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Analysis of Panel Data
TLDR
In this paper, the authors propose a homogeneity test for linear regression models (analysis of covariance) and show that linear regression with variable intercepts is more consistent than simple regression with simple intercepts.Abstract:
1. Introduction 2. Homogeneity test for linear regression models (analysis of covariance) 3. Simple regression with variable intercepts 4. Dynamic models with variable intercepts 5. Simultaneous-equations models 6. Variable-coefficient models 7. Discrete data 8. Truncated and censored data 9. Cross-sectional dependent panel data 10. Dynamic system 11. Incomplete panel data 12. Miscellaneous topics 13. A summary view.read more
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Econometric Analysis of Cross Section and Panel Data
TL;DR: This is the essential companion to Jeffrey Wooldridge's widely-used graduate text Econometric Analysis of Cross Section and Panel Data (MIT Press, 2001).
Report SeriesDOI
Initial conditions and moment restrictions in dynamic panel data models
Richard Blundell,Stephen Bond +1 more
TL;DR: In this paper, two alternative linear estimators that are designed to improve the properties of the standard first-differenced GMM estimator are presented. But both estimators require restrictions on the initial conditions process.
Journal ArticleDOI
Unit root tests in panel data: asymptotic and finite-sample properties
TL;DR: In this article, the authors consider pooling cross-section time series data for testing the unit root hypothesis, and they show that the power of the panel-based unit root test is dramatically higher, compared to performing a separate unit-root test for each individual time series.
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Interorganizational Collaboration and the Locus of Innovation: Networks of Learning in Biotechnology.
TL;DR: Powell et al. as mentioned in this paper developed a network approach to organizational learning and derive firm-level, longitudinal hypotheses that link research and development alliances, experience with managing interfirm relationships, network position, rates of growth, and portfolios of collaborative activities.
Journal ArticleDOI
What to do (and not to do) with time-series cross-section data
Nathaniel Beck,Jonathan N. Katz +1 more
TL;DR: The generalized least squares approach of Parks produces standard errors that lead to extreme overconfidence, often underestimating variability by 50% or more, and a new method is offered that is both easier to implement and produces accurate standard errors.