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Sequential numerical integration in nonlinear state space models for microeconometric panel data

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  • Florian Heiss

    (University of Munich, Department of Economics, Ludwigstr. 28 RG, 80539 Munich, Germany)

Abstract

This paper discusses the estimation of a class of nonlinear state space models including nonlinear panel data models with autoregressive error components. A health economics example illustrates the usefulness of such models. For the approximation of the likelihood function, nonlinear filtering algorithms developed in the time-series literature are considered. Because of the relatively simple structure of these models, a straightforward algorithm based on sequential Gaussian quadrature is suggested. It performs very well both in the empirical application and a Monte Carlo study for ordered logit and binary probit models with an AR(1) error component. Copyright © 2008 John Wiley & Sons, Ltd.

Suggested Citation

  • Florian Heiss, 2008. "Sequential numerical integration in nonlinear state space models for microeconometric panel data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(3), pages 373-389.
  • Handle: RePEc:jae:japmet:v:23:y:2008:i:3:p:373-389
    DOI: 10.1002/jae.993
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    File URL: http://qed.econ.queensu.ca:80/jae/2008-v23.3/
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    References listed on IDEAS

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    Cited by:

    1. Giarda, Elena, 2013. "Persistency of financial distress amongst Italian households: Evidence from dynamic models for binary panel data," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3425-3434.
    2. Francesco, Bartolucci & Silvia, Bacci & Claudia, Pigini, 2015. "A misspecification test for finite-mixture logistic models for clustered binary and ordered responses," MPRA Paper 64220, University Library of Munich, Germany.
    3. Mesters, G. & Koopman, S.J., 2014. "Generalized dynamic panel data models with random effects for cross-section and time," Journal of Econometrics, Elsevier, vol. 180(2), pages 127-140.
    4. Xia, Ye-Mao & Tang, Nian-Sheng & Gou, Jian-Wei, 2016. "Generalized linear latent models for multivariate longitudinal measurements mixed with hidden Markov models," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 259-275.
    5. Rodriguez, Cesar M., 2016. "Economic and political determinants of exchange rate regimes: The case of Latin America," International Economics, Elsevier, vol. 147(C), pages 1-26.
    6. Silvia Cagnone & Francesco Bartolucci, 2017. "Adaptive Quadrature for Maximum Likelihood Estimation of a Class of Dynamic Latent Variable Models," Computational Economics, Springer;Society for Computational Economics, vol. 49(4), pages 599-622, April.
    7. Bartolucci, Francesco & Lupparelli, Monia, 2012. "Nested hidden Markov chains for modeling dynamic unobserved heterogeneity in multilevel longitudinal data," MPRA Paper 40588, University Library of Munich, Germany.
    8. Grigorios Emvalomatis, 2012. "Adjustment and unobserved heterogeneity in dynamic stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 37(1), pages 7-16, February.
    9. Bartolucci, Francesco & Belotti, Federico & Peracchi, Franco, 2015. "Testing for time-invariant unobserved heterogeneity in generalized linear models for panel data," Journal of Econometrics, Elsevier, vol. 184(1), pages 111-123.
    10. Florian Heiss & Daniel McFadden & Joachim Winter, 2009. "Regulation of private health insurance markets: Lessons from enrollment, plan type choice, and adverse selection in Medicare Part D," NBER Working Papers 15392, National Bureau of Economic Research, Inc.
    11. Siem Jan Koopman & André Lucas & Marcel Scharth, 2015. "Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State-Space Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 114-127, January.
    12. Francesco Bartolucci & Silvia Bacci & Fulvia Pennoni, 2014. "Longitudinal analysis of self-reported health status by mixture latent auto-regressive models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(2), pages 267-288, February.

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