论文标题
估计具有随机系数的分化产品的高维离散选择模型
Estimating High-Dimensional Discrete Choice Model of Differentiated Products with Random Coefficients
论文作者
论文摘要
我们为具有高维产品属性的分化产品的离散选择模型提出了一个估计程序。在我们的模型中，高维属性可以是产品间接效用的均值和方差的决定因素。我们模型中的关键限制在于，高维属性仅通过有限的许多指数影响间接实用程序的方差。在随机coefictiont logit模型的框架中，我们显示了$ l_1 $ regultimized的最小距离估计器的错误率，并证明了偏差估计器的渐近线性性。
We propose an estimation procedure for discrete choice models of differentiated products with possibly high-dimensional product attributes. In our model, high-dimensional attributes can be determinants of both mean and variance of the indirect utility of a product. The key restriction in our model is that the high-dimensional attributes affect the variance of indirect utilities only through finitely many indices. In a framework of the random-coefficients logit model, we show a bound on the error rate of a $l_1$-regularized minimum distance estimator and prove the asymptotic linearity of the de-biased estimator.
