306 research outputs found

    Durable Goods Oligopoly with Secondary Markets: the Case of Automobiles

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    We study the effects of durability and secondary markets on equilibrium firm behavior in the car market. We construct a dynamic oligopoly model of a differentiated product market to incorporate the equilibrium production dynamics which arise from the durability of the goods and their active trade in secondary markets. We derive an econometric model and estimate its parameters using data from the automobile industry over a twenty-year period. Our estimates are used to provide a measure of the competitive importance of the secondary market.Publicad

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

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    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

    Monopoly quality degradation and regulation in cable television

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    Using an empirical framework based on the Mussa-Rosen model of monopoly quality choice, we calculate the degree of quality degradation in cable television markets and the impact of regulation on those choices. We find lower bounds of quality degradation ranging from 11 to 45 percent of offered service qualities. Furthermore, cable operators in markets with local regulatory oversight offer significantly higher quality, less degradation, and greater quality per dollar, despite higher prices

    Nonparametric identification of dynamic models with unobserved state variables

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    We consider the identification of a Markov process {W t, X t*} for t=1,2,...,T when only {W t} for t=1, 2,..,T is observed. In structural dynamic models, W t denotes the sequence of choice variables and observed state variables of an optimizing agent, while X t* denotes the sequence of serially correlated state variables. The Markov setting allows the distribution of the unobserved state variable X t* to depend on W t-1 and X t-1 *. We show that the joint distribution of (W t, X t*, W t-1 , X t-1 *) is identified from the observed distribution of (W t+1 , W t, W t-1 , W t-2 , W t-3 ) under reasonable assumptions. Identification of the joint distribution of (W t, X t*, W t-1 , X t-1 *) is a crucial input in methodologies for estimating dynamic models based on the "conditional-choice-probability (CCP)" approach pioneered by Hotz and Miller.

    A Semiparametric Estimator for Dynamic Optimization Models

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    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

    Generalized empirical likelihood-based model selection criteria for moment condition models

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    This paper proposes model selection criteria (MSC) for unconditional moment models using generalized empirical likelihood (GEL) statistics. The use of GEL-statistics in lieu of J-statistics (in the spirit of Andrews, 1999, Econometrica 67, 543-564; and Andrews and Lu, 2001, Journal of Econometrics 101, 123-164) leads to an alternative interpretation of the MSCs that emphasizes the common information-theoretic rationale underlying model selection procedures for both parametric and semiparametric models. The result of this paper also provides a GEL-based model selection alternative to the information criteria-based nonnested tests for generalized method of moments models considered in Kitamura (2000, University of Wisconsin). The results of a Monte Carlo experiment are reported to illustrate the finite-sample performance of the selection criteria and their impact on parameter estimation
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