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On the robustness of location estimators in models of firm growth under heavy-tailedness

机译:重尾下企业成长模型中位置估计器的鲁棒性

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摘要

Focusing on the model of demand-driven innovation and spatial competition over time in Jovanovic and Rob (1987), we study the effects of the robustness of estimators employed by firms to make inferences about their markets on the firms' growth patterns. We show that if consumers' signals in the model are moderately heavy-tailed and the firms use the sample mean of the signals to estimate the ideal product, then the firms' output levels exhibit positive persistence. In such a setting, large firms have an advantage over their smaller counterparts. These properties are reversed for signals with extremely heavy-tailed distributions. In such a case, the model implies anti-persistence in output levels, together with a surprising pattern of oscillations in firm sizes, with smaller firms being likely to become larger ones next period, and vice versa. We further show that the implications of the model under moderate heavy-tailedness continue to hold under the only assumption of symmetry of consumers' signals if the firms use a more robust estimator of the ideal product, the sample median
机译:着眼于Jovanovic和Rob(1987)的需求驱动型创新和空间竞争随时间变化的模型,我们研究了公司用来估计其市场的估计量的稳健性对公司增长模式的影响。我们表明,如果模型中的消费者信号适度地重尾,并且企业使用信号的样本均值来估计理想产品,则企业的产出水平将表现出正的持久性。在这种情况下,大公司比小公司具有优势。对于具有极重尾分布的信号,这些特性相反。在这种情况下,该模型意味着产出水平的反持久性,以及企业规模波动的令人惊讶的模式,较小的企业可能在下一个时期变成较大的企业,反之亦然。我们进一步表明,如果企业使用理想产品的更可靠的估计量(样本中位数),那么在中等重尾的情况下,该模型的含义在假设消费者信号对称的唯一假设下将继续存在。

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