Estimation of dynamic linear models in short panels with ordinal observation

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

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

Classification and Topics

• 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

Contributors, Places and Time

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

Further information

• DOI: doi:10.1920/wp.cem.2005.0505
• Handle: http://hdl.handle.net/10419/79327
• Last update: 10.03.2025, 11:43 AM CET