【9月11日】bevictor伟德官网學術論壇

講座題目：Double Boosting for High Dimensional IV Regression Models

主 講 人：Tae Hwy Lee 教授

講座時間：2017年9月11日14:40—16:00

講座地點：bevictor伟德官网主教樓1108會議室

 

主講人簡介：

加州大學河濱分校經濟系教授，1990年6月畢業于加州大學聖地亞哥分校，獲得經濟學博士學位，導師為Halbert White Jr和諾貝爾經濟學獎獲得者Clive W.J. Granger先生。研究方向涵蓋時間序列、金融風險分析、大數據、機器學習等方面。為American Economic Review，Econometric Theory，Journal of Econometrics，Economics Letters等頂級期刊雜志匿名審稿人。獲得The Bank of Korea Research Award和Tjalling C. Koopmans Econometric Theory Prize等獎項。

Abstracts:

Endogeneity in a regression model for the automobile demand equation leads to inconsistent estimation of the price elasticity parameter. The standard solutions are the two stage least squares (2SLS) and generalized method of moments (GMM). These methods face challenges when instruments are high dimensional and when some are irrelevant and/or invalid. It is critical to select relevant and valid instruments for the consistent estimation. In this paper, we introduce a new method that will select relevant and valid instruments simultaneously using boosting algorithm, which we call Double Boosting (DB). We show that the DB consistently selects relevant and valid instruments. In particular, we consider the case when the endogenous variables X (price) are unknown nonlinear functions of observable instruments W (the product characteristics), which can be approximated by some sieve functions such as polynomials. The sieve approximation captures nonlinearity between endogenous variables X and instruments W , while however it produces high dimensional instruments Z=f(W). Monte Carlo simulation demonstrates the DB procedure, and compares its performance relative to other methods such as penalized GMM (Cheng and Liao 2015) and the standard Boosting (Ng and Bai 2008). In the application to estimating the BLP-type automobile demand function (Berry, Levinson and Pakes 1995) with price being endogenous and instruments being high dimensional functions of product characteristics, we find that the DB estimators indicate that automobiles demands are more consistent with the profit maximization compared to other estimators.