Increasing Competition and the Winner's Curse: Evidence from Procurement

We assess empirically the effects of the winner's curse which, in common-value auctions, counsels more conservative bidding as the number of competitors increases. First, we construct an econometric model of an auction in which bidders' preferences have both common- and private-value components, and propose a new monotone quantile approach which facilitates estimation of this model. Second, we estimate the model using bids from procurement auctions held by the State of New Jersey. For a large subset of these auctions, we find that median procurement costs rise as competition intensifies. In this setting, then, asymmetric information overturns the common economic wisdom that more competition is always desirable

Colour Change Measurements of Gravitational Microlensing Events by Using the Difference Image Analysis Method

Detecting colour changes of a gravitational microlensing event induced by the limb-darkened extended source effect is important to obtain useful information both about the lens and source star. However, precise measurements of the colour changes are hampered by blending, which also causes colour changes of the event. In this paper, we show that although the colour change measured from the subtracted image by using the recently developed photometric method of the ``difference image analysis'' (DIA) differs from the colour change measured by using the conventional method based on the extraction of the individual source stars' point spread functions, the curve of the colour changes (colour curve) constructed by using the DIA method enables one to obtain the same information about the lens and source star, but with significantly reduced uncertainties due to the absence of blending. We investigate the patterns of the DIA colour curves for both single lens and binary lens events by constructing colour change maps.Comment: total 8 pages, including 4 figures and no table, MNRAS, in pres

A Semiparametric Estimator for Dynamic Optimization Models

We develop a new estimation methodology for dynamic optimization models with unobserved state variables Our approach is semiparametric in the sense of not requiring explicit parametric assumptions to be made concerning the distribution of these unobserved state variables We propose a two-step pairwise-difference estimator which exploits two common features of dynamic optimization problems: (1) the weak monotonicity of the agent's decision (policy) function in the unobserved state variables conditional on the observed state variables; and (2) the state-contingent nature of optimal decision-making which implies that conditional on the observed state variables the variation in observed choices across agents must be due to randomness in the unobserved state variables across agents We apply our estimator to a model of dynamic competitive equilibrium in the market for milk production quota in Ontario Canada

Semiparametric Estimation of a Dynamic Game of Incomplete Information

Recently, empirical industrial organization economists have proposed estimators for dynamic games of incomplete information. In these models, agents choose from a finite number actions and maximize expected discounted utility in a Markov perfect equilibrium. Previous econometric methods estimate the probability distribution of agents%u2019 actions in a first stage. In a second step, a finite vector of parameters of the period return function are estimated. In this paper, we develop semiparametric estimators for dynamic games allowing for continuous state variables and a nonparametric first stage. The estimates of the structural parameters are T1/2 consistent (where T is the sample size) and asymptotically normal even though the first stage is estimated nonparametrically. We also propose sufficient conditions for identification of the model.

On the expected number of equilibria in a multi-player multi-strategy evolutionary game

In this paper, we analyze the mean number $E(n,d)$ of internal equilibria in a general $d$-player $n$-strategy evolutionary game where the agents' payoffs are normally distributed. First, we give a computationally implementable formula for the general case. Next we characterize the asymptotic behavior of $E(2,d)$, estimating its lower and upper bounds as $d$ increases. Two important consequences are obtained from this analysis. On the one hand, we show that in both cases the probability of seeing the maximal possible number of equilibria tends to zero when $d$ or $n$ respectively goes to infinity. On the other hand, we demonstrate that the expected number of stable equilibria is bounded within a certain interval. Finally, for larger $n$ and $d$, numerical results are provided and discussed.Comment: 26 pages, 1 figure, 1 table. revised versio