Conditional and unconditional statistical independence
Conditional independence almost everywhere in the space of the conditioning variates does not imply unconditional independence, although it may well imply unconditional independence of certain functions of the variables. An example that is important in linear regression theory is discussed in detail. This involves orthogonal projections on random linear manifolds, which are conditionally independent but not unconditionally independent under normality. Necessary and sufficient conditions are obtained under which conditional independence does imply unconditional independence.
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- Peter C.B. Phillips, 1982.
"The Exact Distribution of LIML: I,"
Cowles Foundation Discussion Papers
658, Cowles Foundation for Research in Economics, Yale University.
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"Statistical Inference in Regressions with Integrated Processes: Part 2,"
Cambridge University Press, vol. 5(01), pages 95-131, April.
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- Ullah, Aman & Zinde-Walsh, Victoria, 1984. "On the Robustness of LM, LR, and W Tests in Regression Models," Econometrica, Econometric Society, vol. 52(4), pages 1055-1066, July.
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- Peter C.B. Phillips & Vassilis A. Hajivassiliou, 1987. "Bimodal t-Ratios," Cowles Foundation Discussion Papers 842, Cowles Foundation for Research in Economics, Yale University.
- Hillier, Grant H., 1985. "On the Joint and Marginal Densities of Instrumental Variable Estimators in a General Structural Equation," Econometric Theory, Cambridge University Press, vol. 1(01), pages 53-72, April.
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