Estimation of dynamic linear models in short panels with ordinal observation

Stephen Pudney

No CWP05/05, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

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.

Keywords: Dynamic panel data models; ordinal variables; simulated maximum likelihood; GHK simulator; BHPS (search for similar items in EconPapers)
JEL-codes: C23 C25 C33 C35 D84 (search for similar items in EconPapers)
Pages: 25 pp.
Date: 2005-06-06
New Economics Papers: this item is included in nep-dcm, nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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