IDEAS home Printed from https://ideas.repec.org/p/anc/wpaper/421.html
   My bibliography  Save this paper

Granger causality in dynamic binary short panel data models

Author

Listed:
  • Francesco Bartolucci

    () (Universita' di Perugia)

  • Claudia Pigini

    () (Universita' Politecnica delle Marche, Dipartimento di Scienze Economiche e Sociali)

Abstract

Strict exogeneity of covariates other than the lagged dependent variable, and conditional on unobserved heterogeneity, is often required for consistent estimation of binary panel data models. This assumption is likely to be violated in practice because of feedback effects from the past of the outcome variable on the present value of covariates and no general solution is yet available. In this paper, we provide the conditions for a logit model formulation that takes into account feedback effects without specifying a joint parametric model for the outcome and predetermined explanatory variables. Our formulation is based on the equivalence between Granger's de nition of noncausality and a modi cation of the Sims' strict exogeneity assumption for nonlinear panel data models, introduced by Chamberlain (1982) and for which we provide a more general theorem. We further propose estimating the model parameters with a recent xed-e ects approach based on pseudo conditional inference, adapted to the present case, thereby taking care of the correlation between individual permanent unobserved heterogeneity and the model's covariates as well. Our results hold for short panels with a large number of cross-section units, a case of great interest in microeconomic applications.

Suggested Citation

  • Francesco Bartolucci & Claudia Pigini, 2017. "Granger causality in dynamic binary short panel data models," Working Papers 421, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    • Handle: RePEc:anc:wpaper:421
    as

    Download full text from publisher

    File URL: http://docs.dises.univpm.it/web/quaderni/pdf/421.pdf
    File Function: First version, 2017
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Rob Alessie & Stefan Hochguertel & Arthur van Soest, 2004. "Ownership of Stocks and Mutual Funds: A Panel Data Analysis," The Review of Economics and Statistics, MIT Press, vol. 86(3), pages 783-796, August.
    2. repec:jss:jstsof:v:078:i07 is not listed on IDEAS
    3. Jesus M. Carro & Alejandra Traferri, 2014. "State Dependence And Heterogeneity In Health Using A Bias‐Corrected Fixed‐Effects Estimator," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(2), pages 181-207, March.
    4. Pigini, Claudia & Presbitero, Andrea F. & Zazzaro, Alberto, 2016. "State dependence in access to credit," Journal of Financial Stability, Elsevier, vol. 27(C), pages 17-34.
    5. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    6. Giulia Bettin & Riccardo Lucchetti, 2016. "Steady streams and sudden bursts: persistence patterns in remittance decisions," Journal of Population Economics, Springer;European Society for Population Economics, vol. 29(1), pages 263-292, January.
    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. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    9. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    10. Pierre‐Carl Michaud & Konstantinos Tatsiramos, 2011. "Fertility and female employment dynamics in Europe: the effect of using alternative econometric modeling assumptions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(4), pages 641-668, June.
    11. Lorenzo Cappellari & Stephen P. Jenkins, 2004. "Modelling low income transitions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(5), pages 593-610.
    12. 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.
    13. Francesco Bartolucci & Valentina Nigro, 2010. "A Dynamic Model for Binary Panel Data With Unobserved Heterogeneity Admitting a √n-Consistent Conditional Estimator," Econometrica, Econometric Society, vol. 78(2), pages 719-733, March.
    14. Wooldridge, Jeffrey M., 2000. "A framework for estimating dynamic, unobserved effects panel data models with possible feedback to future explanatory variables," Economics Letters, Elsevier, vol. 68(3), pages 245-250, September.
    15. Timothy J. Halliday, 2008. "Heterogeneity, state dependence and health," Econometrics Journal, Royal Economic Society, vol. 11(3), pages 499-516, November.
    16. Christoph Wunder & Regina T. Riphahn, 2014. "The dynamics of welfare entry and exit amongst natives and immigrants," Oxford Economic Papers, Oxford University Press, vol. 66(2), pages 580-604.
    17. Michael P. Keane & Robert M. Sauer, 2009. "Classification Error in Dynamic Discrete Choice Models: Implications for Female Labor Supply Behavior," Econometrica, Econometric Society, vol. 77(3), pages 975-991, May.
    18. Bo E. Honore & Arthur Lewbel, 2002. "Semiparametric Binary Choice Panel Data Models Without Strictly Exogeneous Regressors," Econometrica, Econometric Society, vol. 70(5), pages 2053-2063, September.
    19. Jeffrey M. Wooldridge, 2005. "Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(1), pages 39-54.
    20. Martin Biewen, 2009. "Measuring state dependence in individual poverty histories when there is feedback to employment status and household composition," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(7), pages 1095-1116.
    21. Mark B. Stewart, 2007. "The interrelated dynamics of unemployment and low-wage employment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(3), pages 511-531.
    22. Brown, Sarah & Ghosh, Pulak & Taylor, Karl, 2014. "The existence and persistence of household financial hardship: A Bayesian multivariate dynamic logit framework," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 285-298.
    23. Arellano, Manuel & Carrasco, Raquel, 2003. "Binary choice panel data models with predetermined variables," Journal of Econometrics, Elsevier, vol. 115(1), pages 125-157, July.
    24. Sims, Christopher A, 1972. "Money, Income, and Causality," American Economic Review, American Economic Association, vol. 62(4), pages 540-552, September.
    25. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    26. Paul Contoyannis & Andrew M. Jones & Nigel Rice, 2004. "Simulation-based inference in dynamic panel probit models: An application to health," Empirical Economics, Springer, vol. 29(1), pages 49-77, January.
    27. 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.
    28. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Oxford University Press, vol. 58(2), pages 277-297.
    29. Bartolucci, Francesco & Pigini, Claudia, 2017. "cquad: An R and Stata Package for Conditional Maximum Likelihood Estimation of Dynamic Binary Panel Data Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 78(i07).
    30. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    31. Alpaslan Akay, 2012. "Finite‐sample comparison of alternative methods for estimating dynamic panel data models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(7), pages 1189-1204, November.
    32. Mosconi, Rocco & Seri, Raffaello, 2006. "Non-causality in bivariate binary time series," Journal of Econometrics, Elsevier, vol. 132(2), pages 379-407, June.
    33. Gary Chamberlain, 1980. "Analysis of Covariance with Qualitative Data," Review of Economic Studies, Oxford University Press, vol. 47(1), pages 225-238.
    34. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, January.
    35. Heckman, James J & Borjas, George J, 1980. "Does Unemployment Cause Future Unemployment? Definitions, Questions and Answers from a Continuous Time Model of Heterogeneity and State Dependence," Economica, London School of Economics and Political Science, vol. 47(187), pages 247-283, August.
    36. 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.
    37. Florens, J P & Mouchart, M, 1982. "A Note on Noncausality," Econometrica, Econometric Society, vol. 50(3), pages 583-591, May.
    38. Chamberlain, Gary, 1982. "The General Equivalence of Granger and Sims Causality," Econometrica, Econometric Society, vol. 50(3), pages 569-581, May.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    binary panel data; fixed effects; feedback effects; pseudo-conditional inference;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:anc:wpaper:421. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Maurizio Mariotti). General contact details of provider: http://edirc.repec.org/data/deancit.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.