Nonlinear Random Effects Model
The nonlinear random effects model extends classical random effects estimation to settings where the outcome variable is binary, count-based, censored, or otherwise non-continuously distributed across panel units. It accounts for unobserved individual heterogeneity by treating unit-specific effects as random draws from a distribution, then integrating them out to form a likelihood that can be maximised over the structural parameters.
Kilderegistrering
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- Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. · ISBN 978-0262232586
- Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. · ISBN 978-1107038691
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