Knowledge that Transforms

In a rationing problem, each agent demands a quantity of a certain commodity and the available resources fall short of total demand. A rationing method solves this problem at every level of resources and individual demands. We impose three axioms: Consistency—with respect to variations of the set of agents—Upper Composition and Lower Composition—with respect to variations of the available resources. In the model where the commodity comes in indivisible units, the three axioms characterize the family of priority rules, where individual demands are met lexicographically according to an exogeneous ordering of the agents. In the (more familiar) model where the commodity is divisible, these three axioms plus Scale Invariance—independence of the measurement unit—characterize a rich family of methods. It contains exactly three symmetric methods, giving equal shares to equal demands: these are the familiar proportional, uniform gains, and uniform losses methods. The asymmetric methods in the family partition the agents into priority classes; within each class, they use either the proportional method or a weighted version of the uniform gains or uniform losses methods.

We construct a quantitative equilibrium model with firms setting prices in a staggered fashion and use it to ask whether monetary shocks can generate business cycle fluctuations. These fluctuations include persistent movements in output along with the other defining features of business cycles, like volatile investment and smooth consumption. We assume that prices are exogenously sticky for a short time. Persistent output fluctuations require endogenous price stickiness in the sense that firms choose not to change prices much when they can do so. We find that for a wide range of parameter values, the amount of endogenous stickiness is small. Thus, we find that in a standard quantitative model, staggered price-setting, alone, does not generate business cycle fluctuations.

Conditions are derived for the consistency of kernel estimators of the covariance matrix of a sum of vectors of dependent heterogeneous random variables, which match those of the currently best-known conditions for the central limit theorem, as required for a unified theory of asymptotic inference. These include finite moments of order no more than 2 + for > 0, trending variances, and variables which are near-epoch dependent on a mixing process, but not necessarily mixing. The results are also proved for the case of sample-dependent bandwidths.

International audience

This paper introduces a new notion of consistency for social choice functions, called self-selectivity, which requires that a social choice function employed by a society to make a choice from a given alternative set it faces should choose itself from among other rival such functions when it is employed by the society to make this latter choice as well. A unanimous neutral social choice function turns out to be universally self-selective if and only if it is Paretian and satisfies independence of irrelevant alternatives. The neutral unanimous social choice functions whose domains consist of linear order profiles on nonempty sets of any finite cardinality induce a class of social welfare functions that inherit Paretianism and independence of irrelevant alternatives in case the social choice function with which one starts is universally self-selective. Thus, a unanimous and neutral social choice function is universally self-selective if and only if it is dictatorial. Moreover, universal self-selectivity for such functions is equivalent to the conjunction of strategy-proofness and independence of irrelevant alternatives or the conjunction of monotonicity and independence of irrelevant alternatives again.

OUR AIM IN THIS PAPER is to study the incentives to acquire information. We consider decision problems where the payoff has the single-crossing property and signals are affiliated with the unknown parameter. We introduce the notion of risk-sensitiity, and establish that the value of information is higher in decision problems that are more risk-sensitive. We apply this result to auctions: we are able to show that a first price auction induces more information acquisition than a second price auction. Consider a decision maker choosing an action a to maximize the expected value of a Ž. payoff function u , a . The decision maker does not observe , which he regards as a random variable V. Instead, he observes the realization of a random variable X ,a signal which conveys information about .W eassume that the decision maker can make X more informative, at a cost. Making X more informative increases the expected payoff. We analyze the returns to making X more informative. Ž. We focus on problems where X and V are affiliated, and u , a has the weak Ž. Ž .

We derive the asymptotic sampling distribution of various estimators frequently used to order distributions in terms of poverty, welfare, and inequality. This includes estimators of most of the poverty indices currently in use, as well as estimators of the curves used to infer stochastic dominance of any order. These curves can be used to determine whether poverty, inequality, or social welfare is greater in one distribution than in another for general classes of indices and for ranges of possible poverty lines. We also derive the sampling distribution of the maximal poverty lines up to which we may confidently assert that poverty is greater in one distribution than in another. The sampling distribution of convenient dual estimators for the measurement of poverty is also established. The statistical results are established for deterministic or stochastic poverty lines as well as for paired or independent samples of incomes. Our results are briefly illustrated using data for four countries drawn from the Luxembourg Income Study data bases.

Ultra-high-frequency data is defined to be a full record of transactions and their associated characteristics. The transaction arrival times and accompanying measures can be analyzed as marked point processes. The ACD point process developed by Engle and Russell (1998) is applied to IBM transactions arrival times to develop semiparametric hazard estimates and conditional intensities. Combining these intensities with a GARCH model of prices produces ultra-high-frequency measures of volatility. Both returns and variances are found to be negatively influenced by long durations as suggested by asymmetric information models of market micro-structure.

This paper develops asymptotic distribution theory for GMM estimators and test statistics when some or all of the parameters are weakly identified. General results are obtained and are specialized to two important cases: linear instrumental variables regression and Euler equations estimation of the CCAPM. Numerical results for the CCAPM demonstrate that weak-identification asymptotics explains the breakdown of conventional GMM procedures documented in previous Monte Carlo studies. Confidence sets immune to weak identification are proposed. We use these results to inform an empirical investigation of various CCAPM specifications; the substantive conclusions reached differ from those obtained using conventional methods.