Local GMM Estimation of Semiparametric Panel Data with Smooth Coefficient Models
AbstractIn this article, we consider the estimation of semiparametric panel data smooth coefficient models. We propose a class of local generalized method of moments (LGMM) estimators that are simple and easy to implement in practice. We show that the proposed LGMM estimators are consistent and asymptotically normal. Monte Carlo simulations suggest that our proposed estimator performs quite well in finite samples. An empirical application using a large panel of U.K. firms is also presented.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Taylor and Francis Journals in its journal Econometric Reviews.
Volume (Year): 29 (2010)
Issue (Month): 1 ()
Contact details of provider:
Web page: http://taylorandfrancis.metapress.com/link.asp?target=journal&id=107830
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Delis, Manthos D & Tran, Kien & Tsionas, Efthymios, 2009.
"Quantifying and explaining parameter heterogeneity in the capital regulation-bank risk nexus,"
18526, University Library of Munich, Germany.
- Delis, Manthos D. & Tran, Kien C. & Tsionas, Efthymios G., 2012. "Quantifying and explaining parameter heterogeneity in the capital regulation-bank risk nexus," Journal of Financial Stability, Elsevier, vol. 8(2), pages 57-68.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Michael McNulty).
If references are entirely missing, you can add them using this form.