[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/diw/diwwpp/dp584.html
   My bibliography  Save this paper

Calculation of Multivariate Normal Probabilities by Simulation, with Applications to Maximum Simulated Likelihood Estimation

Author

Listed:
  • Lorenzo Cappellari
  • Stephen P. Jenkins
Abstract
We discuss methods for calculating multivariate normal probabilities by simulation and two new Stata programs for this purpose: mvdraws for deriving draws from the standard uniform density using either Halton or pseudo-random sequences, and an egen function mvnp() for calculating the probabilities themselves. Several illustrations show how the programs may be used for maximum simulated likelihood estimation.

Suggested Citation

  • Lorenzo Cappellari & Stephen P. Jenkins, 2006. "Calculation of Multivariate Normal Probabilities by Simulation, with Applications to Maximum Simulated Likelihood Estimation," Discussion Papers of DIW Berlin 584, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwwpp:dp584
    as

    Download full text from publisher

    File URL: https://www.diw.de/documents/publikationen/73/diw_01.c.44288.de/dp584.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Lorenzo Cappellari & Stephen P. Jenkins, 2004. "Modelling low income transitions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(5), pages 593-610.
    2. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555.
    3. Stephane Hess & John Polak, 2003. "An alternative method to the scrambled Halton sequence for removing correlation between standard Halton sequences in high dimensions," ERSA conference papers ersa03p406, European Regional Science Association.
    4. William W. Gould & Jeffrey Pitblado & Brian Poi, 2010. "Maximum Likelihood Estimation with Stata," Stata Press books, StataCorp LP, edition 4, number ml4, March.
    5. Steven Stern, 1997. "Simulation-Based Estimation," Journal of Economic Literature, American Economic Association, vol. 35(4), pages 2006-2039, December.
    6. Peter Haan & Arne Uhlendorff, 2006. "Estimation of multinomial logit models with unobserved heterogeneity using maximum simulated likelihood," Stata Journal, StataCorp LP, vol. 6(2), pages 229-245, June.
    7. Stephen P. Jenkins & Lorenzo Cappellari & Peter Lynn & Annette Jäckle & Emanuela Sala, 2006. "Patterns of consent: evidence from a general household survey," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 701-722, October.
    8. Sandor, Zsolt & Andras, P.Peter, 2004. "Alternative sampling methods for estimating multivariate normal probabilities," Journal of Econometrics, Elsevier, vol. 120(2), pages 207-234, June.
    9. Lorenzo Cappellari & Stephen P. Jenkins, 2003. "Multivariate probit regression using simulated maximum likelihood," Stata Journal, StataCorp LP, vol. 3(3), pages 278-294, September.
    10. Mark Stewart, 2006. "Maximum simulated likelihood estimation of random-effects dynamic probit models with autocorrelated errors," Stata Journal, StataCorp LP, vol. 6(2), pages 256-272, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sara Ayllón, 2013. "Understanding poverty persistence in Spain," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 4(2), pages 201-233, June.
    2. Peter Haan & Arne Uhlendorff, 2006. "Estimation of multinomial logit models with unobserved heterogeneity using maximum simulated likelihood," Stata Journal, StataCorp LP, vol. 6(2), pages 229-245, June.
    3. Kanabar, Ricky, 2017. "In or out? Poverty dynamics among older individuals in the UK," Journal of Pension Economics and Finance, Cambridge University Press, vol. 16(4), pages 509-553, October.
    4. Dang,Hai-Anh H. & King,Elizabeth M. & Dang,Hai-Anh H. & King,Elizabeth M., 2013. "Incentives and teacher effort : further evidence from a developing country," Policy Research Working Paper Series 6694, The World Bank.
    5. John Geweke & Joel Horowitz & M. Hashem Pesaran, 2006. "Econometrics: A Bird’s Eye View," CESifo Working Paper Series 1870, CESifo.
