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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Principal Components,3, and 3.4 are derived from Assumption 2.2 and Definition 2.2. Definition 2.2 specifies the conditions to be met by the elementary trend functions, and it will be considered as a part of Assumption 2.2. Thus our basic assumptions in the parametric approach are Assumptions 2.2 and 3.1.

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Michio Hatanaka,Hiroshi YamadaA research monograph on the time series econometric methods that are applied to macro-economic studies.It builds upon the results on cointegrated VAR by Professors Granger and Johansen, but turns its

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Science Policy Options and Priorities,The following two major points, (a) and (b), have emerged in the previous chapter.

Enthralling

Trend Tests,The following two major points, (a) and (b), have emerged in the previous chapter.

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https://doi.org/10.1007/978-3-030-47166-8The 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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Nizam U. Ahmed,Richard M. TrethowanDirect 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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Science Parks and Place-based Innovation,(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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https://doi.org/10.1007/978-3-030-30963-3proach using Assumption 2.2 with some elementary trend functions. The purpose is to make the DGP of principal components sufficiently specific so that statistical tests can be applied to the principal components. Propositions 3.1 through 3.3 of the previous chapter have been derived from Assumptions

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