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Total Factor Productivity Decomposition and Unobserved Heterogeneity in Stochastic Frontier Models

机译:随机边界模型中的全要素生产率分解和不可观测的异质性

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摘要

This study examines in an empirical comparison how different econometric specifications of stochastic frontier models affect the decomposition of total factor productivity growth. We estimate nine stochastic frontier models, which have been widely used in empirical investigations of sources of productivity growth. Our results show that the relative contribution of components to total factor productivity growth is quite sensitive to the choice of econometric model, which points to the need to select the "right" model. We apply various statistical tests to narrow the range of applicable models and identify additional criteria upon which to base the choice of non-nested models.
机译:本研究以经验比较的方式考察了随机前沿模型的不同计量经济学指标如何影响全要素生产率增长的分解。我们估计了9个随机前沿模型,这些模型已广泛用于生产率增长源的实证研究。我们的结果表明,组件对全要素生产率增长的相对贡献对计量经济模型的选择非常敏感,这表明需要选择“正确的”模型。我们应用各种统计检验来缩小适用模型的范围,并确定其他标准,以选择非嵌套模型为基础。

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