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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Martins, L. F. & Gabriel, V. J.  (2014). Linear instrumental variables model averaging estimation. Computational Statistics and Data Analysis. 71, 709-724
Exportar Referência (IEEE) 
L. F. Martins and G. V.J.,  "Linear instrumental variables model averaging estimation", in Computational Statistics and Data Analysis, vol. 71, pp. 709-724, 2014
Exportar BibTeX 
@article{martins2014_1732396584661,
	author = "Martins, L. F. and Gabriel, V. J. ",
	title = "Linear instrumental variables model averaging estimation",
	journal = "Computational Statistics and Data Analysis",
	year = "2014",
	volume = "71",
	number = "",
	doi = "10.1016/j.csda.2013.05.008",
	pages = "709-724",
	url = "http://www.sciencedirect.com/science/article/pii/S0167947313001813"
}
Exportar RIS 
TY  - JOUR
TI  - Linear instrumental variables model averaging estimation
T2  - Computational Statistics and Data Analysis
VL  - 71
AU  - Martins, L. F.
AU  - Gabriel, V. J. 
PY  - 2014
SP  - 709-724
SN  - 0167-9473
DO  - 10.1016/j.csda.2013.05.008
UR  - http://www.sciencedirect.com/science/article/pii/S0167947313001813
AB  - Model averaging (MA) estimators in the linear instrumental variables regression framework are considered. The obtaining of weights for averaging across individual estimates by direct smoothing of selection criteria arising from the estimation stage is proposed. This is particularly relevant in applications in which there is a large number of candidate instruments and, therefore, a considerable number of instrument sets arising from different combinations of the available instruments. The asymptotic properties of the estimator are derived under homoskedastic and heteroskedastic errors. A simple Monte Carlo study contrasts the performance of MA procedures with existing instrument selection procedures, showing that MA estimators compare very favorably in many relevant setups. Finally, this method is illustrated with an empirical application to returns to education.
ER  -