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Estimation of dynamic linear models in short panels with ordinal observation

Author

Listed:
  • Stephen Pudney

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

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.

Suggested Citation

  • Stephen Pudney, 2005. "Estimation of dynamic linear models in short panels with ordinal observation," CeMMAP working papers CWP05/05, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:05/05
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    File URL: http://cemmap.ifs.org.uk/wps/cwp0505.pdf
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    References listed on IDEAS

    as
    1. McFadden, Daniel & Ruud, Paul A, 1994. "Estimation by Simulation," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 591-608, November.
    2. 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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    Cited by:

    1. Stephen Pudney, 2008. "The dynamics of perception: modelling subjective wellbeing in a short panel," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(1), pages 21-40, January.

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    More about this item

    Keywords

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    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations

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