Bartolucci, Francesco and Pigini, Claudia (2017): Granger causality in dynamic binary short panel data models.
Preview |
PDF
MPRA_paper_77486.pdf Download (392kB) | Preview |
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 definition of noncausality and a modification of the Sims' strict exogeneity assumption for nonlinear panel data models, introduced by Chamberlain1982 and for which we provide a more general theorem. We further propose estimating the model parameters with a recent fixed-effects 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.
Item Type: | MPRA Paper |
---|---|
Original Title: | Granger causality in dynamic binary short panel data models |
Language: | English |
Keywords: | fixed effects, noncausality, predetermined covariates, pseudo-conditional inference, strict exogeneity |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C12 - Hypothesis Testing: General C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C23 - Panel Data Models ; Spatio-temporal Models C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C25 - Discrete Regression and Qualitative Choice Models ; Discrete Regressors ; Proportions ; Probabilities |
Item ID: | 77486 |
Depositing User: | Francesco Bartolucci |
Date Deposited: | 13 Mar 2017 16:01 |
Last Modified: | 27 Sep 2019 23:46 |
References: | Akay, A. (2012). Finite-sample comparison of alternative methods for estimating dynamic panel data models. Journal of Applied Econometrics, 27(7):1189–1204. Alessie, R., Hochguertel, S., and van Soest, A. (2004). Ownership of Stocks and Mutual Funds: A Panel Data Analysis. The Review of Economics and Statistics, 86(3):783–796. Anderson, T. W. and Hsiao, C. (1981). Estimation of dynamic models with error components. Journal of the American statistical Association, 76(375):598–606. Arellano, M. and Bond, S. (1991). Some tests of specification for panel data: Monte carlo evidence and an application to employment equations. The review of economic studies, 58(2):277–297. Arellano, M. and Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of econometrics, 68(1):29–51. Arellano, M. and Carrasco, R. (2003). Binary choice panel data models with predetermined variables. Journal of Econometrics, 115(1):125–157. Arulampalam, W. (2002). State dependence in unemployment incidence: evidence for british men revisited. Technical report, IZA Discussion paper series. Barndorff-Nielsen, O. (1978). Information and exponential families in statistical theory. John Wiley & Sons. Bartolucci, F. and Nigro, V. (2010). A dynamic model for binary panel data with unobserved heterogeneity admitting a √n-consistent conditional estimator. Econometrica, 78:719–733. Bartolucci, F. and Nigro, V. (2012). Pseudo conditional maximum likelihood estimation of the dynamic logit model for binary panel data. Journal of Econometrics, 170:102–116. Bartolucci, F. and Pigini, C. (2016). cquad: An R and Stata package for conditional maximum likelihood estimation of dynamic binary panel data models. Journal of Statistical Software, In press. Bettin, G. and Lucchetti, R. (2016). Steady streams and sudden bursts: persistence patterns in remittance decisions. Journal of Population Economics, 29(1):263–292. Biewen, M. (2009). Measuring state dependence in individual poverty histories when there is feedback to employment status and household composition. Journal of Applied Econometrics, 24(7):1095–1116. Blundell, R. and Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of econometrics, 87(1):115–143. Brown, S., Ghosh, P., and Taylor, K. (2014). The existence and persistence of household financial hardship: A bayesian multivariate dynamic logit framework. Journal of Banking & Finance, 46:285–298. Cappellari, L. and Jenkins, S. P. (2004). Modelling low income transitions. Journal of Applied Econometrics, 19(5):593–610. Carrasco, R. (2001). Binary choice with binary endogenous regressors in panel data. Journal of Business & Economic Statistics, 19(4):385–394. Carro, J. M. and Traferri, A. (2012). State dependence and heterogeneity in health using a bias-corrected fixed-effects estimator. Journal of Applied Econometrics, 29(2):181–207. Chamberlain, G. (1980). Analysis