Blockwise generalized empirical likelihood inference for non-linear dynamic moment conditions models
AbstractThis paper shows how the blockwise generalized empirical likelihood method can be used to obtain valid asymptotic inference in non-linear dynamic moment conditions models for possibly non-stationary weakly dependent stochastic processes. The results of this paper can be used to construct test statistics for overidentifying moment restrictions, for additional moments, and for parametric restrictions expressed in mixed implicit and constraint form. Monte Carlo simulations seem to suggest that some of the proposed test statistics have competitive finite sample properties. Copyright � 2009 The Author(s). Journal compilation � Royal Economic Society 2009
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Bibliographic InfoArticle provided by Royal Economic Society in its journal Econometrics Journal.
Volume (Year): 12 (2009)
Issue (Month): 2 (07)
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- Francesco Bravo & Federico Crudu, 2012.
"Efficient bootstrap with weakly dependent processes,"
12/08, Department of Economics, University of York.
- Bravo, Francesco & Crudu, Federico, 2012. "Efficient bootstrap with weakly dependent processes," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3444-3458.
- Daniel J. Nordman & Helle Bunzel & Soumendra N. Lahiri, 2012. "A Non-standard Empirical Likelihood for Time Series," CREATES Research Papers 2012-55, School of Economics and Management, University of Aarhus.
- Wu, Rongning & Cao, Jiguo, 2011. "Blockwise empirical likelihood for time series of counts," Journal of Multivariate Analysis, Elsevier, vol. 102(3), pages 661-673, March.
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