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Demand estimation with selection bias: A dynamic game approach with an application to the US railroad industry

机译：具有选择偏差的需求估计：适用于美国铁路行业的动态博弈方法

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摘要

This paper is motivated by the US freight railroad industry, which is characterized by a major restructuring over the last 30 years. In particular, the number of active firms decreased from 26 in 1978 to seven in 2006 due to several takeover waves. The empirical focus concerns the estimation of a structural demand model for the US railroad industry. Then, the demand estimates are used to compute the evolution of the mark-ups, the quality of the freight services provided, and the consumer surplus. The restructuring of this industry involves significant exit and takeovers. This implies that the data is characterized by an attrition issue, which generates a selection problem. A focus is to provide an estimation algorithm which takes explicitly into account this attrition issue. I find that the algorithm produces more plausible estimates of demand coefficients compared to standard estimation procedures. Moreover, using the model, I recover the evolution of the marginal costs, mark-ups, and the consumer surplus over time. I find that the takeover waves have led to efficiency gains by decreasing the marginal costs, and this was translated into lower prices and an increase in the consumer surplus. Finally, the takeovers have led to a reallocation of assets from the less efficient firms to the most efficient firms, which improved the quality of the freight services provided.
机译：本文的灵感来自美国货运铁路业，该行业的特点是过去30年进行了重大重组。特别是，由于几次收购浪潮，活跃公司的数量从1978年的26家减少到2006年的7家。实证重点涉及对美国铁路行业的结构需求模型的估计。然后，需求估算将用于计算加价的演变，所提供货运服务的质量以及消费者剩余。该行业的重组涉及大量退出和接管。这意味着数据的特征是损耗问题，这会产生选择问题。重点是提供一种估计算法，该算法明确考虑了损耗问题。我发现，与标准估算程序相比，该算法可得出更合理的需求系数估算值。而且，使用该模型，我可以恢复边际成本，加价和消费者剩余随时间的演变。我发现收购浪潮通过降低边际成本而导致效率提高，这转化为更低的价格和消费者剩余的增加。最后，收购导致资产从效率较低的公司重新分配到效率最高的公司，从而提高了所提供货运服务的质量。

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    Coublucq Daniel;

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  • 年度 2013
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