A Dynamic Analysis of the Market for Wide-Bodied Commercial Aircraft

This paper develops a multi-agent dynamic model of the commercial aircraft industry and then uses that model to analyze industry pricing, industry performance, and optimal industry policy. In the model, firms are differentiated in their products and cost structure, and entry, exit, prices, and quantity sold are endogenously determined in dynamic equilibrium. Re ecting the focus of the paper, demand and supply are modeled structurally, while investment is modeled in reduced form. The model utilizes a cost model of commercial aircraft production developed and estimated in a previous paper (Benkard (2000)), and a discrete choice model of commercial aircraft demand to determine static profits. I find that many unusual aspects of the aircraft data, such as high concentration and pricing below the level of static marginal cost, are explained by this model. The model also replicates the stochastic evolution of the industry well. Many of these properties could not be explained with a static model. These results provide support for the structural dynamic modeling approach in general. I also find that the unconstrained Markov perfect equilibrium is quite efficient from a social perspective, providing only 9% less welfare on average than a social planner would obtain, but that the Markov perfect equilibrium shifts a substantial amount of welfare from consumers to producers. Finally, I provide simulation evidence that an anti-trust policy in the form of a concentration restriction would be welfare reducing with high probability.

On the Nonparametric Identification of Nonlinear Simultaneous Equations Models: comment on B. Brown (1983) and Roehrig (1988)

This note revisits the identification theorems of B. 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, contrary to the theorems, the reduced form of a nonseparable simultaneous equations model is not identified even under the assumptions of those papers. 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.Simultaneous equations, Non-separable errors

Demand Estimation with Heterogeneous Consumers and Unobserved Product Characteristics: A Hedonic Approach

We study the identification and estimation of Gorman-Lancaster style hedonic models of demand for differentiated products for the case when one product characteristic is not observed. Our identification and estimation strategy is a two-step approach in the spirit of Rosen (1974). Relative to Rosen's approach, we generalize the first stage estimation to allow for a single dimensional unobserved product characteristic, and also allow the hedonic pricing function to have a general, non-additive structure. In the second stage, if the product space is continuous and the functional form of utility is known then there exists an inversion between the consumer's choices and her preference parameters. This inversion can be used to recover the distribution of random coefficients nonparametrically. For the more common case when the set of products is finite, we use the revealed preference conditions from the hedonic model to develop a Gibbs sampling estimator for the distribution of random coefficients. We apply our methods to estimating personal computer demand.

Demand Estimation With Heterogeneous Consumers and Unobserved Product Characteristics: A Hedonic Approach

We study the identification and estimation of preferences in hedonic discrete choice models of demand for differentiated products. In the hedonic discrete choice model, products are represented as a finite dimensional bundle of characteristics, and consumers maximize utility subject to a budget constraint. Our hedonic model also incorporates product characteristics that are observed by consumers but not by the economist. We demonstrate that, unlike the case where all product characteristics are observed, it is not in general possible to uniquely recover consumer preferences from data on a consumer's choices. However, we provide several sets of assumptions under which preferences can be recovered uniquely, that we think may be satisfied in many applications. Our identification and estimation strategy is a two stage approach in the spirit of Rosen (1974). In the first stage, we show under some weak conditions that price data can be used to nonparametrically recover the unobserved product characteristics and the hedonic pricing function. In the second stage, we show under some weak conditions that if the product space is continuous and the functional form of utility is known, then there exists an inversion between a consumer's choices and her preference parameters. If the product space is discrete, we propose a Gibbs sampling algorithm to simulate the population distribution of consumers' taste coefficients.

House Prices and Consumer Welfare

We develop a new approach to measuring changes in consumer welfare due to changes in the price of owner-occupied housing. In our approach, an agent's welfare adjustment is defined as the transfer required to keep expected discounted utility constant given a change in current home prices. We demonstrate that, up to a first-order approximation, there is no aggregate change in welfare due to price increases in the existing housing stock. This follows from a simple market clearing condition where capital gains experienced by sellers are exactly offset by welfare losses to buyers. Welfare losses can occur, however, from price increases in new construction and renovations. We show that this result holds (approximately) even in a model that accounts for changes in consumption and investment plans prompted by current price changes. We estimate the welfare cost of house price appreciation to be an average of $127 per household per year over the 1984-1998 period.

Estimating Dynamic Models of Imperfect Competition

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 I.O. 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).

Learning and Forgetting: The Dynamics of Aircraft Production

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.

Markov Perfect Industry Dynamics with Many Firms

We propose an approximation method for analyzing Ericson and Pakes (1995)-style dynamic models of imperfect competition. We develop a simple algorithm for computing an ``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. We prove 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. We develop bounds that can be computed to assess the accuracy of the approximation for any given applied problem. Through computational experiments, we find that the method often generates useful approximations for industries with hundreds of firms and in some cases even tens of firms.

On the Nonparametric Identification of Nonlinear Simultaneous Equations Models: Comment on B. Brown (1983) and Roehrig (1988)

This note revisits the identification theorems of B. 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, contrary to the theorems, the reduced form of a nonseparable simultaneous equations model is not identified even under the assumptions of those papers. 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