Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/79327
Authors: 
Pudney, Stephen E.
Year of Publication: 
2005
Series/Report no.: 
cemmap working paper, Centre for Microdata Methods and Practice CWP05/05
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.
Subjects: 
Dynamic panel data models , ordinal variables , simulated maximum likelihood , GHK simulator , BHPS
JEL: 
C23
C25
C33
C35
D84
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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