Propriétés géométriques des surfaces associées aux solutions des modèles sigma grassmanniens en deux dimensions

Dans cette thèse, nous analysons les propriétés géométriques des surfaces obtenues des solutions classiques des modèles sigma bosoniques et supersymétriques en deux dimensions ayant pour espace cible des variétés grassmanniennes G(m,n). Plus particulièrement, nous considérons la métrique, les formes fondamentales et la courbure gaussienne induites par ces surfaces naturellement plongées dans l'algèbre de Lie su(n). Le premier chapitre présente des outils préliminaires pour comprendre les éléments des chapitres suivants. Nous y présentons les théories de jauge non-abéliennes et les modèles sigma grassmanniens bosoniques ainsi que supersymétriques. Nous nous intéressons aussi à la construction de surfaces dans l'algèbre de Lie su(n) à partir des solutions des modèles sigma bosoniques. Les trois prochains chapitres, formant cette thèse, présentent les contraintes devant être imposées sur les solutions de ces modèles afin d'obtenir des surfaces à courbure gaussienne constante. Ces contraintes permettent d'obtenir une classification des solutions en fonction des valeurs possibles de la courbure. Les chapitres 2 et 3 de cette thèse présentent une analyse de ces surfaces et de leurs solutions classiques pour les modèles sigma grassmanniens bosoniques. Le quatrième consiste en une analyse analogue pour une extension supersymétrique N=2 des modèles sigma bosoniques G(1,n)=CP^(n-1) incluant quelques résultats sur les modèles grassmanniens. Dans le deuxième chapitre, nous étudions les propriétés géométriques des surfaces associées aux solutions holomorphes des modèles sigma grassmanniens bosoniques. Nous donnons une classification complète de ces solutions à courbure gaussienne constante pour les modèles G(2,n) pour n=3,4,5. De plus, nous établissons deux conjectures sur les valeurs constantes possibles de la courbure gaussienne pour G(m,n). Nous donnons aussi des éléments de preuve de ces conjectures en nous appuyant sur les immersions et les coordonnées de Plücker ainsi que la séquence de Veronese. Ces résultats sont publiés dans la revue Journal of Geometry and Physics. Le troisième chapitre présente une analyse des surfaces à courbure gaussienne constante associées aux solutions non-holomorphes des modèles sigma grassmanniens bosoniques. Ce travail généralise les résultats du premier article et donne un algorithme systématique pour l'obtention de telles surfaces issues des solutions connues des modèles. Ces résultats sont publiés dans la revue Journal of Geometry and Physics. Dans le dernier chapitre, nous considérons une extension supersymétrique N=2 du modèle sigma bosonique ayant pour espace cible G(1,n)=CP^(n-1). Ce chapitre décrit la géométrie des surfaces obtenues des solutions du modèle et démontre, dans le cas holomorphe, qu'elles ont une courbure gaussienne constante si et seulement si la solution holomorphe consiste en une généralisation de la séquence de Veronese. De plus, en utilisant une version invariante de jauge du modèle en termes de projecteurs orthogonaux, nous obtenons des solutions non-holomorphes et étudions la géométrie des surfaces associées à ces nouvelles solutions. Ces résultats sont soumis dans la revue Communications in Mathematical Physics.

Comparaison des performances relatives de différents modèles d'estimation de la valeur à risque.

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Analyse bayésienne de modèles de durées des transactions financières de haute fréquence.

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The Seemingly Unrelated Dynamic Cointegration Regression Model and Testing for Purching Power Parity.

This paper studies seemingly unrelated linear models with integrated regressors and stationary errors. By adding leads and lags of the first differences of the regressors and estimating this augmented dynamic regression model by feasible generalized least squares using the long-run covariance matrix, we obtain an efficient estimator of the cointegrating vector that has a limiting mixed normal distribution. Simulation results suggest that this new estimator compares favorably with others already proposed in the literature. We apply these new estimators to the testing of purchasing power parity (PPP) among the G-7 countries. The test based on the efficient estimates rejects the PPP hypothesis for most countries.

International Business Cycles: What Are the Facts?

