摘要翻译:
本文研究并推广了Fox,Kim,Ryan和Bajari(2011)的计算吸引力非参数随机系数估计量。我们证明了它们的估计量是非负套索的特例,解释了它在许多应用中观察到的稀疏性质。认识到这一联系,我们推广了估计量,将它转化为非负弹性网的一个特例。该扩展提高了估计器对真支持度的恢复,并允许对随机系数分布进行更准确的估计。我们的估计量是原始估计量的推广,因此,保证模型的拟合度至少与原始估计值一样好。对两种估计的性质进行了理论分析,结果表明,在一定条件下,我们的广义估计更接近真实分布。两个Monte Carlo实验和对旅行模式数据集的应用说明了广义估计器的改进性能。
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英文标题:
《Nonparametric Estimation of the Random Coefficients Model: An Elastic
Net Approach》
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作者:
Florian Heiss, Stephan Hetzenecker, and Maximilian Osterhaus
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最新提交年份:
2019
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分类信息:
一级分类:Economics 经济学
二级分类:Econometrics 计量经济学
分类描述:Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论,微观计量经济学,宏观计量经济学,通过新方法发现的经济关系的实证内容,统计推论应用于经济数据的方法论方面。
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英文摘要:
This paper investigates and extends the computationally attractive nonparametric random coefficients estimator of Fox, Kim, Ryan, and Bajari (2011). We show that their estimator is a special case of the nonnegative LASSO, explaining its sparse nature observed in many applications. Recognizing this link, we extend the estimator, transforming it to a special case of the nonnegative elastic net. The extension improves the estimator's recovery of the true support and allows for more accurate estimates of the random coefficients' distribution. Our estimator is a generalization of the original estimator and therefore, is guaranteed to have a model fit at least as good as the original one. A theoretical analysis of both estimators' properties shows that, under conditions, our generalized estimator approximates the true distribution more accurately. Two Monte Carlo experiments and an application to a travel mode data set illustrate the improved performance of the generalized estimator.
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PDF链接:
https://arxiv.org/pdf/1909.08434