Tests of Joint Hypotheses for Time Series Regression with a Unit Root

This Paper Studies Tests of Joint Hypotheses in Time Series Regression with a Unit Root in Which Weakly Dependent and Heterogeneously Distributed Innovations Are Allowed. We Consider Two Types of Regression: One with a Constant and Lagged Dependent Variable, and the Other with a Trend Added. the Statistics Studied Are the Regression \"F-Test\" Originally Analysed by Dickey and Fuller (1981) in a Less General Framework. the Limiting Distributions Are Found Using Functinal Central Limit Theory. New Test Statistics Are Proposed Which Require Only Already Tabulated Critical Values But Which Are Valid in a Quite General Framework (Including Finite Order Arma Models Generated by Gaussian Errors). This Study Extends the Results on Single Coefficients Derived in Phillips (1986A) and Phillips and Perron (1986).

GLS Detrending, Efficient Unit Root Tests and Structural Change

We extend the class of M-tests for a unit root analyzed by Perron and Ng (1996) and Ng and Perron (1997) to the case where a change in the trend function is allowed to occur at an unknown time. These tests M(GLS) adopt the GLS detrending approach of Dufour and King (1991) and Elliott, Rothenberg and Stock (1996) (ERS). Following Perron (1989), we consider two models : one allowing for a change in slope and the other for both a change in intercept and slope. We derive the asymptotic distribution of the tests as well as that of the feasible point optimal tests PT(GLS) suggested by ERS. The asymptotic critical values of the tests are tabulated. Also, we compute the non-centrality parameter used for the local GLS detrending that permits the tests to have 50% asymptotic power at that value. We show that the M(GLS) and PT(GLS) tests have an asymptotic power function close to the power envelope. An extensive simulation study analyzes the size and power in finite samples under various methods to select the truncation lag for the autoregressive spectral density estimator. An empirical application is also provided.

Testing for a Unit Root in Panels with Dynamic Factors

This paper studies testing for a unit root for large n and T panels in which the cross-sectional units are correlated. To model this cross-sectional correlation, we assume that the data is generated by an unknown number of unobservable common factors. We propose unit root tests in this environment and derive their (Gaussian) asymptotic distribution under the null hypothesis of a unit root and local alternatives. We show that these tests have significant asymptotic power when the model has no incidental trends. However, when there are incidental trends in the model and it is necessary to remove heterogeneous deterministic components, we show that these tests have no power against the same local alternatives. Through Monte Carlo simulations, we provide evidence on the finite sample properties of these new tests.

Beyond Panel Unit Root Tests: Using Multiple Testing to Determine the Non Stationarity Properties of Individual Series in a Panel

Most panel unit root tests are designed to test the joint null hypothesis of a unit root for each individual series in a panel. After a rejection, it will often be of interest to identify which series can be deemed to be stationary and which series can be deemed nonstationary. Researchers will sometimes carry out this classification on the basis of n individual (univariate) unit root tests based on some ad hoc significance level. In this paper, we demonstrate how to use the false discovery rate (FDR) in evaluating I(1)=I(0) classifications based on individual unit root tests when the size of the cross section (n) and time series (T) dimensions are large. We report results from a simulation experiment and illustrate the methods on two data sets.

Étude du rôle des récepteurs NMDA du mésencéphale ventral dans la récompense induite par la stimulation électrique du mésencéphale postérieur chez le rongeur.

La voie dopaminergique mésolimbique qui prend son origine dans le mésencéphale ventral et qui projette vers des régions rostrales du système limbique fait partie du substrat nerveux qui contrôle la récompense et les comportements motivés. Il a été suggéré qu’un signal de récompense est produit lorsque le patron de décharge des neurones dopaminergiques passe d’un mode tonique à un mode phasique, une transition qui est initiée par l’action du glutamate aux récepteurs N-Méthyl-D-aspartate (NMDA). Étant donné qu’une altération du système de récompense est souvent associée à des anomalies cliniques telles que l’addiction compulsive et à des troubles émotionnels tels que l’anhédonie, nous avons étudié le rôle des récepteurs NMDA dans la récompense induite par la stimulation électrique intracérébrale. Puisque les récepteurs NMDA sont composés de sous-unités distinctes, GluN1, GluN2 et GluN3, nous avons étudié le rôle de deux sous-unités qui sont présentes dans le mésencéphale ventral : GluN2A et GluN2B. Les résultats montrent que des injections mésencéphaliques de R-CPP et de PPPA, des antagonistes préférentiels aux sous-unités GluN2A/B, ont produit une augmentation dose-dépendante de l’effet de récompense, un effet qui était, à certains temps après les injections, accompagné d’une augmentation du nombre de réponses maximales. Ces effets n’ont pas été observés après l’injection d’une large gamme de doses de Ro04-5595, un antagoniste des sous-unités GluN2B. Ces résultats suggèrent que le glutamate mésencéphalique exerce une modulation négative sur le circuit de récompense, un effet dû à son action au niveau des récepteurs NMDA composés des sous-unités GluN2A.