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.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
- 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
Curated claims
Claims persisted in the evidence ledger, each with its own assessment.
This view does not invent a claim assessment when the ledger has none.
Related methods
Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.