IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/31509.html
   My bibliography  Save this paper

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

Author

Listed:
  • Qian, Hang

Abstract

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.

Suggested Citation

  • Qian, Hang, 2009. "Estimating SUR Tobit Model while errors are gaussian scale mixtures: with an application to high frequency financial data," MPRA Paper 31509, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:31509
    as

    Download full text from publisher

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

    References listed on IDEAS

    as
    1. Arslan, Olcay, 2004. "Family of multivariate generalized t distributions," Journal of Multivariate Analysis, Elsevier, vol. 89(2), pages 329-337, May.
    2. 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.
    3. 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.
    4. Keane, Michael P, 1994. "A Computationally Practical Simulation Estimator for Panel Data," Econometrica, Econometric Society, vol. 62(1), pages 95-116, January.
    5. 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.
    6. 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, August.
    7. Amemiya, Takeshi, 1973. "Regression Analysis when the Dependent Variable is Truncated Normal," Econometrica, Econometric Society, vol. 41(6), pages 997-1016, November.
    8. 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.
    9. 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.
    10. Carlos Arias & Thomas Cox, 2001. "Estimation of a US dairy sector model by maximum simulated likelihood," Applied Economics, Taylor & Francis Journals, vol. 33(9), pages 1201-1211.
    11. 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.
    12. 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.
    13. Lee, Lung-Fei & Pitt, Mark M, 1986. "Microeconometric Demand Systems with Binding Nonnegativity Constraints: The Dual Approach," Econometrica, Econometric Society, vol. 54(5), pages 1237-1242, September.
    14. 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.
    15. Hajivassiliou, Vassilis & McFadden, Daniel & Ruud, Paul, 1996. "Simulation of multivariate normal rectangle probabilities and their derivatives theoretical and computational results," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 85-134.
    16. 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 761-764, May.
    17. Wendelin Schnedler, 2005. "Likelihood Estimation for Censored Random Vectors," Econometric Reviews, Taylor & Francis Journals, vol. 24(2), pages 195-217.
    18. Ho-Chuan Huang, 2001. "Bayesian analysis of the SUR Tobit model," Applied Economics Letters, Taylor & Francis Journals, vol. 8(9), pages 617-622.
    19. 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 365-371, July.
    20. 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.
    21. Nobile, Agostino, 2000. "Comment: Bayesian multinomial probit models with a normalization constraint," Journal of Econometrics, Elsevier, vol. 99(2), pages 335-345, December.
    22. 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.
    23. 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, September.
    24. 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.
    25. Chihwa Kao & Lung-fei Lee & Mark M. Pitt, 2001. "Simulated Maximum Likelihood Estimation of the Linear Expenditure System with Binding Non-Negativity Constraints," Annals of Economics and Finance, Society for AEF, vol. 2(1), pages 215-235, May.
    26. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. " Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-1764, December.
    27. 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 425-430, July.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Tobit; Gaussian mixtures; Bayesian;

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. 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). General contact details of provider: http://edirc.repec.org/data/vfmunde.html .

    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 CitEc recognized a reference but did not link an item in RePEc 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 RePEc Author Service 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.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.