Titlebook: Co-trending: A Statistical System Analysis of Economic Trends; Michio Hatanaka,Hiroshi Yamada Book 2003 Springer-Verlag Tokyo 2003 Co-brea

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Technology Policy: Options and Priorities,ta covariance matrix. . is also associated with the principal component that corresponds to the .-th eigenvalue. Given ., and ., the principal components with . = 1, ..., ., with . + 1, ..., . ≡ ., and with . + 1, ..., . each reveal asymptotically distinctive features. Thus . = 1, ..., . has been di

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David C. Mowery,W. Edward Steinmuellermacroeco-nomic studies involving about 100 to 200 quarterly observations. The present chapter presents our simulation studies for the case where . = 2, . = 2, . = 100 or 200, and . = [. (.)] with . = 0.5. Section 8.1 will describe seven DGPs that incorporate a variety of structures of deterministic

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978-4-431-65914-3Springer-Verlag Tokyo 2003

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Introduction,The rest of this section consists of an expository explanation of co-trending and presentation of highlights of the authors’ results. In Section 1.2 the literature on the relations among trends will be reviewed. We have explored a number of different approaches before settling on the one in this boo

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Co-trending,Direct modelling is divided into the parametric approach and the non-parametric approach. The parametric approach is based on a list of parametrically specified functions of time, which will be called trend functions, while the non-parametric approach does not use such functions. We begin with the n

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Statistics from the Data Covariance Matrix,(i) the eigenvalue itself, (ii) the eigenvector associated with it, and (iii) the principal component associated with it form one set of statistics. Altogether . sets of statistics are available, and they are ordered in descending order in terms of the eigenvalues. In Section 3.1 below, the relation

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Unit Root Tests,nd the multivariate unit root tests are considered. The univariate unit root test, which is the well known method in Perron (1989), is expected to be applied to each individual principal component in Group ⊥, one principal component in Group 2 when . = 1, and each individual component in Group 1. Th

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