    6. Maruyama, Shiko, 2014. "Estimation of finite sequential games," Journal of Econometrics, Elsevier, vol. 178(2), pages 716-726.
    7. Ha Trong Nguyen & Luke B. Connelly, 2017. "The Dynamics of Informal Care Provision in an Australian Household Panel Survey: Previous Work Characteristics and Future Care Provision," The Economic Record, The Economic Society of Australia, vol. 93(302), pages 395-419, September.
    8. Stephane Hess & John W. Polak, 2004. "An analysis of parking behaviour using discrete choice models calibrated on SP datasets," ERSA conference papers ersa04p60, European Regional Science Association.
    9. Andersen, Steffen & Harrison, Glenn W. & Lau, Morten Igel & Rutström, Elisabet E., 2010. "Behavioral econometrics for psychologists," Journal of Economic Psychology, Elsevier, vol. 31(4), pages 553-576, August.
    10. Hoque, Mohammad Mainul & Artz, Georgeanne M. & Jarboe, Darren H. & Martens, Bobby J., 2015. "Producer Participation In Biomass Markets: Farm Factors, Market Factors, And Correlated Choices," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 47(3), pages 317-344, August.
    11. Richard Gates, 2006. "A Mata Geweke–Hajivassiliou–Keane multivariate normal simulator," Stata Journal, StataCorp LP, vol. 6(2), pages 190-213, June.
    12. Christelis, Dimitris & Georgarakos, Dimitris & Haliassos, Michael, 2009. "Stockholding: From participation to location and to participation spillovers," CFS Working Paper Series 2009/02, Center for Financial Studies (CFS).
    13. Enrico Fabrizi & Chiara Mussida, 2018. "Assessing poverty persistence in households with dependent children: the role of poverty measurement," DISCE - Quaderni del Dipartimento di Scienze Economiche e Sociali dises1839, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    14. Moura, Guilherme V. & Richard, Jean-François & Liesenfeld, Roman, 2007. "Dynamic Panel Probit Models for Current Account Reversals and their Efficient Estimation," Economics Working Papers 2007-11, Christian-Albrechts-University of Kiel, Department of Economics.
    15. Christelis, Dimitris & Georgarakos, Dimitris, 2013. "Investing at home and abroad: Different costs, different people?," Journal of Banking & Finance, Elsevier, vol. 37(6), pages 2069-2086.
    16. Campbell, Danny & Hutchinson, W. George & Scarpa, Riccardo, 2006. "Lexicographic Preferences in Discrete Choice Experiments: Consequences on Individual-Specific Willingness to Pay Estimates," Sustainability Indicators and Environmental Valuation Working Papers 12224, Fondazione Eni Enrico Mattei (FEEM).
    17. Jones, Stewart & Hensher, David A., 2007. "Modelling corporate failure: A multinomial nested logit analysis for unordered outcomes," The British Accounting Review, Elsevier, vol. 39(1), pages 89-107.
    18. Elena Giarda & Gloria Moroni, 2018. "The Degree of Poverty Persistence and the Role of Regional Disparities in Italy in Comparison with France, Spain and the UK," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 136(1), pages 163-202, February.
    19. Aditya R. Khanal & Ashok K. Mishra & Omobolaji Omobitan, 2019. "Examining organic, agritourism, and agri-environmental diversification decisions of American farms: are these decisions interlinked?," Review of Agricultural, Food and Environmental Studies, Springer, vol. 100(1), pages 27-45, December.
    20. Lucchetti, Riccardo & Pigini, Claudia, 2017. "DPB: Dynamic Panel Binary Data Models in gretl," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i08).

    More about this item

    Keywords

    Simulation estimation; maximum simulated likelihood; multivariate probit; Halton sequences; pseudo-random sequences; multivariate normal; GHK simulator;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:diw:diwwpp:dp584. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Bibliothek (email available below). General contact details of provider: https://edirc.repec.org/data/diwbede.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.