Flexible parametric models for long-tailed patent count distributions

Oxford Bulletin of Economics and Statistics

Volumn 64, Issue 1, 2002, Pages 63-82

  Guo, Jie Q ^a   Trivedi, Pravin K ^a  

^a INDIANA UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0036487482     PISSN: 03059049     EISSN: None     Source Type: Journal    
DOI: 10.1111/1468-0084.00004     Document Type: Article

References (29)

1. Andrews, D.W.K. (1988). 'Chi-square Diagnostic Tests for Econometric Models: Theory', Econometrica, Vol. 56, pp. 1419-53.
2. Blundell, R., Griffith, R. and Van Reenan, J. (1995). 'Dynamic Count Models of Technological Innovation', Economic Journal, Vol. 105, pp. 333-44.
3. Böhning, D. (1995). 'A Review of Reliable Maximum Likelihood Algorithms for Semiparametric Mixture Models', Journal of Statistical Planning and Inference, Vol. 47, pp. 5-28.
4. Cameron, A.C. and Johansson, P. (1997). 'Count Data Regressions using Series Expansions With Applications', Journal of Applied Econometrics, 12, 3, 203-24.
5. Cameron, A.C. and Trivedi, P.K. (1998). Regression Analysis of Count Data, Cambridge University Press, Cambridge, U.K.
6. Cincera, M. (1997). 'Patents, R & D, and Technological Spillovers at the Firm Level: Some Evidence from Econometric Count Models for Panel Data', Journal of Applied Econometrics, Vol. 12, pp. 265-80.
7. Crepon, B. and Duguet, E. (1997a). 'Research and Development, Competition and Innovation Pseudo-maximum Likelihood and Simulated Maximum Likelihood Methods Applied to Count Data Models with Heterogeneity', Journal of Econometrics, Vol. 79, pp. 355-78.
8. Crepon, B. and Duguet, E. (1997b). 'Estimating the Innovation Function from the Patent Numbers: GMM on Count Panel Data', Journal of Applied Econometrics, Vol. 12, pp. 243-64.
9. Dean, C., Lawless, J.F. and Willmot, G.E. (1989). 'A Mixed Poisson-Inverse-Gaussian Regression Model', The Canadian journal of Statistics, Vol. 17, pp. 171-81.
10. Deb, P. and Trivedi, P.K. (1997). 'Demand for Medical Care by the Elderly: A Finite Mixture Approach', Journal of Applied Econometrics, Vol. 12, pp. 313-36.
11. Gallant, A.R. and Nychka, D.W. (1987). 'Seminonparametric Maximum Likelihood Estimation', Econometrica, Vol. 55, pp. 363-90.
12. Goffe, W.L., Ferrier, G.D. and Rogers, J. (1994). 'Global Optimization of Statistical Functions with Simulated Annealing', Journal of Econometrics, Vol. 60, pp. 65-99.
13. Guo, J. and Trivedi, P.K. (2001). 'Firm-Specific Assets and the Link Between Exchange Rates and Japanese Foreign Direct Investment in the United States: A Reexamination', Japanese Economic Review (forthcoming).
14. Gurmu, S., Rilstone, P. and Stern, S. (1999). 'Semiparametric Estimation of Count Regression Models', Journal of Econometrics, Vol. 88, pp. 123-50.
15. Hall, B.H., Hausman, J. and Griliches, Z. (1986). 'Patents and R & D: Is there a lag?', International Economic Review, Vol. 27, pp. 265-83.
16. Hausman, J., Hall, B.H. and Griliches, Z. (1984). 'Econometric Models for Count Data with an Application to the Patents-R & D Relationship', Econometrica, Vol. 52, pp. 909-38.
17. Heckman, J. and Singer, B. (1984). 'A Method of Minimizing the Impact of Distributional Assumptions in Econometric Models for Duration Data', Econometrica, Vol. 52, pp. 271-320.
18. Holgate, P. (1970). 'The Modality of Some Compound Poisson Distributions', Biometrika, Vol. 57, pp. 666-67.
19. Huber, P.J. (1981). Robust Statistics, John Wiley, New York.
20. Lindsay, B.G. (1995). Mixture Models: Theory, Geometry and Applications, NSF-CBMS Regional Conference Series in Probability and Statistics, Volume 5, IMS-ASA.
21. Ord, J. and Whitmore, G. (1986). 'The Poisson-Inverse Gaussian Distribution as a Model for Species Abundance', Communications in Statistics A, Theory and Methods, Vol. 15, pp. 853-71.
22. Severini, T.A. and Staniswalis, J.G. (1994). 'Quasi-likelihood Estimation in Semiparametric Models', Journal of American Statistical Association, Vol. 89, pp. 501-11.
23. Sichel, H.S. (1971). 'On a Family of Discrete Distributions Particularly Suited to Represent Long-tailed Frequency Data', in Laubscher, N.F. (ed.), Proceedings of Third Symposium on Mathematical Statistics, C.S.I.R. Pretoria, South Africa, 51-97.
24. Sichel, H.S. (1974). 'On a Distribution Representing Sentence-length in Written Prose', Journal of the Royal Statistical Society Series A, Vol. 137, pp. 25-34.
25. Sichel, H.S. (1975). 'On a Distribution Law for Word Frequencies', Journal of the American Statistical Association, Vol. 70, pp. 542-47.
26. Tweedie, M.C.K. (1957). 'Statistical Properties of Inverse Gaussian Distributions. I', Annals of Mathematical Statistics, Vol. 28, pp. 362-77.
27. Wang, P., Cockburn, I.M. and Puterman, M.L. (1998). 'Analysis of Patent Data - A Mixed-Poisson-Regression-Model Approach', Journal of Business and Economic Statistics, Vol. 16, pp. 27-41.
28. Willmot, G.E. (1987). 'The Poisson-Inverse Gaussian Distribution as an Alternative to the Negative Binomial', Scandinavian Actuarial Journal, Vol. 198, pp. 113-27.
29. Yule, G.U. (1944). A Statistical Study of Vocabulary, Cambridge University Press, Cambridge, England.