Illustration - Unit no. 9 File no. 2-R1-H21.4

An illustration dated 10 November 1948 of Unit No. 9. The description reads "Sketch Showing Field Notes taken before unit dismantled Nov. 1948 - Runner and shaft were hanging from the thrust bearing when these measurements were taken. The thrust bearing was 3/32" thinner than a full size bearing."

Photograph - Unit 11 Runner Rankine Station January 1959

A photograph of Unit 11 Runner at Rankine Station (5901-2105).

HOEK UNIT C (Y)

Light brown sediment with mainly small clasts. The clast shape ranges from sub-angular to sub-rounded. Lineations are abundant throughout the sample. Edge-to-edge grain crushing, as well as crushed grains, can also be seen.

HOEK UNIT C (Z) B3

Light brown sediment with clasts ranging from small to medium in size. The clast shape ranges from sub-angular to rounded. Lineations and edge-to-edge grain crushing can mainly be seen in this sample. There are also a few examples of grain stacking and crushed grains.

HOEK UNIT C 81

Light brown sediment with clast sizes ranging from small to medium. Clast shape ranges from sub-angular to sub-rounded. Lineations are common throughout the sample. Minor amounts of edge-to-edge grain crushing can also be seen.

Doit-on procéder à la réfection de l'unité 2 de la centrale thermonucléaire de Pickering A? Une application du principe de précaution.

Rapport de recherche

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.