Recently, Lusztig and Schwab [5] drew attention to a problem which occurs when applying linear programming methods to portfolio selection where capital is rationed over several investment periods. The problem is to determine the relevant discount rate with which to calculate the net present values of the objective function when this same rate is dependent upon the optimal solution to the linear program. Lusztig and Schwab suggested a neat procedure by which it was claimed that one could overcome this difficulty and which also does not rely upon the measurement of subjective utility as did the Baumol and Quandt approach [2]. It was decided to test the Lusztig and Schwab model (afterwards called the L-S procedure for brevity) on a hypothetical problem, and doing so resulted in two conclusions:

(a) The L–S procedure, as described, is incomplete but with small modification may be useful; however,

(b) concentration upon the discount rate problem in isolation from other capital budgeting problems may well be a pointless exercise.

This paper is concerned with the validity of the conventional t tests on regression coefficients when there is serious multicollinearity between the explanatory variables. It is well known that increasing multicollinearity causes the true standard errors of regression coefficients to rise. The crucial question, however, is whether the conventional formulas will in practice reflect this rise. The purpose of this note is to show that the conventional t tests will in practice reflect this rise. But this note also points out the danger involved in mechanically dropping variables from multiple regression equations by t tests because t values of the regression coefficients may not be significantly different from zero when the true (population) values of these coefficients are in fact not zero, if the explanatory variables are highly intercorrelated.

This paper discusses the use of simulation as a means of studying the operations of securities markets. To place simulation's role in the proper context, Section I begins with a review of public policy, research, and teaching considerations that have combined in recent years to create a growing need to improve our understanding of the operations of these markets. Following this is a brief discussion of the limitations of traditional price theory models to meet this need. Section II demonstrates the significant, yet largely untapped, potential of simulation in this regard.

This paper describes ways in which computers can be used to help teach elementary college economics. Most of the methods can be implemented with present-day equipment; the rest will prove feasible within a few years. Examples given here are not intended to provide a full or even a representative menu for a beginning course; they simply illustrate some of the more interesting possibilities.

It is often necessary to estimate a frequency function or certain points on a frequency function from very limited data. A usual procedure for this estimation involves two steps. From the set of “well-known” frequency functions, e.g., the normal, poisson, binomial, etc., one chooses that function which seems likely to best “fit” and then uses the available data to estimate the parameters of the chosen distribution. If no one “well-known” function can be chosen a priori, then perhaps several likely candidates are tried and the one which fits best according to some criterion is chosen. For many purposes, this procedure is quite unobjectionable.

In our earlier note, we drew attention to the problem of interdependency between the opportunity cost used as a discount rate in determining the net present values of the objective function and the optimal solution of a linear program. In expanding on our article, Lockett and Tompkins (L and T) rightly point to the need for an appropriate definition of opportunity costs.

Recently, several researchers, including Evans, Archer [1], Latané, and Young [2], have performed empirical analyses of the relationship between the number of securities in a portfolio and the reduction in portfolio dispersion. In this note, an exact mathematical relationship between these two factors is presented.

Presence of a variable market demand function for a given product has significant implications for factor input levels and for the resulting production costs to the firm. In a recent paper, McKean argues that in order to describe the costs of producing a product subject to fluctuating demand it is necessary to take into account the entire distribution of outputs as it relates to the static total cost function. While it is evident that influences on costs and factor inputs will differ in the case of a fluctuating demand schedule compared to the conventional stable demand conditions of classical micro theory, it is not clear that the use of a static cost function in conjunction with a probability distribution of outputs is the proper framework in which to examine the problem.

In a recent article, Professors Robichek and Van Horne have noted the importance of considering abandonment in the capital budgeting process. The basic point of their paper is that:

… a project should be abandoned at that point in time when its abandonment value exceeds the net-present value of the subsequent expected future cash flows discounted at the cost-of-capital rate.

This paper proposes a model that a dealer or investor may employ in determining

• The potential investment value of a debt instrument,

• The potential gains in net after-tax yield which result from swaps, and

• The trade-off, effective, after-tax yield on a municipal vs a taxable corporate bond of the same quality or rating.