A simple dynamic model for limited dependent variables
AbstractA dynamic model for limited dependent variables is proposed, which estimation does not rely on simulation methods. A latent conditional mean function which is measureable with respect to past and observable information circumvents the solution of a T-dimensional integral and yields a simple and computationally parsimonious maximum likelihood estimation. It can be shown that the latent process implied by the limited dependent autoregressive moving average model is covariance stationary. Parameter estimates of this model are shown to be consistent but inefficient estimates of the parameters of a standard latent autoregressive moving average model, for which a maximum likelihood estimator is computationally burdensome. Monte Carlo evidence is provided to assess parameter estimates based on the limited dependent ARMA given the data generation process is a standard latent ARMA. The results indicate that the asymptotic properties hold quite nicely in small samples. An application based on IBM transaction price changes from the NASDAQ demonstrates a potential use of the model suggested here.
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Bibliographic InfoPaper provided by Economics Group, Nuffield College, University of Oxford in its series Economics Papers with number 2001-W11.
Length: 24 pages
Date of creation: 16 Jul 2001
Date of revision:
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Web page: http://www.nuff.ox.ac.uk/economics/
limited dependent variables; quantal response models; latent dynamic; ARMA process; generalised error;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2001-10-16 (All new papers)
- NEP-DCM-2001-10-16 (Discrete Choice Models)
- NEP-ECM-2001-10-16 (Econometrics)
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