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Testing for state dependence in binary panel data with individual covariates

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  • Bartolucci, Francesco
  • Nigro, Valentina
  • Pigini, Claudia

Abstract

We propose a test for state dependence in binary panel data under the dynamic logit model with individual covariates. For this aim, we rely on a quadratic exponential model in which the association between the response variables is accounted for in a different way with respect to more standard formulations. The level of association is measured by a single parameter that may be estimated by a conditional maximum likelihood approach. Under the dynamic logit model, the conditional estimator of this parameter converges to zero when the hypothesis of absence of state dependence is true. This allows us to implement a Wald test for this hypothesis which may be very simply performed and attains the nominal significance level under any structure of the individual covariates. Through an extensive simulation study, we find that our test has good finite sample properties and it is more robust to the presence of (autocorrelated) covariates in the model specification in comparison with other existing testing procedures for state dependence. The test is illustrated by an application based on data coming from the Panel Study of Income Dynamics.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 48233.

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Date of creation: 11 Jul 2013
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Handle: RePEc:pra:mprapa:48233

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Keywords: conditional inference; dynamic logit model; quadratic exponential model; Wald test;

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  1. Jinyong Hahn & Whitney Newey, 2003. "Jackknife and analytical bias reduction for nonlinear panel models," CeMMAP working papers CWP17/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  2. Timothy J. Halliday, 2006. "Testing for State Dependence with Time-Variant Transition Probabilities," Working Papers 200614, University of Hawaii at Manoa, Department of Economics.
  3. Dean R. Hyslop, 1999. "State Dependence, Serial Correlation and Heterogeneity in Intertemporal Labor Force Participation of Married Women," Econometrica, Econometric Society, vol. 67(6), pages 1255-1294, November.
  4. Bo E. Honoré & Ekaterini Kyriazidou, 2000. "Panel Data Discrete Choice Models with Lagged Dependent Variables," Econometrica, Econometric Society, vol. 68(4), pages 839-874, July.
  5. Carro, Jesus M., 2007. "Estimating dynamic panel data discrete choice models with fixed effects," Journal of Econometrics, Elsevier, vol. 140(2), pages 503-528, October.
  6. Bartolucci, Francesco & Farcomeni, Alessio, 2009. "A Multivariate Extension of the Dynamic Logit Model for Longitudinal Data Based on a Latent Markov Heterogeneity Structure," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 816-831.
  7. Bartolucci, Francesco & Nigro, Valentina, 2012. "Pseudo conditional maximum likelihood estimation of the dynamic logit model for binary panel data," Journal of Econometrics, Elsevier, vol. 170(1), pages 102-116.
  8. Chamberlain, G., 1993. "Feedback in Panel Data Medels," Harvard Institute of Economic Research Working Papers 1656, Harvard - Institute of Economic Research.
  9. Francesco Bartolucci† & Valentina Nigro, 2007. "A dynamic model for binary panel data with unobserved heterogeneity admitting a Vn-consistent conditional estimator," CEIS Research Paper 97, Tor Vergata University, CEIS.
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