L'Approximation diophantienne simultanée et l'optimisation discrète

Étant donnée une fonction bornée (supérieurement ou inférieurement) $f:\mathbb{N}^k \To \Real$ par une expression mathématique, le problème de trouver les points extrémaux de $f$ sur chaque ensemble fini $S \subset \mathbb{N}^k$ est bien défini du point de vu classique. Du point de vue de la théorie de la calculabilité néanmoins il faut éviter les cas pathologiques où ce problème a une complexité de Kolmogorov infinie. La principale restriction consiste à définir l'ordre, parce que la comparaison entre les nombres réels n'est pas décidable. On résout ce problème grâce à une structure qui contient deux algorithmes, un algorithme d'analyse réelle récursive pour évaluer la fonction-coût en arithmétique à précision infinie et un autre algorithme qui transforme chaque valeur de cette fonction en un vecteur d'un espace, qui en général est de dimension infinie. On développe trois cas particuliers de cette structure, un de eux correspondant à la méthode d'approximation de Rauzy. Finalement, on établit une comparaison entre les meilleures approximations diophantiennes simultanées obtenues par la méthode de Rauzy (selon l'interprétation donnée ici) et une autre méthode, appelée tétraédrique, que l'on introduit à partir de l'espace vectoriel engendré par les logarithmes de nombres premiers.

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.

Asymptotic Approximations in the Near-Integrated Model with a Non-Zero Initial Condition

This paper considers various asymptotic approximations in the near-integrated firstorder autoregressive model with a non-zero initial condition. We first extend the work of Knight and Satchell (1993), who considered the random walk case with a zero initial condition, to derive the expansion of the relevant joint moment generating function in this more general framework. We also consider, as alternative approximations, the stochastic expansion of Phillips (1987c) and the continuous time approximation of Perron (1991). We assess how these alternative methods provide or not an adequate approximation to the finite-sample distribution of the least-squares estimator in a first-order autoregressive model. The results show that, when the initial condition is non-zero, Perron's (1991) continuous time approximation performs very well while the others only offer improvements when the initial condition is zero.

Exact Multivariate Tests of Asset Pricing Models with Stable Asymmetric Distributions

In this paper, we propose exact inference procedures for asset pricing models that can be formulated in the framework of a multivariate linear regression (CAPM), allowing for stable error distributions. The normality assumption on the distribution of stock returns is usually rejected in empirical studies, due to excess kurtosis and asymmetry. To model such data, we propose a comprehensive statistical approach which allows for alternative - possibly asymmetric - heavy tailed distributions without the use of large-sample approximations. The methods suggested are based on Monte Carlo test techniques. Goodness-of-fit tests are formally incorporated to ensure that the error distributions considered are empirically sustainable, from which exact confidence sets for the unknown tail area and asymmetry parameters of the stable error distribution are derived. Tests for the efficiency of the market portfolio (zero intercepts) which explicitly allow for the presence of (unknown) nuisance parameter in the stable error distribution are derived. The methods proposed are applied to monthly returns on 12 portfolios of the New York Stock Exchange over the period 1926-1995 (5 year subperiods). We find that stable possibly skewed distributions provide statistically significant improvement in goodness-of-fit and lead to fewer rejections of the efficiency hypothesis.

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.

Predictors and Consequences of Simultaneous Alcohol and Cannabis Use in Adolescents

Background/Aims: The simultaneous use of alcohol and cannabis is common among adolescents, but has been little studied. In this study, we examine predictors and consequences of this behavior in a population-based sample of high school students. Method: Self-reports were obtained from students in Quebec (Canada) followed throughout high school (N=6589). Logistic regressions were used to test the association between individual, family, and peerrelated predictors in grades 7–8 and simultaneous alcohol and cannabis use in grade 10, as well as between simultaneous alcohol and cannabis use in grade 10 and experiencing 3 or more substance-related problems of various types (legal, physical, etc.) in grade 11. Results: Most predictors in grades 7–8 were associated with simultaneous alcohol and cannabis use in grade 10. Only variables reflecting early-onset substance use involvement — alcohol intoxication, cannabis use, and drug use by close friend(s) — remained predictive in a multivariate model. Simultaneous alcohol and cannabis use was associated with increased substance-related problems in grade 11, above and beyond baseline problems and the concurrent use of the two substances in separate episodes in grade 10. Conclusions: Simultaneous alcohol and cannabis use 1) is anticipated by multiple psychosocial risk factors which come together with individual and peer substance use in early high school and 2) is independently predictive of subsequent substance-related problems. Providing adolescents with adequate information regarding the potential harm of simultaneous use may be a useful prevention strategy.