IDEAS home Printed from https://ideas.repec.org/p/wrk/warwec/1053.html
   My bibliography  Save this paper

Effects of Marginal Speci cations on Copula Estimation

Author

Listed:
  • Azam, Kazim

    (Vrije Universiteit, Amsterdam)

Abstract

This paper studies the effect of marginal distributions on a copula, in the case of mixed discrete-continuous random variables. The existing literature has proposed various methods to deal with mixed marginals: this paper is the rst to quantify their e ect in a uni ed Bayesian setting. Using order statistics based information for the marginals, as proposed by Ho (2007), we find that in small samples the bias and mean square error are at least half in size as compared to those of empirical or misspecified marginal distributions. The difference in the bias and mean square error enlarges with increasing sample size, especially for low count discrete variables. We employ the order statistics method on firm-level patents data, containing both discrete and continuous random variables, and consistently estimate their correlation. JEL classification: C11 ; C14 ; C52

Suggested Citation

  • Azam, Kazim, 2014. "Effects of Marginal Speci cations on Copula Estimation," The Warwick Economics Research Paper Series (TWERPS) 1053, University of Warwick, Department of Economics.
    • Handle: RePEc:wrk:warwec:1053
    as

    Download full text from publisher

    File URL: https://warwick.ac.uk/fac/soc/economics/research/workingpapers/2014/twerp_1053_azam.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cockburn, Iain & Griliches, Zvi, 1988. "Industry Effects and Appropriability Measures in the Stock Market's Valuation of R&D and Patents," American Economic Review, American Economic Association, vol. 78(2), pages 419-423, May.
    2. Gilroy, Bernard Michael, 1993. "Book Review: John H. Dunning Multinational Enterprises and the Global Economy," MPRA Paper 18660, University Library of Munich, Germany.
    3. Blundell, Richard & Griffith, Rachel & Windmeijer, Frank, 2002. "Individual effects and dynamics in count data models," Journal of Econometrics, Elsevier, vol. 108(1), pages 113-131, May.
    4. Eckel, Carsten & Iacovone, Leonardo & Javorcik, Beata & Neary, J. Peter, 2015. "Multi-product firms at home and away: Cost- versus quality-based competence," Journal of International Economics, Elsevier, vol. 95(2), pages 216-232.
    5. Peter Xue‐Kun Song, 2000. "Multivariate Dispersion Models Generated From Gaussian Copula," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 27(2), pages 305-320, June.
    6. Michael Pitt & David Chan & Robert Kohn, 2006. "Efficient Bayesian inference for Gaussian copula regression models," Biometrika, Biometrika Trust, vol. 93(3), pages 537-554, September.
    7. Sandner, Philipp G. & Block, Joern, 2011. "The market value of R&D, patents, and trademarks," Research Policy, Elsevier, vol. 40(7), pages 969-985, September.
    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. Azam, Kazim, 2014. "Effects of Marginal Specifcations on Copula Estimation," Economic Research Papers 270230, University of Warwick - Department of Economics.
    2. Iain M. Cockburn & Megan J. MacGarvie, 2011. "Entry and Patenting in the Software Industry," Management Science, INFORMS, vol. 57(5), pages 915-933, May.
    3. Philippe Aghion & Antoine Dechezleprêtre & David Hémous & Ralf Martin & John Van Reenen, 2016. "Carbon Taxes, Path Dependency, and Directed Technical Change: Evidence from the Auto Industry," Journal of Political Economy, University of Chicago Press, vol. 124(1), pages 1-51.
    4. Nicholas Bloom & John Van Reenen, 2002. "Patents, Real Options and Firm Performance," Economic Journal, Royal Economic Society, vol. 112(478), pages 97-116, March.
    5. Blazsek, Szabolcs & Escribano, Alvaro, 2010. "Knowledge spillovers in US patents: A dynamic patent intensity model with secret common innovation factors," Journal of Econometrics, Elsevier, vol. 159(1), pages 14-32, November.
    6. Shirong Zhao & Jeremy Losak, 2024. "Two-tiered stochastic frontier models: a Bayesian perspective," Journal of Productivity Analysis, Springer, vol. 61(2), pages 85-106, April.
    7. Zichen Ma & Shannon W. Davis & Yen‐Yi Ho, 2023. "Flexible copula model for integrating correlated multi‐omics data from single‐cell experiments," Biometrics, The International Biometric Society, vol. 79(2), pages 1559-1572, June.
    8. Tsionas, Mike, 2012. "Simple techniques for likelihood analysis of univariate and multivariate stable distributions: with extensions to multivariate stochastic volatility and dynamic factor models," MPRA Paper 40966, University Library of Munich, Germany, revised 20 Aug 2012.
    9. Smith, Michael Stanley & Maneesoonthorn, Worapree, 2018. "Inversion copulas from nonlinear state space models with an application to inflation forecasting," International Journal of Forecasting, Elsevier, vol. 34(3), pages 389-407.
    10. Kruse, Juergen & Wetzel, Heike, 2016. "Innovation in Clean Coal Technologies: Empirical Evidence from Firm-Level Patent Data," EWI Working Papers 2016-1, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    11. Smith, Michael S. & Kauermann, Göran, 2011. "Bicycle commuting in Melbourne during the 2000s energy crisis: A semiparametric analysis of intraday volumes," Transportation Research Part B: Methodological, Elsevier, vol. 45(10), pages 1846-1862.
    12. Smith, Michael Stanley, 2015. "Copula modelling of dependence in multivariate time series," International Journal of Forecasting, Elsevier, vol. 31(3), pages 815-833.
    13. L. L. Henn, 2022. "Limitations and performance of three approaches to Bayesian inference for Gaussian copula regression models of discrete data," Computational Statistics, Springer, vol. 37(2), pages 909-946, April.
    14. Michael S. Smith & Shaun P. Vahey, 2016. "Asymmetric Forecast Densities for U.S. Macroeconomic Variables from a Gaussian Copula Model of Cross-Sectional and Serial Dependence," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 416-434, July.
    15. Smith, Michael Stanley, 2023. "Implicit Copulas: An Overview," Econometrics and Statistics, Elsevier, vol. 28(C), pages 81-104.
    16. Arrighetti, Alessandro & Landini, Fabio & Lasagni, Andrea, 2014. "Intangible assets and firm heterogeneity: Evidence from Italy," Research Policy, Elsevier, vol. 43(1), pages 202-213.
    17. Peter J. Danaher & Michael S. Smith, 2011. "Modeling Multivariate Distributions Using Copulas: Applications in Marketing," Marketing Science, INFORMS, vol. 30(1), pages 4-21, 01-02.
    18. repec:hal:journl:peer-00732533 is not listed on IDEAS
    19. Nadja Klein & Michael Stanley Smith & David J. Nott, 2020. "Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices," Papers 2010.01844, arXiv.org, revised May 2021.
    20. Roy Cerqueti & Francesco Cesarone & Maria C. Heusch & Carlo D. Mottura, 2024. "A new family of modified Gaussian copulas for market consistent valuation of government guarantees," Review of Managerial Science, Springer, vol. 18(7), pages 1985-2005, July.
    21. Michael Stanley Smith, 2021. "Implicit Copulas: An Overview," Papers 2109.04718, arXiv.org.

    More about this item

    Keywords

    Bayesian copula ; discrete data ; order statistics ; semi-parametriccreation-date: 2014;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

    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:wrk:warwec:1053. 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: Margaret Nash (email available below). General contact details of provider: https://edirc.repec.org/data/dewaruk.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.