This paper uses an agency theory model in which the agent's actions are multi‐dimensional to analyze the optimal weights to apply to performance measures in a compensation contract. We show how the optimal contract trades off the congruity of the overall performance measure with the desire to minimize the risk imposed upon the agent. In contrast to the single action case, we find that an increase in the sensitivity of a performance measure to an agent's action does not necessarily increase the weight placed on that performance measure, even if that measure is perfectly congruent with the firm's outcome.

Jeffrey Cohen, Thomas Kida, The Impact of Analytical Review Results, Internal Control Reliability, and Experience on Auditors' Use of Analytical Review, Journal of Accounting Research, Vol. 27, No. 2 (Autumn, 1989), pp. 263-276

We propose an extensive-form solution concept, with players who neglect information from hypothetical events but make inferences from observed events. Our concept modifies cursed equilibrium (Eyster and Rabin 2005) and allows that players can be cursed about endogenous information.

Journal Article Employment and Industrialization: Comment Get access Benjamin I. Cohen, Benjamin I. Cohen Harvard University Search for other works by this author on: Oxford Academic Google Scholar Nathaniel H. Leff Nathaniel H. Leff Harvard University Search for other works by this author on: Oxford Academic Google Scholar The Quarterly Journal of Economics, Volume 81, Issue 1, February 1967, Pages 162–164, https://doi.org/10.2307/1879682 Published: 01 February 1967

Introduction, 112. — Theory construction (model building), 113. — General characteristics of computer models, 115. — Comparison of computer models with operations research simulations and econometric models, 117. — Methodological problems of computer models, 119. — Review of the literature, 121. — Future of computer models, 126.

Abarbanell and Lehavy provide evidence that analysts’ forecast errors are not normally distributed exhibiting a high occurrence of extreme negative forecast errors (left-tail asymmetry) and a high occurrence of small positive forecast errors (middle asymmetry). This is important for researchers who rely on techniques that are sensitive to the distributional assumptions of analysts’ forecast errors. Many of the conclusions drawn by Abarbanell and Lehavy, however, are based on visual impressions (as opposed to formal empirical tests) or based on methods that are very sensitive to the empirical methods used (e.g., whether the serial correlation of forecast errors is caused by the left-tail asymmetry).

Inventories, LIFO, Inventories orders, Tax liabilities

In the February 1967 issue of the Review we presented estimates of a relationship between changes in help-wanted advertising normalized for growth in the labor force, and changes in the unemployment rate, and new hires, and a dummy variable reflecting the phase of the business cycle [11]. Two notes by Burch and Fabricant [2] and Gujarati [3] have expanded upon our findings. Each paper presents an alternative model and finds that in its own model the relationship between the unemployment rate and the amount of help-wanted advertising is not stable over the period 1951 through 1966 or 1968. The Burch-Fabricant paper tests for the shift in the coefficient of the reciprocal of the unemployment rate before and after 1957. The Gujarati paper tests for the difference in the slope of the unemployment coefficient in ten different business cycle phases from 1951 to 1968. Both papers seem to explain a greater fraction of the variance of the dependent variable than does our model. However, our dependent variable is the change in the normalized help-wanted advertising index; in the other two papers it is the level. By the usual standards, our ]R2 of 0.82 is at least as good as the somewhat higher correlations produced in the level equations. When the two tests suggested respectively by Burch and Fabricant, and Gujarati were applied to our model, the coefficient of the unemployment rate change variable proved to be stable. We first added a variable which is zero up to 1957 and equal to the change in the unemployment rate after 1957; in effect, the regression coefficient of AU is allowed to change its value in 1957. If this variable is estimated along with the other variables in our model, a simple t test will indicate whether the coefficient did in fact change after 1957. Our original equation for the period 1951-1966, second quarter, is reproduced as (1) below. The estimated equation with the dummy variable added is shown as (2):