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.

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.

Culture du corps et technosciences : vers une « mise à niveau » technique de l’humain? Analyse des représentations du corps soutenues par le mouvement transhumaniste

L’intérêt marqué porté actuellement aux recherches NBIC (nano-bio-info-cognitivo technologies) visant l’optimisation des capacités humaines augure d’un profond bouleversement dans nos représentations du corps humain et du rapport humain-machine. Tour à tour, des travaux issus des domaines du génie génétique, de la pharmacologie, des biotechnologies ou des nanotechnologies nous promettent un corps moins sujet à la maladie, mieux « adapté » et surtout plus malléable. Cette construction en laboratoire d’un corps amélioré fait amplement écho aux préoccupations contemporaines concernant la santé parfaite, le processus de vieillissement, l’inaptitude, l’apparence, la performance, etc. En vue d’analyser les transformations qu’induisent ces recherches sur les représentations du corps, nous avons construit un modèle théorique appuyé, d’une part, sur des travaux en sociologie du corps et, d’autre part, sur des travaux en épistémologie des sciences. Puis, en scrutant différents textes de vulgarisation scientifique produits par des chercheurs transhumanistes – militant ouvertement en faveur d’une optimisation radicale des capacités humaines par le biais des technosciences –, il a été observé que les représentations du corps s’organisent autour de trois principaux noyaux. Le corps humain est présenté, dans ce discours, comme étant à la fois informationnel, technologiquement perfectible et obsolète. Cette représentation tripartite du corps permet aux transhumanistes d’ériger leur modèle d’action (i.e. amélioration des capacités physiques, intellectuelles, sensitives, émotionnelles, etc.) à titre de nécessité anthropologique. À leurs yeux, l’amélioration des conditions humaines doit passer par une mutation contrôlée de la biologie (i.e. une hybridation avec la machine) du fait que le corps serait « inadapté » au monde contemporain. Ainsi, les promesses NBIC, une fois récupérées par les chercheurs transhumanistes, se voient exacerbées et prennent une tonalité péremptoire. Ceci contribue vivement à la promotion du posthumain ou du cyborg, soit d’un individu transformé dans l’optique d’être plus robuste et intelligent, de moduler sa sensitivité et ses états émotifs et de vivre plus longtemps, voire indéfiniment. Enfin, situé à mi-chemin entre la science et la science-fiction, ce projet est qualifié de techno-prophétie en ce qu’il produit d’innombrables prévisions basées sur les avancées technoscientifiques actuelles et potentielles. Afin d’accroître l’acceptabilité sociale de leur modèle d’action, les transhumanistes ne font pas uniquement appel à la (potentielle) faisabilité technique; ils s’appuient également sur des valeurs socialement partagées, telles que l’autodétermination, la perfectibilité humaine, l’égalité, la liberté ou la dignité. Néanmoins, la lecture qu’ils en font est parfois surprenante et rompt très souvent avec les conceptions issues de la modernité. À leur avis, le perfectionnement humain doit s’opérer par le biais des technosciences (non des institutions sociales), sur le corps même des individus (non sur l’environnement) et en vertu de leur « droit » à l’autodétermination compris comme un droit individuel d’optimiser ses capacités. De même, les technosciences doivent, disent-ils, être démocratisées afin d’en garantir l’accessibilité, de réduire les inégalités biologiques et de permettre à chacun de renforcer son sentiment d’identité et d’accomplissement. L’analyse du discours transhumaniste nous a donc permis d’observer leurs représentations du corps de même que la résonance culturelle du projet qu’ils proposent.

Téléphone, phonographe et radio : l'imaginaire sonore entre textualité et reproduction technique

Ce mémoire décrit l’imaginaire sonore tel qu’il s’est transformé par l’apparition de dispositifs de reproduction (téléphone, phonographe et radio) à la fin du 19ème siècle et au début du 20ème siècle. Si ces appareils de reproduction sonore signalent un nouveau contexte socioculturel permettant la captation, la conservation et la transmission de manifestations sensibles, ils transforment également la manière de concevoir le son, ils modifient le statut de l’audition par rapport aux autres sens et reconfigurent un imaginaire qui traduit un rapport à soi, à autrui et au monde. Cette étude littéraire de la reproductibilité sonore propose une réflexion entre technologie et poétique en questionnant l’idée de communication. L’élément spécifique qui caractérise les appareils de reproduction sonore est un objet technique nommé «transducteur ». Je considère le transducteur à la fois comme métaphore et matérialité de médiation; conçu en termes de dispositif de transduction, ce concept permet une différente compréhension des pratiques sociales et de l’imaginaire constituant cet artefact culturel.