Abstract
This chapter is devoted to the presentation of an estimation technique when all variables arranged in meaningful relations are ordinal. The model that we consider may be stated in a very compact way as follows. Let ɳ’ = (ɳ1, ɳ2, …, ɳm) be a random vector of latent variables, ζ’ = (ζ1, ζ2, …, ζp) a random vector of residuals (errors-in-equations, random disturbances terms), B a m by m non-singular matrix of regression coefficients with zeros on the main diagonal. Rather than observing ɳ directly, we assume for the moment that we have information on y*’ = (y *1 , y *2 , y *p ), a column vector of m variables which are linear functions of the m variables in ɳ. Let Γ be a p by m coefficient matrix of the regressions for the unobserved variables in ɳ. Then we consider:
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(3.1)
ɳ = B ɳ + ζ
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(3.2)
y* = Γ ɳ
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(3.3)
y = g(y*)
Keywords
- Discrete Data
- Weight Little Square
- Multivariate Normal Distribution
- Latent Variable Model
- Polychoric Correlation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 1991 Springer-Verlag Berlin Heidelberg
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Ivaldi, M. (1991). The General Latent Variable Model with Discrete Data. In: A Structural Analysis of Expectation Formation. Lecture Notes in Economics and Mathematical Systems, vol 354. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-46735-6_4
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DOI: https://doi.org/10.1007/978-3-642-46735-6_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-53665-9
Online ISBN: 978-3-642-46735-6
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