Probabilistic depreciation is a method of determining the proper depreciation charge in each year of an asset's service life, when the service life is a random variable with known distribution. The paper discusses how the service life distribution is modified as more information is obtained about the actual lifetime of the asset. The problem of determining the proper amount to be charged each year to depreciation while at the same time maintaining the proper balance in the accumulated depreciation account is considered. The analysis is done both for a single asset case and for group depreciation. A final section discusses the use of Bayesian analysis for estimating the particular form of the service life distribution while the assets are in service.
At the request of Edwin N. Hurley, then Vice-Chairman and later Chairman of the Federal Trade Commission, the President of the American Association of Public Accountants (predecessor of the American Institute of Accountants), J. Porter Joplin, appointed a special committee to confer with the trade commission on all questions of accounting. Robert H. Montgomery was Chairman of the eight-member committee. The most important accomplishment of the committee was the promulgation of a programme for audit procedure which was prepared at the request of the Federal Trade Commission, approved by the commission and transmitted to the Federal Reserve Board... . The audit program in its final form was unanimously approved by the members of the council.2 The Federal Reserve Board published the text in the Federal Reserve Bulletin (April 1, 1917) and reprinted it in pamphlet form in 1917 and again in 1918 for general distribution. The text also appeared in the Journal
The construction of tests of model specification is considered from a general point of view. The results are applied to testing the serial independence of the disturbances in a regression model where some of the regressors are lagged dependent variables. It is shown that the asymptotic distribution of the lag-1 serial correlation coefficient calculated from the least-squares residuals differs from that of the coefficient calculated from the true disturbances. A consequence of this is that tests of serial independence based on the residuals from regression on fixed regressors are invalid when applied to models containing lagged dependent variables even when the null hypothesis of serial independence is true. Tests which are asymptotically valid for the large-sample case are suggested.