IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this paper

Estimating SUR Tobit Model while errors are gaussian scale mixtures: with an application to high frequency financial data

  • Qian, Hang

This 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.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: https://mpra.ub.uni-muenchen.de/31509/1/MPRA_paper_31509.pdf
File Function: original version
Download Restriction: no

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 31509.

as
in new window

Length:
Date of creation: Jul 2009
Date of revision:
Handle: RePEc:pra:mprapa:31509
Contact details of provider: Postal:
Ludwigstra├če 33, D-80539 Munich, Germany

Phone: +49-(0)89-2180-2459
Fax: +49-(0)89-2180-992459
Web page: https://mpra.ub.uni-muenchen.de

More information through EDIRC

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as in new window
  1. Wales, T. J. & Woodland, A. D., 1983. "Estimation of consumer demand systems with binding non-negativity constraints," Journal of Econometrics, Elsevier, vol. 21(3), pages 263-285, April.
  2. Wendelin Schnedler, 2005. "Likelihood Estimation for Censored Random Vectors," Econometric Reviews, Taylor & Francis Journals, vol. 24(2), pages 195-217.
  3. Shew-Jiuan Su & Steven Yen, 2000. "A censored system of cigarette and alcohol consumption," Applied Economics, Taylor & Francis Journals, vol. 32(6), pages 729-737.
  4. Chihwa Kao & Lung-fei Lee & Mark M. Pitt, 2000. "Simulated Maximum Likelihood Estimation of the Linear Expenditure System with Binding Non-Negativity Constraints," CEMA Working Papers 50, China Economics and Management Academy, Central University of Finance and Economics, revised Apr 2001.
  5. Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1770, 08.
  6. Arslan, Olcay, 2004. "Family of multivariate generalized t distributions," Journal of Multivariate Analysis, Elsevier, vol. 89(2), pages 329-337, May.
  7. J. Scott Shonkwiler & Steven T. Yen, 1999. "Two-Step Estimation of a Censored System of Equations," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(4), pages 972-982.
  8. Amemiya, Takeshi, 1973. "Regression Analysis when the Dependent Variable is Truncated Normal," Econometrica, Econometric Society, vol. 41(6), pages 997-1016, November.
  9. Chib, Siddhartha & Greenberg, Edward, 1995. "Hierarchical analysis of SUR models with extensions to correlated serial errors and time-varying parameter models," Journal of Econometrics, Elsevier, vol. 68(2), pages 339-360, August.
  10. Huang, Cliff J & Sloan, Frank A & Adamache, Killard W, 1987. "Estimation of Seemingly Unrelated Tobit Regressions via the EM Algorithm," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(3), pages 425-30, July.
  11. Lee, Lung-Fei & Pitt, Mark M, 1986. "Microeconometric Demand Systems with Binding Nonnegativity Constraints: The Dual Approach," Econometrica, Econometric Society, vol. 54(5), pages 1237-42, September.
  12. Huang, Ho-Chuan (River), 1999. "Estimation of the SUR Tobit model via the MCECM algorithm," Economics Letters, Elsevier, vol. 64(1), pages 25-30, July.
  13. Ho-Chuan Huang, 2001. "Bayesian analysis of the SUR Tobit model," Applied Economics Letters, Taylor & Francis Journals, vol. 8(9), pages 617-622.
  14. Butler, J S & Moffitt, Robert, 1982. "A Computationally Efficient Quadrature Procedure for the One-Factor Multinomial Probit Model," Econometrica, Econometric Society, vol. 50(3), pages 761-64, May.
  15. Praetz, Peter D, 1972. "The Distribution of Share Price Changes," The Journal of Business, University of Chicago Press, vol. 45(1), pages 49-55, January.
  16. Jorge Cornick & Thomas L. Cox & Brian W. Gould, 1994. "Fluid Milk Purchases: A Multivariate Tobit Analysis," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 76(1), pages 74-82.
  17. Amemiya, Takeshi, 1974. "Multivariate Regression and Simultaneous Equation Models when the Dependent Variables Are Truncated Normal," Econometrica, Econometric Society, vol. 42(6), pages 999-1012, November.
  18. Mark M. Pitt & Daniel L. Millimet, 1999. "Estimation of Coherent Demand Systems with Many Binding Non-Negativity Constraints," Working Papers 99-4, Brown University, Department of Economics.
  19. Borsch-Supan, Axel & Hajivassiliou, Vassilis A., 1993. "Smooth unbiased multivariate probability simulators for maximum likelihood estimation of limited dependent variable models," Journal of Econometrics, Elsevier, vol. 58(3), pages 347-368, August.
  20. Steven T. Yen & Biing-Hwan Lin, 2008. "Quasi-maximum likelihood estimation of a censored equation system with a copula approach: meat consumption by U.S. individuals," Agricultural Economics, International Association of Agricultural Economists, vol. 39(2), pages 207-217, 09.
  21. Carlos Arias & THOMAS L. COX, 1998. "Estimation of a U.S. Dairy Sector Model by Maximum Simulated Likelihood," Wisconsin-Madison Agricultural and Applied Economics Staff Papers 417, Wisconsin-Madison Agricultural and Applied Economics Department.
  22. Keane, Michael P, 1994. "A Computationally Practical Simulation Estimator for Panel Data," Econometrica, Econometric Society, vol. 62(1), pages 95-116, January.
  23. Nobile, Agostino, 2000. "Comment: Bayesian multinomial probit models with a normalization constraint," Journal of Econometrics, Elsevier, vol. 99(2), pages 335-345, December.
  24. Vassilis A. Hajivassiliou & Daniel L. McFadden & Paul Ruud, 1993. "Simulation of Multivariate Normal Rectangle Probabilities and their Derivatives: Theoretical and Computational Results," Working Papers _024, Yale University.
  25. Brock, W. & Lakonishok, J. & Lebaron, B., 1991. "Simple Technical Trading Rules And The Stochastic Properties Of Stock Returns," Working papers 90-22, Wisconsin Madison - Social Systems.
  26. Taylor, Mark P. & Allen, Helen, 1992. "The use of technical analysis in the foreign exchange market," Journal of International Money and Finance, Elsevier, vol. 11(3), pages 304-314, June.
  27. Heien, Dale & Wessells, Cathy Roheim, 1990. "Demand Systems Estimation with Microdata: A Censored Regression Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(3), pages 365-71, July.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:31509. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Joachim Winter)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.