We reconsider the identification and estimation of Gorman-Lancasterstyle 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.

This paper reviews a framework for numerically analyzing dynamic interactions in imperfectly competitive industries. The framework dates back to Ericson & Pakes (1995), but it is based on equilibrium notions that had been available for some time be-fore, and it has been extended in many ways by different authors since. The framework requires as input a set of primitives which describe the institutional structure in the industry to be analyzed. The framework outputs profits and policies for every incum-bent and potential entrant at each possible state of the industry. These policies can be used to simulate the distribution of sample paths for all firms from any initial industry structure. The sample paths generated by the model can be quite different depending on the primitives that are fed into it, and most of the extensions were designed to enable the framework to accommodate empirically relevant cases that required modification of the initial structure. The sample paths possess similar properties to those observed in (the recently made available) panel data sets on industries. These sample paths can be used either for an analysis of the likely response to a policy or an environmental change,

Discrete-time stochastic games with a finite number of states have been widely applied to study the strategic interactions among forward-looking players in dynamic environments. However, these games suffer from a “curse of dimensionality” since the cost of computing players’ expectations over all possible future states increases exponentially in the number of state variables. We explore the alternative of continuous-time stochastic games with a finite number of states and show that continuous time has substantial advantages. Most important, continuous time avoids the curse of dimensionality, thereby speeding up the computations by orders of magnitude in games with more than a few state variables. This much smaller computational burden greatly extends the range and richness of applications of stochastic games.

Learning-by-doing and organizational forgetting have been shown to be important in a variety of industrial settings. This paper provides a general model of dynamic competition that accounts for these economic fundamentals and shows how they shape industry structure and dynamics. Previously obtained results regarding the dominance properties of firms ’ pricing behavior no longer hold in this more general setting. We show that forgetting does not simply negate learning. Rather, learning and forgetting are distinct economic forces. In particular, a model with learning and forgetting can give rise to aggressive pricing behavior, market dominance, and multiple equilibria, whereas a model with learning alone cannot.

This paper studies generic properties of Markov perfect equilibria in dynamic stochastic games. We show that almost all dynamic stochastic games have a finite number of locally isolated Markov perfect equilibria. These equilibria are essential and strongly stable. Moreover, they all admit purification. To establish these results, we introduce a notion of regularity for dynamic stochastic games and exploit a simple connection between normal form and dynamic

We propose a new method for studying the medium and long run dynamic effects of horizontal mergers. Our method builds on the two-step estimator of Bajari, Benkard, and Levin (2007). Policy functions are estimated on historical pre-merger data, and then future industry outcomes are simulated both with and without the proposed merger. Using data for 2003-2007, we apply our model to two recently proposed airline mergers. In our airline entry model, an airline’s entry/exit decisions are made jointly across routes, and depend on features of its own route network as well as the networks of the other airlines. The model allows for city-specific profitability shocks that affect all routes out of a given city, as well as route-specific shocks. We find that the model fits the data very well. Empirical conclusions in the paper are preliminary. PLEASE NOTE: This paper is preliminary and incomplete. Empirical work does not yet include data for regional airlines operating routes for major carriers. Results may change when these are added. ∗This draft is a very rough first effort – not even really a complete paper yet. We thank Steve Berry, Severin

In this paper we study stochastic games with many players; such games are relevant for a wide range of social, economic, and engineering applications. The standard solution concept for such games is Markov perfect equilibrium (MPE), but it is well known that MPE computation becomes intractable as the number of players increases. Further, MPE demands a perhaps implausible level of rationality on the part of players in large games. In this paper we instead consider stationary equilibrium (SE), where players optimize assuming the empirical distribution of others ’ states remains constant at its long run average.We make three main contributions. First, we provide exogenous conditions over model primitives to ensure stationary equilibria exist, in a general model with possibly unbounded state spaces. Second, we show that the same conditions that ensure existence of SE also ensure that SE is a good approximation to MPE in large finite games. Finally, we consider a series of applications, including dynamic oligopoly models, supply chain competition, and consumer learning. These examples highlight that our conditions amount to a dichotomy between “decreasing ” and “increasing ” returns to larger states; SE approximates MPE well in the former case in which the equilibrium market structure becomes fragmented in the limit. In the latter case, SE may not approximate MPE well. 1.

Two major issues have led courts and antitrust enforcers to take a highly skeptical view when assessing claims of anticompetitive predation. First, predation is an inherently dynamic and strategic phenomenon but the practical tools available to identify predatory behavior are based on a static, competitive view of markets. Second, there is understandable concern about the potential distortionary implications of punishing firms for competing too intensely. This paper analyzes these problems in the context of the U.S. airline industry, where there have been frequent allegations of predatory conduct. I first argue, via an explicit dynamic industry model, that certain features of the industry do make it fertile ground for predatory incentives to arise. Specifically, di¤erences in cost structures between large, hub and spoke carriers and small, low cost carriers give incentives for the large carriers to respond aggressively to low cost carriers. I estimate the model parameters and use it to quantify the welfare and behavioral implications of predation policy for a widely discussed case: U.S. vs. American Airlines (2000). To do this, I solve and simulate the model under a menu of counterfactual antitrust predation policies, similar to those employed in practice. I find, for the example of the American case, the potential problems of predation policy are not as severe as the problem of predatory behavior itself.

Many government policies either target the underlying supply infrastructure or have indirect effects on market structure. In this paper we seek to understand the impact of the Critical Access Hospital (CAH) program on the U.S. rural hospital infrastructure and societal welfare. This program provides generous reimbursement to hospitals in exchange for size and service limitations. We specify and estimate a model of the rural hospital industry in which hospitals choose investment, exit, prices and conversion to CAH status. Because these choices have dynamic impacts and even rural hospitals have geographically close competitors, we model hospitals as a dynamic oligopoly. We estimate the structural parameters from this model using a two-step inference method and assess the structural and welfare impacts of the CAH program. Our methods extend current estimation techniques for dynamic oligopoly models to allow for investment behaviors that are more consistent with the data. Our estimated parameters on investment costs and the costs of CAH conversion appear reasonable in magnitude. Preliminary results reveal that the CAH program increases the profits of converting hospitals by $260,000 and decreases exits by 5%.

Discrete-time stochastic games with a finite number of states have been widely applied to study the strategic interactions among forward-looking players in dynamic environments. The model as written down by Ericson & Pakes (1995), Pakes & McGuire (1994, 2001) (hereafter, EP, PM1, and PM2), the subsequent literature (e.g., Gowrisankaran 1999, Fershtman