Individual effects and dynamics in count data models

Journal of Econometrics

Volumn 108, Issue 1, 2002, Pages 113-131

  Blundell, Richard ^a,b   Griffith, Rachel ^b   Windmeijer, Frank ^b  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b INSTITUTE FOR FISCAL STUDIES   (United Kingdom)

Author keywords

Dynamic count panel data; Generalised method of moments; Individual effects; Pre Sample information; Predetermined regressors

Indexed keywords

CORRELATION METHODS; DATA STRUCTURES; FEEDBACK; MATHEMATICAL MODELS; METHOD OF MOMENTS; MONTE CARLO METHODS; PARAMETER ESTIMATION; RESEARCH AND DEVELOPMENT MANAGEMENT;

DYNAMIC COUNT PANEL DATA; EMPIRICAL MICROECONOMICS; PANEL DATA ESTIMATION;

ECONOMICS;

EID: 0346093811     PISSN: 03044076     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0304-4076(01)00108-7     Document Type: Article

References (38)

1. Ahn S.C., Schmidt P. Efficient estimation of models for dynamic panel data. Journal of Econometrics. 68:1995;5-28.
2. Al-Osh M.A., Alzaid A.A. First-order integer valued autoregressive (INAR(1)) process. Journal of Time Series Analysis. 8:1987;261-275.
3. Alzaid A.A., Al-Osh M.A. Integer-valued pth order autoregressive structure (INAR(p)) process. Journal of Applied Probability. 27:1990;314-324.
4. Arellano M., Bond S. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies. 58:1991;277-298.
5. Becketti S., Gould W., Lillard L., Welch F. The PSID after fourteen years: an evaluation. Journal of Labor Economics. 6:1988;472-492.
6. Bloom, N., Van Reenen, J., 2000. Real options, patents, productivity and market value: evidence from a panel of British firms. Working paper series no. W00/21, Institute for Fiscal Studies, London.
7. Blundell R., Bond S. Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics. 87:1998;115-144.
8. Blundell R., Griffith R., Van Reenen J. Dynamic count data models of technological innovation. The Economic Journal. 105:1995;333-344.
9. Blundell, R., Griffith, R., Windmeijer, F., 1999. Individual effects and dynamics in count data models. Working paper series no. W99/3, Institute for Fiscal Studies.
10. Bound J., Cummins C., Griliches Z., Hall B.H., Jaffe A. Who does R&D and who patents? Griliches Z. R&D, Patents and Productivity. 1982;University of Chicago Press, Chicago.
11. Böckenholt U. Mixed INAR(1) poisson regression models: analyzing heterogeneity and serial dependencies in longitudinal count data. Journal of Econometrics. 89:1999;317-338.
12. Brännäs, K., 1994. Estimation and testing in integer valued AR(1) models. Umeå Economic Studies Paper No. 335, Department of Economics, University of Umeå, February.
13. Cameron A., Trivedi P. Econometric models based on count data: comparisons and applications of some estimators and tests. Journal of Applied Econometrics. 1:1986;29-53.
14. Cameron A., Trivedi P. Regression analysis of count data. 1998;Econometric Society Monograph, Cambridge University Press.
15. Chamberlain G. Panel data. Griliches Z., Intriligator M. Handbook of Econometrics. 1984;North Holland, Amsterdam.
16. Chamberlain G. Comment: sequential moment restrictions in panel data. Journal of Business & Economic Statistics. 10:1992;20-26.
17. Chamberlain, G., 1993. Feedback in panel data models. Mimeo, Econometric Society Summer Meeting, Boston, June.
18. Cincera M. Patents, R&D, and technological spillovers at the firm level: some evidence from econometric count models for panel data. Journal of Applied Econometrics. 12:1997;265-280.
19. Crépon B., Duguet E. Estimating the innovation function from patent numbers: GMM on count panel data. Journal of Applied Econometrics. 12:1997;243-263.
20. Gourieroux C., Monfort A., Trognon A. Pseudo maximum likelihood methods: applications to poisson models. Econometrica. 52:1984;701-720.
21. Gustman A.L., Mitchell O.S., Steinmeier T.L. Retirement research using the health and retirement survey. Journal of Human Resources. 30:1995;557-583.
22. Hall B., Griliches Z., Hausman J. Patents and R and D: is there a lag? International Economic Review. 27:1986;265-283.
23. Hansen L.P. Large sample properties of generalized method of moments estimators. Econometrica. 50:1982;1029-1054.
24. Hausman J., Hall B., Griliches G. Econometric models for count data and an application to the patents-R&D relationship. Econometrica. 52:1984;909-938.
25. Holtz-Eakin D., Newey W., Rosen H. Estimating vector autoregressions with panel data. Econometrica. 56:1988;1371-1395.
26. Jin-Guan D., Yuan L. The integer-valued autoregressive (INAR(p)) model. Journal of Time Series Analysis. 12:1991;129-142.
27. Lancaster, T., 1999. Orthogonal parameters and panel data. Mimeo, Brown University.
28. McKenzie E. Some ARMA models for dependent sequences of poisson counts. Advances in Applied Probability. 20:1988;822-835.
29. McCullagh P., Nelder J.A. Generalized linear models. Monographs on Statistics and Applied Probability. vol. 37:1989;Chapman & Hall, London.
30. Montalvo J.G. GMM estimation of count-panel-data models with Fixed Effects and Predetermined Instruments. Journal of Business and Economic Statistics. 15:1997;82-89.
31. Nickell S.J. Biases in dynamic models with fixed effects. Econometrica. 39:1981;359-382.
32. Pavitt K., Robson M., Townsend J. The size distribution of innovating firms in the UK: 1945-83. Journal of Industrial Economics. 35:1987;297-316.
33. Ronning G., Jung R.C. Estimation of a first order autoregressive process with Poisson marginals for count data. Fahrmeir L., Francis B., Gilchrist R., Tutz G. Advances in GLIM and Statistical Modelling. Lecture Notes in Statistics. vol. 78:1992;188-194 Springer, Berlin.
34. Staiger D., Stock J.H. Instrumental variables regression with weak instruments. Econometrica. 65:1997;557-586.
35. Windmeijer F. Moment conditions for fixed effects count data models with endogenous regressors. Economics Letters. 68:2000;21-24.
36. Winkelmann R. Econometric analysis of count data. 3rd Edition:2000;Springer, Heidelberg.
37. Wooldridge, J.M., 1991. Multiplicative panel data models without the strict exogeneity assumption. MIT Working paper No. 574.
38. Wooldridge J.M. Multiplicative panel data models without the strict exogeneity assumption. Econometric Theory. 13:1997;667-678.