NDLI: Optimal designs subject to cost constraints in simultaneous equations models

Content Provider	Springer Nature Link
Author	Casero Alonso, Víctor López Fidalgo, Jesús
Copyright Year	2015
Abstract	A procedure based on a multiplicative algorithm for computing optimal experimental designs subject to cost constraints in simultaneous equations models is presented. A convex criterion function based on a usual criterion function and an appropriate cost function is considered. A specific L-optimal design problem and a numerical example are taken from Conlisk (J Econ 11:63–76, 1979) to compare the procedure. The problem would need integer nonlinear programming to obtain exact designs. To avoid this, he solves a continuous nonlinear programming problem and then he rounds off the number of replicates of each experiment. The procedure provided in this paper reduces dramatically the computational efforts in computing optimal approximate designs. It is based on a specific formulation of the asymptotic covariance matrix of the full-information maximum likelihood estimators, which simplifies the calculations. The design obtained for estimating the structural parameters of the numerical example by this procedure is not only easier to compute, but also more efficient than the design provided by Conlisk.
Starting Page	701
Ending Page	713
Page Count	13
File Format	PDF
ISSN	11330686
Journal	Test
Volume Number	24
Issue Number	4
e-ISSN	18638260
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2015-03-01
Publisher Institution	Spanish Society of Statistics and Operations Research
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Approximate design Cost constraints Exact design L-optimal design Multiplicative algorithm Simultaneous equations Structural equations Optimal designs Applications to economics Statistics Statistical Theory and Methods Statistics for Business/Economics/Mathematical Finance/Insurance
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty

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