Modern business cycle theory involves developing models that explain stylized facts. For this strategy to be successful, these facts should be well established. In this paper, we focus on the stylized facts of international business cycles. We use the generalized method of moments and quarterly data from nineteen industrialized countries to estimate pairwise cross-country and within-country correlations of macroeconomic aggregates. We calculate standard errors of the statistics for our unique panel of data and test hypotheses about the relative sizes of these correlations. We find a lower cross-country correlation of all aggregates and especially of consumption than in previous studies. The cross-country correlations of consumption, output and Solow residuals are not significantly different from one another over the whole sample, but there are significant differences in the post-1973 subsample.

International Transmission of the Business Cycle in a Multi-Sector Model.

Multi-country models have not been very successful in replicating important features of the international transmission of business cycles. Standard models predict cross-country correlations of output and consumption which are respectively too low and too high. In this paper, we build a multi-country model of the business cycle with multiple sectors in order to analyze the role of sectoral shocks in the international transmission of the business cycle. We find that a model with multiple sectors generates a higher cross-country correlation of output than standard one-sector models, and a lower cross-country correlation of consumption. In addition, it predicts cross-country correlations of employment and investment that are closer to the data than the standard model. We also analyze the relative effects of multiple sectors, trade in intermediate goods, imperfect substitution between domestic and foreign goods, home preference, capital adjustment costs, and capital depreciation on the international transmission of the business cycle.

Sharing a River

A group of agents located along a river have quasi-linear preferences over water and money. We ask how the water should be allocated and what money transfers should be performed. We are interested in efficiency, stability (in the sense of the core), and fairness (in a sense to be defined). We first show that the cooperative game associated with our problem is convex : its core is therefore large and easily described. Next, we propose the following fairness requirement : no group of agents should enjoy a welfare higher than what it could achieve in the absence of the remaining agents. We prove that only one welfare vector in the core satisfies this condition : it is the marginal contribution vector corresponding to the ordering of the agents along the river. We discuss how it could be decentralized or implemented.

Simulation-Based Finite and Large Sample Tests in Multivariate Regressions.

In the context of multivariate linear regression (MLR) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. In this paper, we propose a general method for constructing exact tests of possibly nonlinear hypotheses on the coefficients of MLR systems. For the case of uniform linear hypotheses, we present exact distributional invariance results concerning several standard test criteria. These include Wilks' likelihood ratio (LR) criterion as well as trace and maximum root criteria. The normality assumption is not necessary for most of the results to hold. Implications for inference are two-fold. First, invariance to nuisance parameters entails that the technique of Monte Carlo tests can be applied on all these statistics to obtain exact tests of uniform linear hypotheses. Second, the invariance property of the latter statistic is exploited to derive general nuisance-parameter-free bounds on the distribution of the LR statistic for arbitrary hypotheses. Even though it may be difficult to compute these bounds analytically, they can easily be simulated, hence yielding exact bounds Monte Carlo tests. Illustrative simulation experiments show that the bounds are sufficiently tight to provide conclusive results with a high probability. Our findings illustrate the value of the bounds as a tool to be used in conjunction with more traditional simulation-based test methods (e.g., the parametric bootstrap) which may be applied when the bounds are not conclusive.

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.

Inflation Targeting Under Asymmetric Preferences

This paper develops and estimates a game-theoretical model of inflation targeting where the central banker's preferences are asymmetric around the targeted rate. In particular, positive deviations from the target can be weighted more, or less, severely than negative ones in the central banker's loss function. It is shown that some of the previous results derived under the assumption of symmetry are not robust to the generalization of preferences. Estimates of the central banker's preference parameters for Canada, Sweden, and the United Kingdom are statistically different from the ones implied by the commonly used quadratic loss function. Econometric results are robust to different forecasting models for the rate of unemployment but not to the use of measures of inflation broader than the one targeted.

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.

Organizational Design of R&D Activities

This paper addresses the question of whether R&D should be carried out by an independent research unit or be produced in-house by the firm marketing the innovation. We define two organizational structures. In an integrated structure, the firm that markets the innovation also carries out and finances research leading to the innovation. In an independent structure, the firm that markets the innovation buys it from an independent research unit which is financed externally. We compare the two structures under the assumption that the research unit has some private information about the real cost of developing the new product. When development costs are negatively correlated with revenues from the innovation, the integrated structure dominates. The independent structure dominates in the opposite case.

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.