Additive and Multiplicative Uncertainty Revisited: What Explains the Contrasting Biases?

above the certainty level while for additive uncertainty the price should be lower than the certainty level. This note gives an intuitive explanation for the result after first presenting a parsimonious review of the two models. We also discuss which, if either, of the two models is more realistic. Received December 14, 2001; revised version received July 16, 2002 Published online: April 30, 2003 We thank referees for helpful comments. Ciaran Driver would like to acknowledge the research facilities from ANU, Canberra for their help in writing this paper.     

Corporate Board Composition, Protocols, and Voting Behavior: Experimental Evidence

We examine voting by a board designed to mitigate conflicts of interest between privately informed insiders and owners. Our model demonstrates that, as argued by researchers and the business press, boards with a majority of trustworthy but uninformed “watchdogs” can implement institutionally preferred policies. Our laboratory experiments strongly support this conclusion. Our model also highlights the necessity of penalties on insiders when there is dissension among board members. However, penalties for dissent appeared to have little impact on the experimental outcomes.     

Using an approximate kiefer-weiss solution for testing insect population densities

Summary A sequential testing procedure called 2-SPRT for the meanμ of the negative binomial distribution with known exponentk is presented. For any fixedμ ₀(μ ₁<μ ₀<μ ₂), the 2-SPRT provides an asymptotic solution to the modified Kiefer-Weiss problem. Determination ofμ ₀ such that the test provides an approximate solution to the Kiefer-Weiss problem is described. The behavior of the 2-SPRT and the Sequential Probability Ratio Test is investigated using Monte Carlo methods. The error probabilities and the average sample numbers are compared. All the computations were carried out on the Alabama Supercomputer.     

VAR,ERROR CORRECTION AND PRETEST FORECASTS AT LONG HORIZONS

This paper focuses on the construction of forecasts over long horizons where a typical long-horizon forecast might span four years using 20 to 40 years’ data. It is argued that the presence of persistence in the form of unit or near-unit autoregressive roots poses substantial difficulties for long-horizon interval and point forecasting. These difficulties may not be overcome even by efficient pre-testing or model-selection procedures and might, in general, lead to point forecasts with large asymptotic root mean squared errors and undesirably wide prediction intervals.