Welfare, freedom of choice and composite utility in the logit model

Discrete choice models characterize the alternatives in the choice set by utilities/attributes. The decision making is described by a probability distribution over the choice set. In this paper we introduce a welfare measure based on expected payoff and expected freedom of choice for the simple one parameter logit model. In this case the welfare measure turns out to be the so called composite utility. This means that the composite utility can be interpreted as the combined benefit of expected payoff and expected freedom of choice. The proposed welfare measure can be extended to the linear-in-parameters logit model and nested logit models and others. The proposed welfare measure is formulated in terms of the choice probability distribution. It depends on the form of the probabilities, but not on any particular derivation of the distribution.     

Parameter Orthogonality and Bias Adjustment for Estimating Functions

Abstract.  We consider an extended notion of parameter orthogonality for estimating functions, called nuisance parameter insensitivity, which allows a unified treatment of nuisance parameters for a wide range of methods, including Liang and Zeger's generalized estimating equations. Nuisance parameter insensitivity has several important properties in common with conventional parameter orthogonality, such as the nuisance parameter causing no loss of efficiency for estimating the interest parameter, and a simplified estimation algorithm. We also consider bias adjustment for profile estimating functions, and apply the results to restricted maximum likelihood estimation of dispersion parameters in generalized estimating equations.