A general central limit theorem is proved for estimators defined by minimization of the length of a vector-valued, random criterion function. No smoothness assumptions are imposed on the criterion function in order that the results might apply to a broad class of simulation estimators. Complete analyses of two simulation estimators, one introduced by A. Pakes (1986) and the other by D. McFadden (1989), illustrate the application of the general theorems. These examples illustrate how simulation can be used to circumvent two computational problems that arise frequently in applied econometrics: evaluating intractable aggregation formulae and evaluating discrete response probabilities.

Technological change and deregulation have caused a major restructuring of the telecommunications equipment industry over the last two decades. We estimate the parameters of a production function for the equipment industry and then use those estimates to analyze the evolution of plant level productivity over this period. The restructuring involved significant entry and exit and large changes in the sizes of incumbents. Since firms' choices on whether to liquidate and on the quantities of inputs demanded should they continue depend on their productivity, we use an equilibrium model to suggest an estimation algorithm that takes into account the relationship between productivity on the one hand. and both input demand and survival on the other. A fully parametric version of the estimation algorithm would be both computationally burdensome and require a host of auxiliary assumptions. So we develop a semi parametric technique which is both consistent with a quite general version of the theoretical framework and easy to use. The algorithm produces markedly different estimates of both production function parameters and of productivity movements than traditional estimation procedures. We find an increase in the rate of industry productivity growth after deregulation. This in spite of the fact there was no increase in the average of the plants' rates of productivity growth, and there was actually a fall in our index of the efficiency of the allocation of variable factors conditional on the existing distribution of fixed factors. Deregulation was, however, followed by a reallocation of capital towards more productive establishments (by a down sizing, often shutdown. of unproductive plants and by disproportionate growth of productive establishments) which more than offset the other factors' negative impacts on aggregate productivity.

We show that moment inequalities in a wide variety of economic applications have a particular linear conditional structure. We use this structure to construct uniformly valid confidence sets that remain computationally tractable even in settings with nuisance parameters. We first introduce least-favorable critical values which deliver non-conservative tests if all moments are binding. Next, we introduce a novel conditional inference approach which ensures a strong form of insensitivity to slack moments. Our recommended approach is a hybrid technique which combines desirable aspects of the least favorable and conditional methods. The hybrid approach performs well in simulations calibrated to Wollmann (2018, American Economic Review, 108, 1364–1406), with favorable power and computational time comparisons relative to existing alternatives.

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We provide an asymptotic distribution theory for a class of generalized method of moments estimators that arise in the study of differentiated product markets when the number of observations is associated with the number of products within a given market. We allow for three sources of error: sampling error in estimating market shares, simulation error in approximating the shares predicted by the model, and the underlying model error. It is shown that the estimators are CAN provided the size of the consumer sample and the number of simulation draws grow at a large enough rate relative to the number of products. We consider the implications of the results for the Berry, Levinsohn and Pakes (1995) random coefficient logit model and the pure characteristics model analysed in Berry and Pakes (2002). The required rates differ for these two frequently used demand models. A small Monte Carlo study shows that the differences in asymptotic properties of the two models are reflected, in quite a striking way, in the models' small sample properties. Moreover the limit distributions provide a good approximation to the actual Monte Carlo distribution of the parameter estimates. The results have important implications for the computational burden of the two models.

This paper considers the problem of hiring scientists for research and development projects when one takes explicit account of the fact that the scientist may be able to use the information acquired during the project in a rival enterprise. Management's problem is to determine an optimum labor policy for its project. The policy consists of an employment decision and a labor contract. Given optimum behavior, it is straightforward to analyze the effect of the potential for mobility of scientific personnel on project profitability and on research employment. We also formalize conditions under which one would expect to observe a scientist leaving his employer to set up or join a rival.

We evaluate the voluntary export restraint (VER) that was initially placed on exports of automobiles from Japan in 1981. We evaluate the impact this policy had on U.S. consumer welfare, firm profits, and forgone tariff revenue from its initiation through 1990.