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.

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.

Simulation-Based Finite-Sample Normality Tests in Linear Regressions

In the literature on tests of normality, much concern has been expressed over the problems associated with residual-based procedures. Indeed, the specialized tables of critical points which are needed to perform the tests have been derived for the location-scale model; hence reliance on available significance points in the context of regression models may cause size distortions. We propose a general solution to the problem of controlling the size normality tests for the disturbances of standard linear regression, which is based on using the technique of Monte Carlo tests.

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.

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.

Time Reversibility of Stationary Regular Finite State Markov Chains

We propose an alternate parameterization of stationary regular finite-state Markov chains, and a decomposition of the parameter into time reversible and time irreversible parts. We demonstrate some useful properties of the decomposition, and propose an index for a certain type of time irreversibility. Two empirical examples illustrate the use of the proposed parameter, decomposition and index. One involves observed states; the other, latent states.

Monte Carlo Tests with Nuisance Parameters: A General Approach to Finite-Sample Inference and Nonstandard Asymptotics

The technique of Monte Carlo (MC) tests [Dwass (1957), Barnard (1963)] provides an attractive method of building exact tests from statistics whose finite sample distribution is intractable but can be simulated (provided it does not involve nuisance parameters). We extend this method in two ways: first, by allowing for MC tests based on exchangeable possibly discrete test statistics; second, by generalizing the method to statistics whose null distributions involve nuisance parameters (maximized MC tests, MMC). Simplified asymptotically justified versions of the MMC method are also proposed and it is shown that they provide a simple way of improving standard asymptotics and dealing with nonstandard asymptotics (e.g., unit root asymptotics). Parametric bootstrap tests may be interpreted as a simplified version of the MMC method (without the general validity properties of the latter).

Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing

Statistical tests in vector autoregressive (VAR) models are typically based on large-sample approximations, involving the use of asymptotic distributions or bootstrap techniques. After documenting that such methods can be very misleading even with fairly large samples, especially when the number of lags or the number of equations is not small, we propose a general simulation-based technique that allows one to control completely the level of tests in parametric VAR models. In particular, we show that maximized Monte Carlo tests [Dufour (2002)] can provide provably exact tests for such models, whether they are stationary or integrated. Applications to order selection and causality testing are considered as special cases. The technique developed is applied to quarterly and monthly VAR models of the U.S. economy, comprising income, money, interest rates and prices, over the period 1965-1996.

Le care entre dépendance et domination: l'intérêt de la théorie néorépublicaine pour penser une "caring society".

L’éthique du care est confrontée à un problème qui a des allures de paradoxe: bien que sa politisation paraisse nécessaire, l’éthique du care ne semble pas pouvoir trouver en elle-même les ressources suffisantes à la formulation d’une théorie politique compréhensive. Il ne semble pas exister de théorie politique du care à part entière. Cet article examine la fécondité d’un rapprochement entre éthique du care et théorie néorépublicaine de la non-domination. Le résultat, non négligeable, serait de garantir des formes importantes de protections aux activités de care, mais il cela n’empêcherait pas nécessairement les représentations négatives de ces activités, dont on peut supposer qu’elle est l’un des facteurs de la marginalisation et de la répartition inégale qui les affecte. Pour bloquer ces représentations dégradantes, il faudrait que le care soit discuté et défini dans l’espace public non seulement comme un lieu possible de la domination, mais comme un aspect fondamental et positif de la vie individuelle et collective.

Préférences décisives et précarité: Comment distinguer liberté comme capabilité et liberté comme non-domination?

Cet article se penche sur le rapport de la conception républicaine de la liberté comme non-domination défendue par P. Pettit avec la conception de la liberté comme capabilité proposée par A. Sen. L’usage que fait Pettit de la conception défendue par Sen lui permet d’avancer une conception plus réaliste des préférences des individus en contexte social. Cette définition des «préférences décisives» guide toute sa démonstration de la compatibilité de la liberté comme capabilité avec la théorie néorépublicaine. Elle lui permet en outre de donner une valeur particulière à l’objectif social de lutte contre la précarité, précarité comprise comme situation dans laquelle un individu est placé sous la dépendance arbitraire d’un autre en l’empêchant de se mouvoir à sa guise dans le jeu social. Nous examinons les enjeux de cette articulation à la lumière de la réponse critique qui a été formulée par Sen à l’effet que la conception néorépublicaine limitait trop la pluralité sociale de la liberté. Enfin, nous esquissons une manière de réconcilier l’approche par les capabilités avec la conception républicaine.

La contestation internationale : les problèmes de la souveraineté et de la domination

Dans ce travail, nous posons d’abord la question de la légitimité de la contestation internationale. En partant de la conception libérale de la souveraineté étatique, nous montrons que la contestation internationale pourrait être critiquée pour l’interférence qu’elle crée entre des acteurs étrangers. Pour défendre la légitimité de la contestation, nous argumentons en faveur de la position républicaine de Philip Pettit selon laquelle la souveraineté étatique ne devrait pas être comprise comme une absence d’interférence, mais plutôt comme une absence de domination. En montrant que les problèmes environnementaux peuvent être compris en tant que domination écologique, nous tentons alors de démontrer que la contestation internationale ne pose pas nécessairement problème pour la souveraineté des États, mais qu’au contraire, celle-ci peut servir protection contre d’éventuels cas de domination. Dans la seconde partie du travail, nous explorons la question de la légitimité des moyens de contestation utilisés par les activistes. En conservant les idées de Pettit concernant la domination, nous prenons toutefois nos distances par rapport à cet auteur et sa conception délibérative de la contestation. Nous amorcerons finalement la réflexion dans le but de trouver des critères pouvant légitimer certains recours à des moyens de contestation plus radicaux. Nous défendons notamment une position originale, voulant que la contestation soit comprise en continuité avec la délibération plutôt qu’en rupture avec celle-ci.

Homogeneity and heterogeneity of political traditions in the remaking of world order

Deux décennies après la chute de l'URSS (1991), ce mémoire propose une réévaluation de la thèse de Francis Fukuyama sur la Fin de l'Histoire, élaborée en 1989, qui postule qu'avec la chute de l'URSS aucune idéologie ne peut rivaliser avec la démocratie libérale capitaliste; et de la thèse de Samuel P. Huntington sur le Choc des civilisations, élaborée en 1993, qui pose l'existence d'un nombre fini de civilisations homogènes et antagonistes. Pourtant, lorsque confrontées à une étude approfondie des séquences historiques, ces deux théories apparaissent pour le moins relatives. Deux questions ont été traitées: l'interaction entre Idéologie et Conditions historiques, et la thèse de l'homogénéité intracivilisationnelle et de l'hétérogénéité antagoniste intercivilisationnelle. Sans les invalider complètement, cette recherche conclut toutefois que ces deux théories doivent être nuancées; elles se situent aux deux extrémités du spectre des relations internationales. La recherche effectuée a montré que les idéologies et leur poids relatif sont tributaires d'un contexte, contrairement à Fukuyama qui les pose dans l'absolu. De plus, l'étude de la Chine maoïste et particulièrement de la pensée de Mao Zedong montre que les traditions politiques locales sont plus hétérogènes qu'il n'y paraît au premier abord, ce qui relativise la thèse de Huntington. En conclusion, les rapports entre États sont plus dynamiques que ne le laissent penser les thèses de Fukuyama et de Huntington.