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The Unscientific Incompleteness and Bias of Unidirectional Projections (= Regressions): A Questionnaire


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  • Cornelis A Los


Why do statisticians (econometricians, economists, financial analysts, etc.) continue to incompletely identify the algebraic/geometric structure of the multi-variate data series they profess to analyze, and instead continue to publish the results of incomplete, prejudiced and biased unidirectional projections (= 'regressions') of such covariance structures? Such incomplete, prejudiced and biased representations cannot lead to scientific knowledge, as has been demonstrated already more than twenty years ago.

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Bibliographic Info

Paper provided by EconWPA in its series Econometrics with number 0410011.

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Length: 6 pages
Date of creation: 26 Oct 2004
Date of revision:
Handle: RePEc:wpa:wuwpem:0410011

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Keywords: system identification; noisy data; regression analysis; projection; incompleteness; prejudice; bias;

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  1. Cornelis A. Los, 1987. "Identification of a linear system from inexact data: a three variable example," Research Paper 8703, Federal Reserve Bank of New York.
  2. Cornelis A. Los, 2004. "Galton's Error and the Under-Representation of Systematic Risk," Finance 0409041, EconWPA.
  3. R.E. Kalman & C.A. Los, 1987. "The prejudices of least squares, principal components and common factor schemes," Research Paper 8701, Federal Reserve Bank of New York.
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Cited by:
  1. Cornelis Los, 2004. "Measuring the Degree of Efficiency of Financial Market," Finance 0411003, EconWPA.


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