no idea about something like a structural level of disaggregation, a well-written volatility model should be specified in such a way that one is always allowed to reduce the information set without invalidating the model. To this respect, the debate between observable past information (in the GARCH spirit) versus unobservable conditioning information (in the state-space spirit) is irrelevant. In this paper, we stress a square-root autoregressive stochastic volatility (SR-SARV) model which remains true to the GARCH paradigm of ARMA dynamics for squared innovations but weakens the GARCH structure in order to obtain required robustness properties with respect to various kinds of aggregation. It is shown that the lack of robustness of the usual GARCH setting is due to two very restrictive assumptions : perfect linear correlation between squared innovations and conditional variance on the one hand and linear relationship between the conditional variance of the future conditional variance and the squared conditional variance on the other hand. By relaxing these assumptions, thanks to a state-space setting, we obtain aggregation results without renouncing to the conditional variance concept (and related leverage effects), as it is the case for the recently suggested weak GARCH model which gets aggregation results by replacing conditional expectations by linear projections on symmetric past innovations. Moreover, unlike the weak GARCH literature, we are able to define multivariate models, including higher order dynamics and risk premiums (in the spirit of GARCH (p,p) and GARCH in mean) and to derive conditional moment restrictions well suited for statistical inference. Finally, we are able to characterize the exact relationships between our SR-SARV models (including higher order dynamics, leverage effect and in-mean effect), usual GARCH models and continuous time stochastic volatility models, so that previous results about aggregation of weak GARCH and continuous time GARCH modeling can be recovered in our framework.

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.

Projection-Based Statistical Inference in Linear Structural Models with Possibly Weak Instruments

It is well known that standard asymptotic theory is not valid or is extremely unreliable in models with identification problems or weak instruments [Dufour (1997, Econometrica), Staiger and Stock (1997, Econometrica), Wang and Zivot (1998, Econometrica), Stock and Wright (2000, Econometrica), Dufour and Jasiak (2001, International Economic Review)]. One possible way out consists here in using a variant of the Anderson-Rubin (1949, Ann. Math. Stat.) procedure. The latter, however, allows one to build exact tests and confidence sets only for the full vector of the coefficients of the endogenous explanatory variables in a structural equation, which in general does not allow for individual coefficients. This problem may in principle be overcome by using projection techniques [Dufour (1997, Econometrica), Dufour and Jasiak (2001, International Economic Review)]. AR-types are emphasized because they are robust to both weak instruments and instrument exclusion. However, these techniques can be implemented only by using costly numerical techniques. In this paper, we provide a complete analytic solution to the problem of building projection-based confidence sets from Anderson-Rubin-type confidence sets. The latter involves the geometric properties of “quadrics” and can be viewed as an extension of usual confidence intervals and ellipsoids. Only least squares techniques are required for building the confidence intervals. We also study by simulation how “conservative” projection-based confidence sets are. Finally, we illustrate the methods proposed by applying them to three different examples: the relationship between trade and growth in a cross-section of countries, returns to education, and a study of production functions in the U.S. economy.