Production function estimation using subjective expectations data

Agnes Norris Keiller, Á. D. Paula, J. V. Reenen
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Abstract

Standard methods for estimating production functions in the Olley and Pakes (1996) tradition require assumptions on input choices. We introduce a new method that exploits (increasingly available) data on a firm's expectations of its future output and inputs that allows us to obtain consistent production function parameter estimates while relaxing these input demand assumptions. In contrast to dynamic panel methods, our proposed estimator can be implemented on very short panels (including a single cross-section), and Monte Carlo simulations show it outperforms alternative estimators when firms' material input choices are subject to optimization error. Implementing a range of production function estimators on UK data, we find our proposed estimator yields results that are either similar to or more credible than commonly-used alternatives. These differences are larger in industries where material inputs appear harder to optimize. We show that TFP implied by our proposed estimator is more strongly associated with future jobs growth than existing methods, suggesting that failing to adequately account for input endogeneity may underestimate the degree of dynamic reallocation in the economy.
利用主观预期数据估算生产函数
按照 Olley 和 Pakes(1996 年)的传统,估算生产函数的标准方法需要对投入品的选择做出假设。我们引入了一种新方法,利用(越来越多的)企业对未来产出和投入的预期数据,在放宽这些投入需求假设的同时,获得一致的生产函数参数估计值。与动态面板方法不同的是,我们提出的估计方法可以在非常短的面板(包括单个横截面)上实施,蒙特卡罗模拟显示,当企业的材料投入选择存在优化误差时,我们提出的估计方法优于其他估计方法。在对英国数据实施一系列生产函数估计时,我们发现我们提出的估计方法得出的结果与常用的替代方法相似或更可信。在材料投入似乎更难优化的行业中,这些差异更大。我们的研究表明,与现有方法相比,我们提出的估计方法所隐含的全要素生产率与未来就业增长的关联度更高,这表明,如果不能充分考虑投入的内生性,可能会低估经济中动态再分配的程度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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