to prepare and allow a large number of options. The program can be run in batch mode or interactively at a computer terminal. Computer core storage is dynamically allocated so that large problems are only limited by the size of the machine. SHAZAM is designed to grow so that new algorithms and procedures can easily be added by any programmer familiar with the internal structure of the program. Features of SHAZAM include ordinary least squares, two-stage least squares, seemingly unrelated regressions and iterative estimation of seemingly unrelated regressions, threestage least squares and iterative three-stage least squares, models with first and second order autocorrelated disturbances, estimation of Box-Cox [1] type nonlinear functional forms, principal components and factor analysis, regression on principal components, ridge regression, regressions by matrix decompositions, random number generatign for Monte Carlo samples, forecasting, and plotting. Any set of linear restrictions or hypothesis tests can be used in the estimation. A wide variety of output statistics are available with each procedure. The autocorrelation section of SHAZAM is rather extensive and includes maximum likelihood or least squares estimation by a grid search or iterative Cochrane-Orcutt [2] procedure and inclusion or deletion of initial observations, exact and higher-order DurbinWatson [4] type tests, tests based on Golub's [6] uncorrelated residuals, Dhrymes [3, p. 199] corrections for lagged dependent variables, Savin-White [7] corrections for missing observations in a time series, Savin-White [8] type simultaneous testing for functional form and autocorrelation, and forecasting using Goldberger's [5] best linear unbiased predictor. A SHAZAM user's manual [9], which is also machine readable, is available from the author on request.

[A gap is filled in a proof of a central limit theorem, for regression in a time series context, which has been used in some econometric theory.]

Using the result that under the null hypothesis of no misspecification an asymptotically efficient estimator must have zero asymptotic covariance with its difference from a consistent but asymptotically inefficient estimator, specification tests are devised for a number of model specifications in econometrics. Local power is calculated for small departures from the null hypothesis. An instrumental variable test as well as tests for a time series cross section model and the simultaneous equation model are presented. An empirical model provides evidence that unobserved individual factors are present which are not orthogonal to the included right-hand-side variable in a common econometric specification of an individual wage equation.

[This paper deals with the problem of computing optimal strategies in a model of intertemporal choice under uncertainty for a competitive firm. The paper presents a method for computing the optimal strategies for (i) investment, (ii) production, (iii) financing, and (iv) consumption or dividends when (i) the entrepreneur's utility function displays constant absolute risk aversion, (ii) the production technology is certain and of the activity analysis variety, and (iii) prices are uncertain and serially independent. It is shown that the task of finding the optimal investment and production strategies reduces to a concave programming problem while the optimal financing and consumption strategies can be computed by analytical methods after the investment and production strategies are obtained. When prices are normally distributed, the optimal production and investment strategies can be found by means of quadratic programming.]

DESPITE THE INTENSE ACTIVITY in the area of estimation of limited dependent variable (LDV) models (see, for example, the Fall 1976 issue of the Annals of Economic and Social Measurement), the interpretation of regression coefficients in truncated regression models has been largely ignored. This issue should not be taken lightly since the obvious interpretation which equates regression coefficients to partial derivatives of the conditional mean of the dependent variable is unfortunately incorrect. One specific implication of the results presented here is that whenever the dependent variable y in the usual classical linear normal regression model is truncated above and/or below, then the effect of the fth regressor on the conditional mean of y is proportional to, but not equal to, the fth regression coefficient. However, more generally this note presents a simple expression for the effect of the fth regressor on any conditional moment of y in terms of a generalized LDV model introduced by Poirier [3]. Poirier introduced a general LDV model which permitted skewness in a pre-truncated variable by transforming it within the class of transformations suggested by Box and Cox

Darius J. Conger, John M. Campbell, Jr., Simultaneity in the Birth Rate Equation: The Effects of Eduction, Labor Force Participation, Income and Health, Econometrica, Vol. 46, No. 3 (May, 1978), pp. 631-641

The conditions under which aggregate CES demand behavior is consistent with polytomous choice by micro demanders are explored. Several special cases are treated in which either the relative efficiency or the relative input price is assumed to vary randomly over the micro units. In each case it is shown that the random variable must have either a log-logistic (Burr) distribution function or a generalization thereof if the aggregate and the micro behavior are to be consistent.