This paper reports the testing of hypotheses concerning: (i) whether the household is better viewed as planning over a single-period versus a multiperiod horizon; (ii) whether the household is better viewed as planning in a single-asset or a multiasset framework; (iii) the relative importance of substitution and wealth effects as sources of change in the stock demand for automobiles. The findings are that a multiperiod, multiasset model best describes stock demand, that the separation theorem which implies a zero wealth effect is rejected, and that substitution effects are seven times more important than wealth effects. THE ECONOMIC LITERATURE CONTAINS several empirical studies of household automobile demand [3, 7, 8, and 10] and theoretical models of the household [1, 4, 5, 6, and 14] which are or could be applied to automobile demand. Two aspects of theory which are not fully reflected in the empirical studies are the implications of a multiperiod horizon and the possibility of substitution among assets. Theoretical models [5 and 15] which assume a multiperiod horizon imply that relevant asset prices are user costs and the appropriate constraint is wealth. In contrast, most empirical studies use purchase prices rather than user costs, and income rather than wealth. In addition, theoretical models [4 and 5] permit substitution over a variety of goods, whereas most empirical studies restrict substitutions to automobiles and consumption goods. To the extent that estimated equations are misspecified, the prevailing conclusion that income effects are more important than substitution effects may be due to left-out-variable bias. This paper investigates each of these three issues-the length of the horizon, the range of substitutions, and the relative importance of substitution and wealth effects-by estimating over the same set of data a variety of alternative equations which reflect different assumptions about the horizon and range of substitutions. Initially, a multiperiod, multiasset model of the household consumption-saving decision is stated and used to derive the appropriate arguments for the broadest estimating equation. A linear approximation of this equation is estimated using quarterly United States data covering the years 1952-1972. Then this estimate is compared to competing equations derived under restrictions on the multiperiod, multiasset model. Specifically, demand equations derived under multiperiod, single-asset, single-period, single-asset, and single-period, multiasset assumptions are estimated and compared to the broadest multiperiod, multiasset equation. In addition, versions of the restricted equations which have appeared in the literature are estimated and compared. The findings are: (i) a multiperiod, multiasset equation best describes automobile stock demand, (ii) estimates of substitution and wealth effects are quite sensitive to specification bias, and (iii) substitution effects are seven times more important than wealth effects in the dominant equation.

In analyzing the city as an economic institution, it seems reasonable to ask if the advantages of proximity are sufficient to assure that traders will form and maintain a market place. This process is called agglomeration. A general theorem concerning iterative spatial games is developed first. A spatial general equilibrium model comprised of a sequence of pure trade economies is proffered. Restrictions on transport technologies sufficient to assure agglomeration are determined. The possibility of a policy maker speeding the process of agglomeration is demonstrated. In conclusion, the optimality properties of the model are discussed. The research draws heavily on the works of A. Weber [8] and G. Debreu [2]. The model includes a dynamic adjustment process which is developed from individuals' maximizing behavior.

Edgeworth series expansions are obtained of the finite sample distributions of the least squares estimator and the associated t ratio test statistic in the context of a first-order noncircular stochastic difference equation. General formulae are given for these expansions up to 0(Th1) where T is the sample size and explicit representations of these in terms of the true parameters are derived up to 0(12). Some numerical comparisons of the approximations and the exact distributions are made in the case of the least squares estimator.

The Lorenz curve relates the cumulative proportion of income units to the cumulative proportion of income received when units are arranged in ascending order of their income. In the past the curve has been mainly used as a convenient graphic device to represent the size distribution of income and wealth. In this paper the Lorenz curve technique is used as a tool to introduce distributional considerations in economic analysis. The concept has been extended and generalized to study the relationships among the distributions of different economic variables. The generalized Lorenz curves are called concentration curves and the Lorenz curve is only a special case of such curves, the concentration curve for income. Section 2 of the paper gives the derivation of the Lorenz curve. Section 3 provides some theorems relative to the concentration curve of a function and its elasticity, which provide the basis for studying relationships among the distributions of different economic variables. Section 4 discusses applications of the theorems.

P. C. B. Phillips, A General Theorem in the Theory of Asymptotic Expansions as Approximations to the Finite Sample Distributions of Econometric Estimators, Econometrica, Vol. 45, No. 6 (Sep., 1977), pp. 1517-1534

[Social choice lottery rules are analyzed for two-candidate elections with voters who may be uncertain about whom they prefer. A voter's uncertainty is reflected by a nonobservable choice probability of voting for candidate A rather than candidate B, given that he votes. Lottery rules are based on the votes for A and B; they are to be monotonic and symmetric in voters and in candidates. Given n voters, all lottery rules are convex combinations of about n/2 basic rules ranging from the coin-flip rule to simple majority. Candidate A's win probability and two measures of expected voter satisfaction are examined as functions of the individuals' choice probabilities and the lottery rules. Comparisons are made between simple majority and the proportional lottery rule which assigns social choice probability of j/n to A when A gets j of n votes. Each of simple majority and the proportional lottery rule satisfies attractive properties that are not satisfied by the other rule.]

Charles J. Cicchetti, Anthony C. Fisher, V. Kerry Smith, An Econometric Evaluation of a Generalized Consumer Surplus Measure: The Mineral King Controversy, Econometrica, Vol. 45, No. 4 (May, 1977), p. 1041