Advertisement

Structural Interdependence and Unobserved Heterogeneity in Event History Analysis

  • Daniel J. BlakeEmail author
  • Janet M. Box-Steffensmeier
  • Byungwon Woo
Chapter

Abstract

This chapter introduces how latent variables are handled in event history analysis, a popular method used to examine both the occurrence and the timing of events. We first emphasize why event history models are popular and what kinds of research questions the model can be used to answer. We also review the major estimation issues, briefly trace the development of event history models, and highlight the differences and similarities across various types of event history models. We then consider how latent variables are handled in event history analysis and demonstrate this with an example of latent variable analysis. In the conclusion we consider possible areas for future research.

Keywords

Ordinary Little Square Event History Failure Time Baseline Hazard Discrete Choice Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aalen, O. O. (1975). Statistical inference for a family of counting processes. PhD thesis, University of California, Berkeley.Google Scholar
  2. Adair, L. S., Popkin, B. M., & Guilkey D. K. (1993). The duration of breast-feeding: How is it affected by biological, sociodemographic, health sector, and food industry factors? Demography, 30, 63-80.CrossRefGoogle Scholar
  3. Addison, J. T., & Portugal, P. (1989). Job displacement, relative wage changes, and duration of unemployment. Journal of Labor Economics, 7, 281–302.CrossRefGoogle Scholar
  4. Allison, P. D. (1984). Event history analysis: Regression for longitudinal event data. Thousands Oaks: Sage.Google Scholar
  5. Baizán, P., Aassave, A., & Billari, F. C. (2004). The interrelations between cohabitation, marriage and first birth in Germany and Sweden. Population and Environment, 25, 531-561.CrossRefGoogle Scholar
  6. Billari, F. C., & Philipov D. (2004). Women’s education and entry into a first union: A simultaneous-hazard comparative analysis of Central and Eastern Europe. Vienna Yearbook of Population Research, 91-110.CrossRefGoogle Scholar
  7. Boehmke, F. J. (2006). The influence of unobservable factors in position timing and content in the NAFTA vote. Political Analysis, 14, 421-428.CrossRefGoogle Scholar
  8. Boehmke, F. J., Morey, D. S., & Shannon, M. (2006). Selection bias and continuous-time duration models: Consequences and a proposed solution. Journal of Political Science, 50, 192-207.CrossRefGoogle Scholar
  9. Box-Steffensmeier, J. M., & Jones, B. S. (2004). Event history modeling: A guide for social scientists. New York: Cambridge University Press.Google Scholar
  10. Box-Steffensmeier, J. M., Arnold, L. W., & Zorn, C. J. W. (1997). The strategic timing of position taking in Congress: A Study of the North American Free Trade Agreement. American Political Science Review, 91, 324-338.CrossRefGoogle Scholar
  11. Boyle, P. J., Kulu, H., Cooke, T., Gayle, V., & Mulder, C. H. (2008). Moving and union dissolution. Demography, 45, 209-222.CrossRefGoogle Scholar
  12. Collett, D. (2003). Modelling survival data in medical research. London: Chapman & Hall.Google Scholar
  13. Coppola, L. (2004). Education and union formation as simultaneous processes in Italy and Spain. European Journal of Population, 20, 219-250.CrossRefGoogle Scholar
  14. Cox, D. R. (1972). Regression models and life tables. Journal of the Royal Statistical Society, B, 34, 187-220.zbMATHGoogle Scholar
  15. Cox, D. R. (1975). Partial likelihood. Biometrika, 62, 269-276.zbMATHCrossRefMathSciNetGoogle Scholar
  16. Darmofal, D. (2009). Bayesian spatial survival models for political event processes. American Journal of Political Science, 53, 241-257.CrossRefGoogle Scholar
  17. Dwivedi, T. D., & Srivastava, V. K. (1978). Optimality of least squares in the seemingly unrelated regression equation model. Journal of Econometrics, 7, 391-395zbMATHCrossRefMathSciNetGoogle Scholar
