Arbeitspapier

Estimation of dynamic linear models in short panels with ordinal observation

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.

Language
Englisch

Bibliographic citation
Series: cemmap working paper ; No. CWP05/05

Classification
Wirtschaft
Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
Single Equation Models; Single Variables: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models
Multiple or Simultaneous Equation Models: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
Expectations; Speculations
Subject
Dynamic panel data models , ordinal variables , simulated maximum likelihood , GHK simulator , BHPS
Dynamisches Modell
Mathematische Optimierung
Panel
Maximum-Likelihood-Methode

Event
Geistige Schöpfung
(who)
Pudney, Stephen E.
Event
Veröffentlichung
(who)
Centre for Microdata Methods and Practice (cemmap)
(where)
London
(when)
2005

DOI
doi:10.1920/wp.cem.2005.0505
Handle
Last update
10.03.2025, 11:43 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Pudney, Stephen E.
  • Centre for Microdata Methods and Practice (cemmap)

Time of origin

  • 2005

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