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Titlebook: Statistical Analysis and Forecasting of Economic Structural Change; Peter Hackl Book 1989 Springer-Verlag Berlin Heidelberg 1989 data anal

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书目名称Statistical Analysis and Forecasting of Economic Structural Change
编辑Peter Hackl
视频videohttp://file.papertrans.cn/877/876321/876321.mp4
图书封面Titlebook: Statistical Analysis and Forecasting of Economic Structural Change;  Peter Hackl Book 1989 Springer-Verlag Berlin Heidelberg 1989 data anal
描述In 1984, the University of Bonn (FRG) and the International Institute for Applied System Analysis (IIASA) in Laxenburg (Austria), created a joint research group to analyze the relationship between economic growth and structural change. The research team was to examine the commodity composition as well as the size and direction of commodity and credit flows among countries and regions. Krelle (1988) reports on the results of this "Bonn-IIASA" research project. At the same time, an informal IIASA Working Group was initiated to deal with prob­ lems of the statistical analysis of economic data in the context of structural change: What tools do we have to identify nonconstancy of model parameters? What type of models are particularly applicable to nonconstant structure? How is forecasting affected by the presence of nonconstant structure? What problems should be anticipated in applying these tools and models? Some 50 experts, mainly statisticians or econometricians from about 15 countries, came together in Lodz, Poland (May 1985); Berlin, GDR (June 1986); and Sulejov, Poland (September 1986) to present and discuss their findings. This volume contains a selected set of those conference c
出版日期Book 1989
关键词data analysis; econometrics; economics; efficiency; forecasting; latent variables; macroeconomics; modeling
版次1
doihttps://doi.org/10.1007/978-3-662-02571-0
isbn_softcover978-3-662-02573-4
isbn_ebook978-3-662-02571-0
copyrightSpringer-Verlag Berlin Heidelberg 1989
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978-3-662-02573-4Springer-Verlag Berlin Heidelberg 1989
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https://doi.org/10.1007/978-3-662-02571-0data analysis; econometrics; economics; efficiency; forecasting; latent variables; macroeconomics; modeling
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The Robustness of the Chow Test to Autocorrelation among DisturbancesThe chapter considers the rejection probability of the Chow test when there is unaccounted for autocorrelation among the disturbances in a linear regression model. The Chow test proved to be extremely nonrobust to autocorrelation. Its true size can even be one for the special case of an AR(1) disturbance process.
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s explained step by step.With over 640 color illustrations aFrom neurophysiological basics to diagnostic, different techniques and PNF patterns - with this practice and textbook you will learn step by step about all facets of Proprioceptive Neuromuscular Facilitation and become an expert in this est
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