Maximum likelihood estimation of an extended latent Markov model for clustered binary panel data

Computational Statistics and Data Analysis

Volumn 51, Issue 7, 2007, Pages 3470-3483

  Bartolucci, Francesco ^a   Nigro, Valentina ^b  

^a UNIVERSITY OF PERUGIA   (Italy)

^b UNIVERSITY OF ROME TOR VERGATA   (Italy)

Author keywords

EM algorithm; Finite mixture models; Heterogeneity; Latent class model; State dependence

Indexed keywords

ALGORITHMS; COMPUTATIONAL METHODS; ERROR ANALYSIS; MARKOV PROCESSES; MATRIX ALGEBRA; MAXIMUM LIKELIHOOD ESTIMATION;

CLUSTERED BINARY PANEL DATA; EXPECTATION MAXIMIZATION (EM); TIME INVARIANT;

DATA REDUCTION;

EID: 33847388026     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2006.11.030     Document Type: Article

References (27)

1. Arellano, M., Honoré, B.E., 2001. Panel data models: some recent developments. In: Heckman, J.J., Leamer, E. (Eds.), Handbook of Econometrics, vol. 5. Elsevier Science, Amsterdam, pp. 3229-3296.
2. Bartolucci F. Likelihood inference for a class of latent Markov models under linear hypotheses on the transition probabilities. J. Roy. Statist. Soc. Ser. B 68 (2006) 155-178
3. Butler J.S., and Moffitt R. A computationally efficient quadrature procedure for one-factor multinomial probit model. Econometrica 50 (1982) 761-768
4. Chamberlain, G., 1984. Panel data. In: Griliches, Z., Intriligator, M.D. (Eds.), vol. 2. Handbook of Econometrics. Elsevier Science, Amsterdam, pp. 1247-1318.
5. Chintagunta P., Kyriazidou E., and Perktold J. Panel data analysis of household brand choices. J. Econometrics 103 (2001) 111-153
6. Dempster A.P., Laird N.M., and Rubin D.B. Maximum likelihood from incomplete data via the EM algorithm (with discussion). J. Roy. Statist. Soc. Ser. B 39 (1977) 1-38
7. Fuertes A.M., and Kalotychou E. Early warning systems for sovereign debt crises: the role of heterogeneity. Comput. Statist. Data Anal. 51 (2006) 1420-1441
8. Goodman L.A. Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika 61 (1974) 215-231
9. Hajivassiliou V.A., and McFadden D.L. The method of simulated scores for the estimation of LDV models. Econometrica 66 (1998) 863-896
10. Heckman J.J. Statistical models for discrete panel data. In: Manski C.F., and McFadden D. (Eds). Structural Analysis of Discrete Data with Econometric Applications (1981), MIT Press, Cambridge 114-178
11. Heckman J.J. The incidental parameters problem and the problem of initial conditions in estimating a discrete time-discrete data stochastic process. In: Manski C.F., and McFadden D. (Eds). Structural Analysis of Discrete Data with Econometric Applications (1981), MIT Press, Cambridge, USA 179-195
12. Heckman J.J. Heterogeneity and state dependence. In: Rosen S. (Ed). Studies in Labor Markets (1981), University of Chicago Press, Chicago 91-139
13. Heckman J.J., and Willis R.J. A beta-logistic model for the analysis of sequential labor force participation by married women. J. Political Economy 85 (1977) 27-58
14. Heckman J., and Singer B. A method for minimizing the impact of distributional assumptions in econometric models for duration data. Econometrica 52 (1984) 271-320
15. Hsiao C. Analysis of Panel Data. second ed. (2003), Cambridge University Press, Cambridge, UK
16. Hyslop D.R. State dependence, serial correlation and heterogeneity in intertemporal labor force participation of married women. Econometrica 67 (1999) 1255-1294
17. Langeheine R., and van de Pol F. Latent Markov chains. In: Hagenaars J.A., and McCutcheon A.L. (Eds). Applied Latent Class Analysis (2002), Cambridge University Press, Cambridge, UK 304-341
18. Langeheine R., Stern E., and van de Pol F. State mastery learning: dynamic models for longitudinal data. Appl. Psychological Measurement 18 (1994) 277-291
19. Lazarsfeld P.F., and Henry N.W. Latent Structure Analysis (1968), Houghton Mifflin, Boston
20. Mannan H.R., and Koval J.J. Latent mixed Markov modelling of smoking transitions using Monte Carlo bootstrapping. Statist. Meth. Med. Res. 12 (2003) 125-146
21. McLachlan G.J., and Peel D. Finite Mixture Models (2000), Wiley, New York
22. Rothenberg T.J. Identification in parametric models. Econometrica 39 (1971) 577-591
23. Schwarz G. Estimating the dimension of a model. Ann. Statist. 6 (1978) 461-464
24. Van de Pol F., and Langeheine R. Mixed Markov latent class models. In: Clogg C.C. (Ed). Sociological Methodology (1990), Blackwell, Oxford 213-247
25. Vermunt J.K., Langeheine R., and Böckenholt U. Discrete-time discrete-state latent Markov models with time-constant and time-varying covariates. J. Edu. Behav. Statist. 24 (1999) 179-207
26. Wiggins L.M. Panel Analysis: Latent Probability Models for Attitude and Behavior Processes (1973), Elsevier, Amsterdam
27. Yang M., Goldstein H., and Heath A. Multilevel models for repeated binary outcomes: attitudes and voting over the electoral cycle. J. Roy. Statist. Soc. Ser. A 163 (2000) 49-62