Publication type
Research Paper
Series Number
CWP05/05
Series
CEMMAP Working Papers
Author
Publication date
June 1, 2005
Abstract:
We develop a simulated ML method for short-panel estimation of one or more dynamic linear equations, where the dependent variables are only partially observed through ordinal scales. We argue that this latent autoregression (LAR) model is often more appropriate than the usual state-dependence (SD) probit model for attitudinal and interval variables. We propose a score test for assisting in the treatment of initial conditions and a new simulation approach to calculate the required partial derivative matrices. An illustrative application to a model of households’ perceptions of their financial well-being demonstrates the superior fit of the LAR model.
DOI
http://dx.doi.org/10.1920/wp.cem.2005.0505
Subject
Link
- http://www.ifs.org.uk/publications/3375
Notes
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working paper
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