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Estimation of dynamic linear models in short panels with ordinal observation

  • Stephen Pudney

    ()

    (Institute for Fiscal Studies and Institute for Social and Economic Research)

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.

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File URL: http://cemmap.ifs.org.uk/wps/cwp0505.pdf
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Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number CWP05/05.

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Length: 25 pp.
Date of creation: Jun 2005
Date of revision:
Handle: RePEc:ifs:cemmap:05/05
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  1. Wooldridge, Jeffrey M., 2000. "A framework for estimating dynamic, unobserved effects panel data models with possible feedback to future explanatory variables," Economics Letters, Elsevier, vol. 68(3), pages 245-250, September.
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