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Bayesian Inference for the Mover-Stayer Model in Continuous Time with an Application to Labour Market Transition Data

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  • Denis Fougère

    (Crest)

  • Thierry Kamionka

    (Crest)

Abstract

This paper presents Bayesian inference procedures for the continuous time mover-stayer model applied to labour market transition data collected in discrete time. These methods allow us to derive the probability of embeddability of the discrete-time modelling with the continuous-time one. A special emphasis is put on two alternative procedures, namely the importance sampling algorithm and a new Gibbs sampling algorithm. Transition intensities, proportions of stayers and functions of these parameters are then estimated with the Gibbs sampling algorithm for individual transition data coming from the French Labour Force Surveys collected over the period 1986-2000. Copyright © 2003 John Wiley & Sons, Ltd.
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  • Denis Fougère & Thierry Kamionka, 2002. "Bayesian Inference for the Mover-Stayer Model in Continuous Time with an Application to Labour Market Transition Data," Working Papers 2002-23, Center for Research in Economics and Statistics.
    • Handle: RePEc:crs:wpaper:2002-23
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    1. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-1339, November.
    2. Neumark, David & Polsky, Daniel & Hansen, Daniel, 1999. "Has Job Stability Declined Yet? New Evidence for the 1990s," Journal of Labor Economics, University of Chicago Press, vol. 17(4), pages 29-64, October.
    3. Kiefer, Nicholas M, 1978. "Discrete Parameter Variation: Efficient Estimation of a Switching Regression Model," Econometrica, Econometric Society, vol. 46(2), pages 427-434, March.
    4. Denis Fougère & Thierry Kamionka, 1992. "Un modéle markovien du marché du travail," Annals of Economics and Statistics, GENES, issue 27, pages 149-188.
    5. Van Dijk, Herman K. & Kloek, Teun & Boender, C. Guus E., 1985. "Posterior moments computed by mixed integration," Journal of Econometrics, Elsevier, vol. 29(1-2), pages 3-18.
    6. John Geweke & Robert C. Marshall & Gary A. Zarkin, 1986. "Exact Inference for Continuous Time Markov Chain Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 53(4), pages 653-669.
    7. Thierry Kamionka, 1998. "Simulated maximum likelihood estimation in transition models," Econometrics Journal, Royal Economic Society, vol. 1(Conferenc), pages 129-153.
    8. Denis Fougère & Thierry Kamionka, 1992. "Mobilité et précarisation sur le marché français du travail : une analyse longitudinale pour les années 1986 à 1988," Économie et Prévision, Programme National Persée, vol. 102(1), pages 157-178.
    9. John Geweke, 1991. "Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments," Staff Report 148, Federal Reserve Bank of Minneapolis.
    10. Gottschalk, Peter & Moffitt, Robert, 1999. "Changes in Job Instability and Insecurity Using Monthly Survey Data," Journal of Labor Economics, University of Chicago Press, vol. 17(4), pages 91-126, October.
    11. McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240.
    12. repec:adr:anecst:y:1992:i:27:p:06 is not listed on IDEAS
    13. Gourieroux,Christian & Monfort,Alain, 1995. "Statistics and Econometric Models," Cambridge Books, Cambridge University Press, number 9780521477444, January.
    14. Kloek, Tuen & van Dijk, Herman K, 1978. "Bayesian Estimates of Equation System Parameters: An Application of Integration by Monte Carlo," Econometrica, Econometric Society, vol. 46(1), pages 1-19, January.
    15. Burton Singer & Seymour Spilerman, 1976. "Some Methodological Issues in the Analysis of Longitudinal Surveys," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 447-474, National Bureau of Economic Research, Inc.
    16. Robert E. McCulloch & Ruey S. Tsay, 1994. "Statistical Analysis Of Economic Time Series Via Markov Switching Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(5), pages 523-539, September.
    17. van Dijk, H. K. & Kloek, T., 1980. "Further experience in Bayesian analysis using Monte Carlo integration," Journal of Econometrics, Elsevier, vol. 14(3), pages 307-328, December.
    18. Gourieroux,Christian & Monfort,Alain, 1995. "Statistics and Econometric Models 2 volume set," Cambridge Books, Cambridge University Press, number 9780521478373, July.
    19. Heckman, James J. & Singer, Burton, 1984. "Econometric duration analysis," Journal of Econometrics, Elsevier, vol. 24(1-2), pages 63-132.
    20. Geweke, John & Marshall, Robert C & Zarkin, Gary A, 1986. "Mobility Indices in Continuous Time Markov Chains," Econometrica, Econometric Society, vol. 54(6), pages 1407-1423, November.
    21. Light, Audrey & Ureta, Manuelita, 1992. "Panel Estimates of Male and Female Job Turnover Behavior: Can Female Nonquitters Be Identified?," Journal of Labor Economics, University of Chicago Press, vol. 10(2), pages 156-181, April.
    22. Chib, Siddhartha, 1992. "Bayes inference in the Tobit censored regression model," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 79-99.
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    Cited by:

    1. Moshe Buchinsky & Denis Fougère & Francis Kramarz & Rusty Tchernis, 2002. "Interfirm Mobility, Wages and the Returns to Seniority and Experience in the U.S," Working Papers 2002-29, Center for Research in Economics and Statistics.
    2. Bosch, Mariano & Maloney, William, 2008. "Cyclical movements in unemployment and informality in developing countries," Policy Research Working Paper Series 4648, The World Bank.
    3. Thierry Kamionka & Cyriaque Edon, 2007. "Modélisation dynamique de la participation au marché du travail des femmes en couple," Annals of Economics and Statistics, GENES, issue 86, pages 77-108.
    4. Thierry Kamionka & Xavier VU NGOC, 2015. "Trajectoire des jeunes sur le marché du travail, quartier d’origine et diplôme : une modélisation dynamique," Working Papers 2015-01, Center for Research in Economics and Statistics.
    5. Legrand D. F. Saint-Cyr & Laurent Piet, 2017. "Movers and stayers in the farming sector: accounting for unobserved heterogeneity in structural change," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(4), pages 777-795, August.
    6. Laurent, Piet & Legrand D.F. Saint-Cyr, 2016. "Projection de la population des exploitations agricoles françaises à l’horizon 2025," Working Papers SMART 16-11, INRAE UMR SMART.
    7. Saint-Cyr, Legrand D. F. & Piet, Laurent, 2014. "Movers and Stayers in the Farming Sector: Another Look at Heterogeneity in Structural Change," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 183068, European Association of Agricultural Economists.
    8. Michael K. Ng & Yuho Chung, 2012. "Double Mover–Stayer model on customer switching in telecommunications industry," Naval Research Logistics (NRL), John Wiley & Sons, vol. 59(8), pages 663-674, December.
    9. Saint-Cyr, Legrand D. F., 2016. "Accounting for farm heterogeneity in the assessment of agricultural policy impacts on structural change," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235778, Agricultural and Applied Economics Association.

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