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Empirical exercises in economics frequently involve estimation of highly nonlinear models. The criterion function may not be globally concave or convex and exhibit many local extrema. Choosing among these local extrema is non-trivial for a variety of reasons. In this paper, the authors analyze the sensitivity of parameter estimates, and most importantly of economic variables of interest, to both starting values and the type of non-linear optimization algorithm employed. They focus on a class of demand models for differentiated products that have been used extensively in industrial organization, and more recently in public and labor. They find that convergence may occur at a number of local extrema, at saddles and in regions of the objective function where the first-order conditions are not satisfied.
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