3,115 research outputs found
A SIMPLE PROCEDURE TO EVALUATE EX-ANTE PRODUCER WELFARE UNDER PRICE UNCERTAINTY
We propose a simple and tractable procedure for evaluating producer welfare under price uncertainty. These properties are achieved at the cost of assuming constant absolute risk aversion, where risk attitude depends on the stock of wealth but not on the flow of income. Numerical examples corroborate the procedure's properties; the validity of the constant absolute risk aversion case as an approximation is discussed.Research Methods/ Statistical Methods,
ON TESTING FOR REVEALED PREFERENCE CONDITIONS
A procedure to test for the significance of violations of revealed preference conditions is described. The procedure is simple and hence may especially be appropriate for large data sets. An application to consumption data is presented.Research Methods/ Statistical Methods,
GROUNDWATER CONTAMINATION AND THE MANAGEMENT OF A CONJUNCTIVE GROUND AND SURFACE WATER IRRIGATION SYSTEM
Irrigation water (including rainfall) that infiltrates the subsurface carries salts, pesticide and fertilizer residues, and other trace elements, thus causing a contamination of aquifers and soils. A similar situation occurs when irrigating with saline groundwater (aquifers containing saline water often are found in arid and semi-arid regions, where agricultural production depends critically on groundwater irrigation). Evaporation of the irrigation water increases salt concentration, causing salinization of soils and aquifers. Although not immediately noticeable, these quality deterioration processes will have long-term effects and therefore require careful management. The paper describes a general framework for the intertemporal management of a conjunctive ground and surface water irrigation system, taking into account the quality deterioration processes. Policy implications are discussed and the results are compared with those that come from a model which neglects quality effects.Resource /Energy Economics and Policy,
The Power of an Example: Hidden Set Size Approximation Using Group Queries and Conditional Sampling
We study a basic problem of approximating the size of an unknown set in a
known universe . We consider two versions of the problem. In both versions
the algorithm can specify subsets . In the first version, which
we refer to as the group query or subset query version, the algorithm is told
whether is non-empty. In the second version, which we refer to as the
subset sampling version, if is non-empty, then the algorithm receives
a uniformly selected element from . We study the difference between
these two versions under different conditions on the subsets that the algorithm
may query/sample, and in both the case that the algorithm is adaptive and the
case where it is non-adaptive. In particular we focus on a natural family of
allowed subsets, which correspond to intervals, as well as variants of this
family
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