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Multiplicative Panel Data Models Without the Strict Exogeneity Assumption
Open accessAbstract
This paper considers estimation of multiplicative, unobserved components panel data models without imposing a strict exogeneity assumption on the conditioning variables. The method of moments estimators proposed have significant robustness properties. They require only a conditional mean assumption and apply to models with lagged dependent variables and to finite distributed lag models with arbitrary feedback from the explained to future values of the explanatory variables. The model is particularly suited to nonnegative explained variables, including count variables, continuously distributed nonnegative outcomes, and even binary variables. The general model can also be applied to certain nonlinear Euler equations.
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Cites
Balestra, P., & Nerlove, M. (1966) · Econometrica · doi:10.2307/1909771 · Cited by 1,005
Cited by
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Bollinger, B., & Gillingham, K. (2012) · Marketing Science · doi:10.1287/mksc.1120.0727 · Cited by 1,013
Panel Data Models: Some Recent Developments
Arellano, M., & Honoré, B. (2001) · Handbook of Econometrics · doi:10.1016/s1573-4412(01)05006-1 · Cited by 561
Su, H.-N., & Moaniba, I. M. (2017) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2017.04.017 · Cited by 366
Keane, M. P., & Runkle, D. E. (1992) · Journal of Business & Economic Statistics · doi:10.1080/07350015.1992.10509877 · Cited by 337
Metadata
- Authors
- Wooldridge, J. M.
- Venue
- Econometric Theory
- Publisher
- Cambridge University Press (CUP)
- Published
- 1997-10
- Volume
- 13
- Issue
- 5
- Pages
- 667-678
- ISSN
- 0266-4666 (ISSN-L)
- 1469-4360
- OpenAlex
- W2134819260
- Cited by
- 185
- References
- 1
- FWCI
- 7.32
- Type
- journal article
- Language
- en
- Open access
- green
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- Wooldridge, J. M. (1997). Multiplicative Panel Data Models Without the Strict Exogeneity Assumption. Econometric Theory. https://doi.org/10.1017/s0266466600006125
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
