Identification of linear panel data models when instruments are not available
One of the major virtues of panel data models is the possibility to control for unobserved and unobservable heterogeneity at the unit (individual, firm, sector...) level, even when this is correlated with the variables included on the right hand side of the equation. By assuming an additive error structure, identification of the model parameters spans from transformations of the data that wipe out the individual component. We propose an alternative identification strategy, where the equation of interest is embedded in a structural system that properly accounts for the endogeneity of the variables on the right hand side (without distinguishing correlation with the individual component or the idiosyncratic term). We show that, under certain conditions, the system is identified even in the case where no exogenous variable is available, due to the presence of cross-equation restrictions. Estimation of the model parameters can rely on an iterated Zellner-type estimator, with remarkable performance gains over traditional GMM approaches.
|Date of creation:||Feb 2012|
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- 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.
- M Arellano & O Bover, 1990. "Another Look at the Instrumental Variable Estimation of Error-Components Models," CEP Discussion Papers dp0007, Centre for Economic Performance, LSE.
- R Blundell & Steven Bond, .
"Initial conditions and moment restrictions in dynamic panel data model,"
W14&104., Economics Group, Nuffield College, University of Oxford.
- 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.
- Blundell, R. & Bond, S., 1995. "Initial Conditions and Moment Restrictions in Dynamic Panel Data Models," Economics Papers 104, Economics Group, Nuffield College, University of Oxford.
- Richard Blundell & Steve Bond, 1995. "Initial conditions and moment restrictions in dynamic panel data models," IFS Working Papers W95/17, Institute for Fiscal Studies.
- Bhargava, Alok & Sargan, J D, 1983. "Estimating Dynamic Random Effects Models from Panel Data Covering Short Time Periods," Econometrica, Econometric Society, vol. 51(6), pages 1635-59, November.
- Anderson, T. W. & Hsiao, Cheng, 1982. "Formulation and estimation of dynamic models using panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 47-82, January.
- Dagenais, Marcel G, 1978. "The Computation of FIML Estimates as Iterative Generalized Least Squares Estimates in Linear and Nonlinear Simultaneous Equations Models," Econometrica, Econometric Society, vol. 46(6), pages 1351-62, November.
- Ahn, Seung C. & Schmidt, Peter, 1995. "Efficient estimation of models for dynamic panel data," Journal of Econometrics, Elsevier, vol. 68(1), pages 5-27, July.
- Berzeg, Korhan, 1979. "The error components model : Conditions for the existence of the maximum likelihood estimates," Journal of Econometrics, Elsevier, vol. 10(1), pages 99-102, April.
- Giorgio Calzolari & Laura Magazzini, 2011. "Moment Conditions and Neglected Endogeneity in Panel Data Models," Working Papers 02/2011, University of Verona, Department of Economics.
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