Nicholas J. Gonedes

Differencing of random walks and near random walks

Abstract The traditional rationale for differencing time series data is to attain stationarity. For a nearly non-stationary first-order autoregressive process—AR (1) with positive slope parameter near unity—we were led to a complementary rationale. If one suspects near non-stationarity of the AR (1) process, if the sample size is ‘small’ or ‘moderate’, and if good one-step-ahead prediction performance is the goal, then it is wise to difference the data and treat the differences as observations on a stationary AR (1) process. Estimation by Ordinary Least Squares then appears to be at least as satisfactory as nonlinear least squares. Use of differencing for an already stationary process can be motivated by Bayesian concepts: differencing can be viewed as an easy way to incorporate non-diffuse prior judgement—that the process is nearly non-stationary—into ones analysis. Random walks and near random walks are often encountered in economics. Unless ones sample size is large, the same statistical analyses apply to either.

The Significance of Selected Accounting Procedures: A Statistical Test

The purpose of this paper is to present the results of a statistical test of the significance of using or not using each of three accounting procedures. The procedures which constitute the axial considerations of this paper include two measurement procedures and one reporting procedure; they are: amortization of the investment credit, interperiod tax allocation, and the presentation of funds flow statements in annual reports. The basic issue to which this investigation is related is the influence of alternative accounting procedures on the decisions of persons who are assumed to be users of accounting messages. The predications of some members of the accounting community suggest that alternative accounting procedures may produce different decisions. For example, the Committee to Prepare a Statement of Basic Accounting Theory remarked: