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Some Generalized Mixture Distributions with an Application to Unemployment Duration

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  • McDonald, James B
  • Butler, Richard J

Abstract

Compounding or mixture distributions provide a rich class of models for applications ranging from models of heterogeniety, measurement error, distribution of stock returns and income to models of unemployment duration. Some very general mixtures are considered which include many new mixture models and also provide a unified method of organizing and comparing previously considered models as well as a test of heterogeneity. These models are used to analyze CPS unemployment duration data. A heterogeniety interpretation of the mixture models explains the discrepancy between implications of search theory and patterns observed in aggregate unemployment data. Copyright 1987 by MIT Press.

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  • McDonald, James B & Butler, Richard J, 1987. "Some Generalized Mixture Distributions with an Application to Unemployment Duration," The Review of Economics and Statistics, MIT Press, vol. 69(2), pages 232-240, May.
    • Handle: RePEc:tpr:restat:v:69:y:1987:i:2:p:232-40
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    Cited by:

    1. Arslan, Olcay, 2004. "Family of multivariate generalized t distributions," Journal of Multivariate Analysis, Elsevier, vol. 89(2), pages 329-337, May.
    2. Gould, Brian W., 1996. "Consumer Promotion And Purchase Timing: The Case Of Cheese," Staff Papers 12664, University of Wisconsin-Madison, Department of Agricultural and Applied Economics.
    3. Pope, Rulon, 1990. "Aggregation, Heterogeneity and Risk: Adding Apples and Oranges, or Apples and Apples," 1990 Quantifying Long Run Agricultural Risks and Evaluating Farmer Responses to Risk Meeting, January 28-31, 1990, Sanibel Island, Florida 271538, Regional Research Projects > S-232: Quantifying Long Run Agricultural Risks and Evaluating Farmer Responses to Risk.
    4. Brian GOULD, 1996. "Consumer Promotion And Purchase Timing: The Case Of Cheese," Staff Papers 396, University of Wisconsin Madison, AAE.
    5. Peng Shi & Wei Zhang, 2011. "A copula regression model for estimating firm efficiency in the insurance industry," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(10), pages 2271-2287.
    6. Dong, A.X.D. & Chan, J.S.K., 2013. "Bayesian analysis of loss reserving using dynamic models with generalized beta distribution," Insurance: Mathematics and Economics, Elsevier, vol. 53(2), pages 355-365.
    7. Sarabia, José María & Jordá, Vanesa, 2014. "Explicit expressions of the Pietra index for the generalized function for the size distribution of income," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 582-595.
    8. Monique Graf & J. Miguel Marín & Isabel Molina, 2019. "A generalized mixed model for skewed distributions applied to small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 565-597, June.
    9. Corrado Di Guilmi & Edoardo Gaffeo & Mauro Gallegati & Antonio Palestrini, 2004. "International evidence on business cycle magnitude dependence," Papers cond-mat/0401495, arXiv.org.
    10. Safari-Katesari Hadi & Zaroudi Samira, 2020. "Count copula regression model using generalized beta distribution of the second kind," Statistics in Transition New Series, Polish Statistical Association, vol. 21(2), pages 1-12, June.
    11. Callealta Barroso, Francisco Javier & García-Pérez, Carmelo & Prieto-Alaiz, Mercedes, 2020. "Modelling income distribution using the log Student’s t distribution: New evidence for European Union countries," Economic Modelling, Elsevier, vol. 89(C), pages 512-522.
    12. Broda, Simon A. & Krause, Jochen & Paolella, Marc S., 2018. "Approximating expected shortfall for heavy-tailed distributions," Econometrics and Statistics, Elsevier, vol. 8(C), pages 184-203.
    13. Ivana Malá, 2013. "The Use of Finite Mixtures of Probability Distributions for Modelling the Distribution of the Duration of Unemployment in the Czech Republic [Použití konečných směsí pravděpodobnostních rozdělení p," Acta Oeconomica Pragensia, Prague University of Economics and Business, vol. 2013(5), pages 47-63.
    14. Punzo, Antonio & Bagnato, Luca & Maruotti, Antonello, 2018. "Compound unimodal distributions for insurance losses," Insurance: Mathematics and Economics, Elsevier, vol. 81(C), pages 95-107.
    15. Fischer, Matthias J., 2000. "The folded EGB2 distribution and its application to financial return data," Discussion Papers 32/2000, Friedrich-Alexander University Erlangen-Nuremberg, Chair of Statistics and Econometrics.
    16. Andrew M. Jones & James Lomas & Nigel Rice, 2014. "Applying Beta‐Type Size Distributions To Healthcare Cost Regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(4), pages 649-670, June.
    17. Christofides, Louis N & McKenna, C J, 1996. "Unemployment Insurance and Job Duration in Canada," Journal of Labor Economics, University of Chicago Press, vol. 14(2), pages 286-312, April.
    18. Cummins, J. David & McDonald, James B. & Merrill, Craig, 2007. "Risky Loss Distributions and Modeling the Loss Reserve Pay-out Tail," Review of Applied Economics, Lincoln University, Department of Financial and Business Systems, vol. 3(1-2), pages 1-23.
    19. Brian W. Gould, 1996. "Consumer Promotion and Purchase Timing: The Case of Cheese," Wisconsin-Madison Agricultural and Applied Economics Staff Papers 396, Wisconsin-Madison Agricultural and Applied Economics Department.
    20. Singh, Vijay P., 2018. "Systems of frequency distributions for water and environmental engineering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 50-74.
    21. Higbee, Joshua D. & Jensen, Jonathan E. & McDonald, James B., 2019. "The asymmetric log-Laplace distribution as a limiting case of the generalized beta distribution," Statistics & Probability Letters, Elsevier, vol. 151(C), pages 73-78.
    22. Szwed, P. & Dorp, J. Rene van & Merrick, J.R.W. & Mazzuchi, T.A. & Singh, A., 2006. "A Bayesian paired comparison approach for relative accident probability assessment with covariate information," European Journal of Operational Research, Elsevier, vol. 169(1), pages 157-177, February.
    23. Shi, Peng & Valdez, Emiliano A., 2011. "A copula approach to test asymmetric information with applications to predictive modeling," Insurance: Mathematics and Economics, Elsevier, vol. 49(2), pages 226-239, September.
    24. José María Sarabia & Vanesa Jordá & Faustino Prieto & Montserrat Guillén, 2020. "Multivariate Classes of GB2 Distributions with Applications," Mathematics, MDPI, vol. 9(1), pages 1-21, December.
    25. Hadi Safari-Katesari & Samira Zaroudi, 2020. "Count copula regression model using generalized beta distribution of the second kind," Statistics in Transition New Series, Polish Statistical Association, vol. 21(2), pages 1-12, June.

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