This paper uses an empirical dynamic oligopoly model of the commercial aircraft industry to analyse industry pricing, industry performance, and optimal industry policy. A novel feature of the model with respect to the previous literature is that entry, exit, prices, and quantities are endogenously determined in Markov perfect equilibrium (MPE). We find that many unusual aspects of the aircraft data, such as high concentration and persistent pricing below static marginal cost, are explained by this model. We also find that the unconstrained MPE is quite efficient from a social perspective, providing only 10% less welfare on average than a social planner would obtain. Finally, we provide simulation evidence that an anti-trust policy in the form of a concentration restriction would be welfare reducing.

This paper introduces a new cost dataset for a commercial aircraft firm and uses this data to analyze the dynamics of learning in commercial aircraft production. This dataset is found to be inconsistent with the simple learning hypothesis, and particularly the prediction that a firm's unit cost must decline with its cumulative production. Instead, strong support is found for the hypothesis of organizational forgetting, a more general learning model where unit costs are similarly dependent on a firm's past production experience, but where that experience depreciates over time. Additionally, it is found that some, but not all, of a firm's production experience transfers from one generation of an aircraft to the next. This evidence adds to our understanding of productivity in industries with learning and thus has implications to many fields of economics.

This note revisits the identification theorems of Brown (1983) and Roehrig (1988). We describe an error in the proofs of the main identification theorems in these papers, and provide an important counterexample to the theorems on the identification of the reduced form. Specifically, the reduced form of a nonseparable simultaneous equations model is not identified even under the assumptions of these papers. We provide conditions under which the reduced form is identified and is recoverable using the distribution of the endogenous variables conditional on the exogenous variables. However, these conditions place substantial limitations on the structural model. We conclude the note with a conjecture that it may be possible to use classical exclusion restrictions to recover some of the key implications of the theorems in more general settings.

We reconsider the identification and estimation of Gorman‐Lancaster‐style hedonic models of demand for differentiated products in the spirit of Sherwin Rosen. We generalize Rosen’s first stage to account for product characteristics that are not observed and to allow the hedonic pricing function to have a general nonseparable form. We take an alternative semiparametric approach to Rosen’s second stage in which we assume that the parametric form of utility is known, but we place no restrictions on the aggregate distribution of utility parameters. If there are only a small number of products, we show how to construct bounds on individuals’ utility parameters, as well as other economic objects such as aggregate demand and consumer surplus. We apply our methods to estimating the demand for personal computers.

We propose an approximation method for analyzing Ericson and Pakes (1995)-style dynamic models of imperfect competition. We define a new equilibrium concept that we call oblivious equilibrium, in which each firm is assumed to make decisions based only on its own state and knowledge of the long-run average industry state, but where firms ignore current information about competitors' states. The great advantage of oblivious equilibria is that they are much easier to compute than are Markov perfect equilibria. Moreover, we show that, as the market becomes large, if the equilibrium distribution of firm states obeys a certain "light-tail" condition, then oblivious equilibria closely approximate Markov perfect equilibria. This theorem justifies using oblivious equilibria to analyze Markov perfect industry dynamics in Ericson and Pakes (1995)-style models with many firms.

We describe a two-step algorithm for estimating dynamic games under the assumption that behavior is consistent with Markov perfect equilibrium. In the first step, the policy functions and the law of motion for the state variables are estimated. In the second step, the remaining structural parameters are estimated using the optimality conditions for equilibrium. The second step estimator is a simple simulated minimum distance estimator. The algorithm applies to a broad class of models, including industry competition models with both discrete and continuous controls such as the Ericson and Pakes (1995) model. We test the algorithm on a class of dynamic discrete choice models with normally distributed errors and a class of dynamic oligopoly models similar to that of Pakes and McGuire (1994).