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Type: cemmap Working Papers Authors: Stephen Pudney ISSN: 1753-9196
Volume, issue, pages: 25 pp.
JEL classification: C23, C25, C33, C35, D84 Keywords: Dynamic panel data models, ordinal variables, simulated maximum likelihood, GHK simulator, BHPS
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. Search |
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