Knowledge that Transforms

This empirical study presents an analysis of mode choice for selected urban trips in the San Francisco Bay area. The economic model is a restricted consumer choice model, where the mutually exclusive collectively exhaustive choice is between auto driver and transit passenger. The main testable hypothesis is that, in the absence of knowledge about the value a traveler attaches to his time, if a choice exists and if a mode is cheaper than the alternative in terms of both time and money, it should be chosen. The hypothesis was subjected to empirical analysis and, within the limits of the data, appeared to be a good approximation to reality. The statistical model used to further investigate the data was discrimination-classification analysis. The discriminant function can be interpreted as an indifference hypersurface of the indirect or constrained utility function. The statistical theory underlying the three versions of the model used is presented along with a derivation of the elasticity of choice. The data were a subset of the Bay Area Transportation Study Commission origin and destination home interview data merged with interzonal travel times and costs of both modes. Trips were stratified by purpose. Elasticities were calculated and compared both within a purpose by different variables and between purposes for each variable. Some potential policy changes were treated in the context of the model and compared with results of other studies.

[This paper is concerned with the econometric problems associated with estimating supply and demand schedules in disequilibrium markets. The general problem is that in the absence of an equilibrium condition the ex ante demand and supply quantities cannot in general be equated to the observed quatity traded in the market. Four methods of estimation, differing primarily in their use of information on price-setting behavior, are developed in this paper. The first method is a generalization of an earlier meothd developed by R. Quandt and is based upon the maximization of a likelihood function. The method does not require any specific assumption about price-setting behavior, and it allows the sample separation (into demand and supply regimes) to be estimated along with the coefficient estimates. The second and third methods use the change in price as a qualitative proxy in determining the sample separation. The fouth method uses the change in price as a quantitative proxy for the amount of excess demand (supply) in the market. In the final section of the paper the four methods are used to estimate a a model of the housing and mortgage market in an effort to gauge the potential usefulness of each of the methods.]

USUAL SPECIFYING ASSUMPTIONS for simultaneous equation econometric models imply strong inequality constraints on structural parameters which are formally similar to those encountered in connection with the classical errors-in-the-variables model.2 To illustrate, consider the following simple simultaneous equation model for two endogenous variables, Y,, and Y2t, say price and quantity, respectively:

[This paper contains the derivation of the exact finite sample distribution function of the LIML identifiability test statistic associated with an overidentified nondynamic structural equation which contains two endogenous variables. Some of the properties of the moments of this statistic are also investigated.]

[This paper deals with two single-equation estimators in a set of simultaneous linear stochastic equations--namely, ordinary least squares (OLS) and two-stage least squares (2SLS). Under the assumption that all predetermined variables in the model are exogenous, necessary and sufficient conditions are obtained for the existence of even moments of the above estimators. It is shown that for the general case with an arbitrary number of included endogenous variables, even moments of the 2SLS estimator are finite if and only if the order is less than K2 - G1 + 1. Furthermore, even moments of the OLS estimator exist if and only if the order is less than N - K1 - G1 + 1, where N is the sample size, G1 + 1 is the number of included endogenous variables, K1 and K2 respectively are the number of included and excluded exogenous variables in the equation to be estimated.]