Knowledge that Transforms

Preference relations over two-stage lotteries are analyzed. Empirical evidence indicates that decisionmakers do not always behave in accordance with the reduction of compound lotteries axiom, but they seem to satisfy a compound independence axiom. Although the reduction and the compound independence axioms, together with continuity, imply expected utility theory, each of them by itself is compatible with all possible preference relations over simple lotteries. Using these axioms, the author analyzes three different versions of expected utility for two-stage lotteries. The author suggests several different compound dominance axioms as possible replacements of the reduction axiom, which are strictly weaker than the reduction of compound lotteries axiom. Copyright 1990 by The Econometric Society.

New results on the exact small sample distribution of the instrumental variable estimator are presented by studying an important special case. The exact closed forms for the probability density and cumulative distribution functions are given. There are a number of surprising findings. The small sample distribution is bimodal. with a point of zero probability mass. As the asymptotic variance grows large, the true distribution becomes concentrated around this point of zero mass. The central tendency of the estimator may be closer to the biased least squares estimator than it is to the true parameter value. The first and second moments of the IV estimator are both infinite. In the case in which least squares is biased upwards, and most of the mass of the IV estimator lies to the right of the true parameter, the mean of the IV estimator is infinitely negative. The difference between the true distribution and the normal asymptotic approximation depends on the ratio of the asymptotic variance to a parameter related to the correlation between the regressor and the regression, error. In particular, when the instrument is poorly correlated with the regressor, the asymptotic approximation to the distribution of the instrumental variable estimator will not be very accurate.

This paper endogenizes the frequency of major discoveries and the extent of their refinement.Four axioms deliver a one-parameter family of beliefs that guide exploratory effort.Such effort trades off the prospect of major new discovery against the chance of successfully refining discoveries made in the past.The only other parameter is the cost of making new discoveries relative to the cost of refining old ones.The paper derives time-series properties of inventive activity as they relate to the two parameters, and it discusses several specific inventions and their subsequent refinement.In doing so, the paper arguably enhances our understanding of the process of discovery.1 We thank the C. V. Starr Center for Applied Economics for technical and financial assistance.The second

This paper defines and tests a form of market efficiency called market dexterity which requires that asset prices adjust instantaneously and completely in response to new information. Examining the behavior of the yen/dollar exchange rate while each of the major markets are open it is possible to test for informational effects from one market to the next. Assuming that news has only country specific autocorrelation such as a heat wave. any intra-daily volatility spillovers (meteor showers) become evidence against market dexterity. ARCH models are employed to model heteroskedasticity across intra-daily market segments. Statistical tests lead to the rejection of the heat wave and therefore the market dexterity hypothesis. Using a volatility type of vector autoregression we examine the impact of news in one market on the time path of volatility in other markets.

Following Jureckova (1981) we introduce a finite-sample measure of performance of regression estimators based on tail behavior.The least squares estimator is studied in detail, and we find that it may achieve good tail performance under strictly Gaussian conditions.However, the tail performance of the least-squares estimator is found to be extremely poor in the case of heavy-tailed error distributions or when leverage points are present.Further analysis of the least-squares estimator with light-tailed errors indicates the strong influence of the design matrix in determining tail performance.Turning to the tail behavior of various robust estimators of the parameters of the linear model, we focus on tail performance under heavy (algebraic) tailed errors.The /^estimator is seen to be a leading case: we find a simple characterization of its tail behavior in terms of the design configuration and show that a broad class of M-estimators have the same performance.Perhaps most significantly, it is shown that our finite-sample measure of tail performance is, for heavy tailed error distributions, essentially the same as the finite sample concept of breakdown point introduced by Donoho and Huber(1983).This finding provides an important probabilistic interpretation of the breakdown point and clarifies the role of tail behavior as a quantitative measure of robustness.This link is further explored for high-breakdown regression estimators including Rousseeuw's (1982) least-median-of-squares estimator.

ABSTRACT Analyses of rational speculation usually presume that it dampens fluctuations caused by “noise” traders. This is not necessarily the case if noise traders follow positive‐feedback strategies—buy when prices rise and sell when prices fall. It may pay to jump on the bandwagon and purchase ahead of noise demand. If rational speculators' early buying triggers positive‐feedback trading, then an increase in the number of forward‐looking speculators can increase volatility about fundamentals. This model is consistent with a number of empirical observations about the correlation of asset returns, the overreaction of prices to news, price bubbles, and expectations.