of covariance with qualitative data. The Review of Economic Studies, 47(1):225–238. Chamberlain, G. (1982). The general equivalence of granger and sims causality. Econometrica: Journal of the Econometric Society, 50(3):569–581. Chamberlain, G. (1984). Panel data. Handbook of Econometrics, 2:1247–1318. Chamberlain, G. (1985). Heterogeneity, omitted variable bias, and duration dependence. In Heckman, J. J. and Singer, B., editors, Longitudinal Analysis of Labor Market Data. Cambridge University Press: Cambridge. Contoyannis, P., Jones, A. M., and Rice, N. (2004). Simulation-based inference in dynamic panel probit models: an application to health. Empirical Economics, 29(1):49–77. Cox, D. (1972). The analysis of multivariate binary data. Applied statistics, 21(2):113–120. Florens, J.-P. and Mouchart, M. (1982). A note on noncausality. Econometrica: Journal of the Econometric Society, 50(3):583–591. Giarda, E. (2013). Persistency of financial distress amongst italian households: Evidence from dynamic models for binary panel data. Journal of Banking & Finance, 37(9):3425 – 3434. Granger, C. W. (1969). Investigating causal relations by econometric models and crossspectral methods. Econometrica: Journal of the Econometric Society, 37(3):424–438. Halliday, T. J. (2008). Heterogeneity, state dependence and health. The Econometrics Journal, 11(3):499–516. Hansen, L. P. (1982). Large sample properties of generalized method of moments estimators. Econometrica: Journal of the Econometric Society, 50(4):1029–1054. Heckman, J. J. (1981). Heterogeneity and state dependence. Structural Analysis of Discrete Data with Econometric Applications, MIT Press: Cambridge MA. Manski CF, McFadden (eds). Heckman, J. J. and Borjas, G. J. (1980). Does unemployment cause future unemployment? definitions, questions and answers from a continuous time model of heterogeneity and state dependence. Economica, 47(187):pp. 247–283. Heiss, F. (2011). Dynamics of self-rated health and selective mortality. Empirical economics, 40(1):119–140. Honoré, B. E. and Kyriazidou, E. (2000). Panel data discrete choice models with lagged dependent variables. Econometrica, 68(4):839–874. Honoré, B. E. and Lewbel, A. (2002). Semiparametric binary choice panel data models without strictly exogeneous regressors. Econometrica, 70(5):2053–2063. Hsiao, C. (2005). Analysis of Panel Data. Cambridge University Press, New York, 2nd edition. Hyslop, D. R. (1999). State dependence, serial correlation and heterogeneity in intertemporal labor force participation of married women. Econometrica, 67(6):1255–1294. Keane, M. P. and Sauer, R. M. (2009). Classification error in dynamic discrete choice models: Implications for female labor supply behavior. Econometrica, 77(3):975–991. Michaud, P.-C. and Tatsiramos, K. (2011). Fertility and female employment dynamics in europe: the effect of using alternative econometric modeling assumptions. Journal of Applied Econometrics, 26(4):641–668. Mosconi, R. and Seri, R. (2006). Non-causality in bivariate binary time series. Journal of Econometrics, 132(2):379–407. Mundlak, Y. (1978). On the Pooling of Time Series and Cross Section Data. Econometrica, 46(1):69–85. Pigini, C., Presbitero, A. F., and Zazzaro, A. (2016). State dependence in access to credit. Journal of Financial Stability, pages –. forthcoming. Sims, C. A. (1972). Money, income, and causality. The American Economic Review, 62(4):540–552. Stewart, M. B. (2007). The interrelated dynamics of unemployment and low-wage employment. Journal of Applied Econometrics, 22(3):511–531. Wooldridge, J. M. (2000). A framework for estimating dynamic, unobserved effects panel data models with possible feedback to future explanatory variables. Economics Letters, 68(3):245–250. Wooldridge, J. M. (2005). Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity. Journal of Applied Econometrics, 20(1):39–54. Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. The MIT press. Wunder, C. and Riphahn, R. T. (2014). The dynamics of welfare entry and exit amongst natives and immigrants. Oxford Economic Papers, 66(2):580–604. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/77486 |