Likelihood-based inference for nonlinear models with both individual and time effects

Yutao Sun

No 541934, Working Papers of Department of Economics, Leuven from KU Leuven, Faculty of Economics and Business (FEB), Department of Economics, Leuven

Abstract: We propose a bias correction method for nonlinear models with both individual and time effects. Under the presence of the incidental parameter problem, the maximum likelihood estimator derived from such models may be severely biased. Our method produces an approximation to an infeasible log-likelihood function that is not exposed to the incidental parameter problem. The maximizer derived from the approximating function serves as a bias-corrected estimator that is asymptotically unbiased when the sequence N=T converges to a constant. The proposed method is general in several perspectives. The method can be extended to models with multiple fixed effects and can be easily modified to accommodate dynamic models.

Date: 2016-05
New Economics Papers: this item is included in nep-ecm and nep-net
Note: paper number DPS 16.07
References: Add references at CitEc
Citations:

Published in Department of Economics. Discussion paper series,, pages 1-35

Downloads: (external link)
https://lirias.kuleuven.be/retrieve/389354 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ete:ceswps:541934

Access Statistics for this paper

More papers in Working Papers of Department of Economics, Leuven from KU Leuven, Faculty of Economics and Business (FEB), Department of Economics, Leuven
Bibliographic data for series maintained by library EBIB ().