Estimating SUR Tobit Model while errors are gaussian scale mixtures: with an application to high frequency financial data
AbstractThis paper examines multivariate Tobit system with Scale mixture disturbances. Three estimation methods, namely Maximum Simulated Likelihood, Expectation Maximization Algorithm and Bayesian MCMC simulators, are proposed and compared via generated data experiments. The chief finding is that Bayesian approach outperforms others in terms of accuracy, speed and stability. The proposed model is also applied to a real data set and study the high frequency price and trading volume dynamics. The empirical results confirm the information contents of historical price, lending support to the usefulness of technical analysis. In addition, the scale mixture model is also extended to sample selection SUR Tobit and finite Gaussian regime mixtures.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 31509.
Date of creation: Jul 2009
Date of revision:
Tobit; Gaussian mixtures; Bayesian;
Find related papers by JEL classification:
- C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
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