Showing papers by "Daniel McFadden published in 1993"

Living Arrangements: Health and Wealth Effects

Abstract: This paper investigates the choice of living arrangements among elderly Americans. It has two specific aims. First, because health is not directly measurable and can only be described by indicators such as ADLs and IADLs, it explores a new econometric approach to model the influence of the latent health status on living arrangements. Second, it exploits the NBER Economic Supplement of the Longitudinal Study on Aging to investigate the role of housing and financial wealth in the choice of living arrangements.

Living Arrangements: Health and Wealth Effects

Abstract: This paper investigates the choice of living arrangements among elderly Americans. It has two specific aims. First, because health is not directly measurable and can only be described by indicators such as ADLs and IADLs, it explores a new econometric approach to model the influence of the latent health status on living arrangements. Second, it exploits the NBER Economic Supplement of the Longitudinal Study on Aging to investigate the role of housing and financial wealth in the choice of living arrangements.

The Method of Simulated Scores for the Estimation of LDV Models

Abstract: The method of simulated scores (MSS) is presented for estimating limited dependent variables models (LDV) with flexible correlation structure in the unobservables. We propose simulators that are continuous in the unknown parameter vectors, and hence standard optimization methods can be used to compute the MSS estimators that employ these simulators. The first continuous method relies on a recursive conditioning of the multivariate normal density through a Cholesky triangularization of its variance-covariance matrix. The second method combines results about the conditions of the multivariate normal distribution with Gibbs resampling techniques. We establish consistency and asymptotic normality of the MSS estimators and derive suitable rates at which the number of simulations must rise if biased simulators are used.

Simulation of Multivariate Normal Rectangle Probabilities and their Derivatives: Theoretical and Computational Results

Abstract: An extensive literature in econometrics and in numerical analysis has considered the problem of evaluating the multiple integral P(B; μ, Ω) = ∝ a b n(v − μ, Ω)dv ≡ E v 1(V ϵ B) , where V is a m-dimensional normal vector with mean μ, covariance matrix Ω, and density n(v − μ, Ω) , and 1(V ϵ B) is an indicator for the event B = (V¦a . A leading case of such an integral is the negative orthant probability, where B = (V ¦V . The problem is computationally difficult except in very special cases. The multinomial probit (MNP) model used in econometrics and biometrics has cell probabilities that are negative orthant probabilities, with μ and Ω depending on unknown parameters (and, in general, on covariates). Estimation of this model requires, for each trial parameter vector and each observation in a sample, evaluation of P(B; μ, Ω) and of its derivatives with respect to μ and Ω. This paper surveys Monte Carlo techniques that have been developed for approximations of P(B; μ, Ω) and its linear and logarithmic derivatives, that limit computation while possessing properties that facilitate their use in iterative calculations for statistical inference: the Crude Frequency Simulator (CFS), Normal Importance Sampling (NIS), a Kernel-Smoothed Frequency Simulator (KFS), Stern's Decomposition Simulator (SDS), the Geweke-Hajivassiliou-Keane Simulator (GHK), a Parabolic Cylinder Function Simulator (PCF), Deak's Chi-squared Simulator (DCS), an Acceptance/Rejection Simulator (ARS), the Gibbs Sampler Simulator (GSS), a Sequentially Unbiased Simulator (SUS), and an Approximately Unbiased Simulator (AUS). We also discuss Gauss and FORTRAN implementations of these algorithms and present our computational experience with them. We find that GHK is overall the most reliable method.