On introduit le concept de processus aleatoire vectoriel presque integre. Ce type de processus aide a travailler vers une theorie asymptotique generale de la regression pour des series temporelles multiples

This paper explores implications for one-stage and two-stage decision processes of a theory of choice tha t accommodates nontransitive preferences. It focuses on probabilistic convexification of finite base sets and on choice from convex sets. The one-stage formulation always has a maximally-preferred element in the convex set. Two-stage processes allow not only a holistic procedure for the entire problem, but also give rise to naive and sophisticated sequential procedures. All three have unambiguous solutions, but they can be radically different under intransitivities. The thre e two-stage solutions coincide when preferences are transitive. Copyright 1988 by The Econometric Society.

The exact distribution of the regression t statistic for testing the value of the AR parameter in a Gaussian first ord er autoregressive model is investigated by Monte Carlo methods. The S tudent's t distribution is not a satisfactory approximation for sampl es typical in economic applications. The main problem is the location of the distribution of the t statistic rather than the shape. Once t he t statistic is adjusted so that it has the same mean and standard deviation as Student's t, the distribution of the adjusted t statisti c is accurately approximated by Student's t. Techniques are presented for mak-ing these adjustments in practice. Copyright 1988 by The Econometric Society.

This paper utilizes asymptotic expansions of the Edgeworth type to investigate alternative forms of the Wald test of nonlinear restrictions. Some formulae for the asymptotic expansion of the distribution of the Wald statistic are provided for a general case that should include most econometric applications. When specialized to the simple cases that have been studied recently in the literature, these formulae are found to explain rather well the discrepancies in sampling behavior that have been observed by other authors. It is further shown how the corrections delivered by Edgeworth expansions may be used to find transformations of the restrictions which accelerate convergence to the asymptotic distribution.

This paper studies the effects of spurious detrending in regression. The asymptotic behavior of traditional least squares estimators and tests is examined in the context of models where the generating mechanism is systematically misspecified by the presence of deterministic time trends. Most previous work on the subject has relied upon Monte Carlo studies to understand the issues involved in detrending data that are generated by integrated processes and our analytical results help to shed light on many of the simulation findings. Standard F tests and Hausman tests are shown to inadequately discriminate between the competing hypotheses. Durbin-Watson statistics, on the other hand, are shown to be valuable measures of series stationarity. The asymptotic properties of regressions and excess volatility tests with detrended integrated time series are also explored.