Book review, Inventory valuation

Inventory calculation procedures may be viewed as a set of linear transformations or functions which map the appropriate price and quantity vectors into a corresponding set of valuation scalars. This paper examines some of these calculations in terms of elementary matrix operations. The matrix can be readily adapted to the computer, and standard programs now exist for performing matrix algebra operations. These can be used for the required accounting calculations discussed herein. First, we consider purchase transactions; each such transaction may be described by a pair of points representing quantity and unit price, respectively. The purchase quantities, by years, may be represented by a diagonal matrix having, as the elements of its main diagonal, the quantities purchased during each accounting period. Such a matrix, Q, with typical element qji, for n accounting periods would appear as follows:

level, the generalizations which emerge will have correspondingly great power as principles, predictors, or guides. The task is by no means simple, and Mattessich properly disclaims belief in the perfection of the result (p. 32). First of all, some comments on generality. Although he seeks to formulate the assumptions in the most general terms, all the illustrative and explanatory comment on the assumptions, with one or two minor exceptions, has reference to business entities. It would have been of interest to have some illustration of the counterparts of the examples given, or specific examples of the interpretation to be given to the assumptions and their component terms, in the case of service organizations and economies as wholes. One may well suspect that the absence of illustration is evidence that, after all, private accounting and national accounting may not be considered as of the same class. The generality of the statements of the assumptions is indicated in each case by the formula exists.. . or some equivalent indicative. Thus, the assumption of monetary values reads: There exists a set of additive values, expressed in a monetary unit; this set is isomorphic to the system of (positive and negative) integers plus the number zero (p. 32). And to take an example of the class of assumptions which are described as place-holders: There exists a set of hypotheses determining the value assigned to an accounting transaction (p. 42). A system built up on such pure postulates escapes many of the difficulties which arise if the postulates are to be tied to statements of function. A hypothesis is itself functional; we formulate hypotheses to serve our ends. It seems This content downloaded from 207.46.13.131 on Sun, 16 Oct 2016 05:11:45 UTC All use subject to http://about.jstor.org/terms

Estate accounting, Financial reporting audiences, Equity reporting

One of the difficulties of evaluating formula plans by testing them in the light of past experience is the fact that there is no index of bond experience which simulates typical portfolio performance. Bond indices are stated in terms of yields with constant maturities, but the capital values of bonds vary according to their maturities given the same yield. Cottle and Whitman used elaborate techniques to make their bond portion of the total portfolio realistic, but were only partially successful. This writer also attempted to eliminate the problem of simulating the bond portfolio in a previous paper by assuming that the defensive portion of the portfolio was invested in a Savings and Loan account receiving the national average return. This procedure thus ignored the problem of bond maturities and variation in capital values due to changes in interest rates.

The sampling properties of the usual estimator of marginal product from a CobbDouglas production function are studied under the assumption of normally distributed errors. The asymptotic normality of these estimators is demonstrated and certain special cases are considered for which the asymptotic distribution is particularly simple. An alternative estimator of marginal product is suggested which has both a smaller bias and greater precision than the usual estimator. Estimators of the variance-covariance matrix of all marginal product estimators mentioned are given. These are accurate to order n-1. A GREAT deal of attention is given nowadays to the statistical properties of estimators of parameters in simultaneous equations models. By contrast, little attention is given to the statistical properties of estimators of parameters in simpler economic models. One such model that has been virtually ignored by statisticians is the Cobb-Douglas production function. The usefulness of this function in the cross section analysis of an industry should not be underrated although, as with all models, care must be taken to ensure that the data to which the function is fitted correspond reasonably well with the assumptions, both economic and statistical, implied by the function. The purpose of the present paper is to consider some of the statistical properties of estimators of marginal product obtained from a CobbDouglas function. Section 2 will discuss the estimator most frequently used (see, for example, Tintner and Brownlee [8] and Heady [4]). The third section will discuss alternative estimators which reduce bias to order n-2 where n is the size of sample. These alternative estimators may be useful when the sample size is small and the variability of observed production about the fitted Cobb-Douglas function is large. The fourth section considers the problem of estimating the variance matrices of the estimators discussed in Sections 2 and 3. A numerical illustration is given in Section 5.