Knowledge that Transforms

[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.]

IN THEIR ARTICLE [5], Liu and Breen propose a new estimator of the large-sample asymptotic covariance matrix for the limited information maximum likelihood estimator in simultaneous equations, and express surprise that their estimator is different from the estimator proposed by Chernoff and Divinsky [1]. Additionally, they question the interpretation of a statistic used in the past to test over-identifying restrictions.

IN THEIR PAPER [1] with the above title, Blattberg and Sargent have suggested a method for estimating regression parameters when disturbances are generated by a symmetric stable law. This procedure was originally suggested by John Wise [4]. Wise arrived at this estimator by confining attention to the class of linear unbiased estimators and minimizing the scale parameter. He also suggested that his procedure might be considered as a generalization of the classical least squares procedure which fails when the second moments of the disturbances do not exist. Blattberg and Sargent have compared various estimators by using simulated data. The main purpose of this note is to show that the procedure suggested by Wise and Blattberg and Sargent has an optimal property, namely that it yields an estimator that has minimum mean absolute error among the class of all linear unbiased estimators. The model is the well-known linear regression model given by