  18. Fleming, T. R., & Lin, D. Y. (2000). Survival analysis in clinical trials: Past developments and future directions. Biometrics, 56, 971-983.zbMATHCrossRefMathSciNetGoogle Scholar
  19. Fukumoto, K. (2009). What happens depends on when it happens: Continuous or ordered event history analysis. Working paper: Faculty of Law, Gakushuin University, Tokyo.Google Scholar
  20. Golub, J., & Collett, D. (2002). Institutional reform and decision making in the European Union. In M. Hosli & A. van Deemen (Eds.), Institutional Challenges in the European Union. London: Routledge.Google Scholar
  21. Grambsch, P. M., & Therneau, T. M. (1994). Proportional hazards tests and diagnostics on weighted residuals. Biometrika, 81, 515-526.zbMATHCrossRefMathSciNetGoogle Scholar
  22. Hays, J. C., & Kachi, A. (2009). Interdependent Duration Models in Political Science. Paper presented at the Annual Meeting of the American Political Science Association, Toronto, Sept. 3-6, 2009.Google Scholar
  23. Honoré, B., & de Paula, A. (in press). Interdependent durations. Review of Economic Studies.Google Scholar
  24. Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation for incomplete observations. Journal of the American Statistical Association, 53, 457-481.zbMATHCrossRefMathSciNetGoogle Scholar
  25. Larsen, U., & Vaupel, J. W. (1993). Hutterite fecundability by age and parity: Strategies for frailty modeling of event histories. Demography, 30, 81-102.CrossRefGoogle Scholar
  26. Lillard, L. A. (1993). Simultaneous equations for hazards. Journal of Econometrics, 56, 189-217.CrossRefGoogle Scholar
  27. Maitra, P., & Sarmistha P. (2007). Birth spacing, fertility selection and child survival: Analysis using a correlated hazard model. Institute for the Study of Labor (IZA), Discussion Paper No. 2878.Google Scholar
  28. Mantel, N. (1966). Evaluation of survival data and two new rank order statistics arising in its consideration. Cancer Chemotherapy Report, 50, 163-170.Google Scholar
  29. Oakes, D. (2001). Biometrika centenary: Survival analysis. Biometrika, 88, 1.CrossRefMathSciNetGoogle Scholar
  30. Olshanksy, S. J., & Carnes, B. A. (1997). Ever since Gompertz. Demography, 34, 1-15.CrossRefGoogle Scholar
  31. Pindyck, R. S., & Rubinfeld, D. L. (1991). Econometric models and economic forecasts. New York: McGraw-Hill.Google Scholar
  32. Quiroz Flores, A. (2008). Copula functions and bivariate distributions for survival analysis: An application to government survival. NYU Department of Politics. Working paper.Google Scholar
  33. Rosholm, M., & Svarer, M. (2001). Structurally dependent competing risks. Economics Letters, 73, 169-173.zbMATHCrossRefGoogle Scholar
  34. Singer, J. D., & Willett, J. B. (1993). It’s about time: Using time survival analysis to study duration and the timing of events. Journal of Educational Statistics, 18, 155-195.CrossRefGoogle Scholar
  35. Therneau, T. M., Grambsch, P. M., & Fleming, T. R. (1990). Martingale based residuals for survival models. Biometrika, 77, 147-160.zbMATHCrossRefMathSciNetGoogle Scholar
  36. Therneau, T. M., & Grambsch, P. M. (2001). Modeling survival data: Extending the Cox model. New York: Springer-Verlag.Google Scholar
  37. Tuma, N. B. (1976). Rewards, resources, and the rate of mobility: A nonstationary multivariate stochastic model. American Sociological Review, 41, 338-360.CrossRefGoogle Scholar
  38. van Montfort, K., Oud, J., & Satorra, A. (Eds.) (2004). Recent developments on structural equation models: Theory and Applications. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  39. Zellner, A. (1962). An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. Journal of the American Statistical Association, 57, 348-368.zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2010

Authors and Affiliations

  • Daniel J. Blake
    • 1
    Email author
  • Janet M. Box-Steffensmeier
  • Byungwon Woo
  1. 1.Department of Political ScienceOhio State UniversityColumbusUSA

Personalised recommendations