Alumni

Le-Yu Chen

Le-Yu is a PhD scholar in the Centre for Microdata Methods and Practice. His research interests are in econometric theories and applications. He is currently working on identification and estimation of dynamic discrete choice models, specification test of multiple inequality constraints, and identification of continuous treatment models.

Selected Publications

Sparse Quantile Regression

We consider both l0-penalized and l0-constrained quantile regression estimators. For the l0-penalized estimator, we derive an […]

Sokbae (Simon) Lee, Le-Yu Chen
24 June 2020 | CWP30/20
Best subset binary prediction

We consider a variable selection problem for the prediction of binary outcomes. We study the best […]

Sokbae (Simon) Lee, Le-Yu Chen
22 November 2017 | CWP50/17
Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models

This paper studies inference of preference parameters in semiparametric discrete choice models when these parameters are […]

Sokbae (Simon) Lee, Le-Yu Chen
22 November 2017 | CWP51/17
Exact computation of GMM estimators for instrumental variable quantile regression models

We show that the generalized method of moments (GMM) estimation problem in instrumental variable quantile regression […]

Le-Yu Chen, Sokbae (Simon) Lee
22 November 2017 | CWP52/17
Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models

This paper studies inference of preference parameters in semiparametric discrete choice models when these parameters are […]

Sokbae (Simon) Lee, Le-Yu Chen
18 June 2015 | CWP26/15
Maximum score estimation with nonparametrically generated regressors

The estimation problem in this paper is motivated by the maximum score estimation of preference parameters […]

Sokbae (Simon) Lee, Myung Jae Sung, Le-Yu Chen
1 October 2014 | Journal Article

Previous version

Maximum score estimation with nonparametrically generated regressors
Sokbae (Simon) Lee, Myung Jae Sung, Le-Yu Chen
28 May 2014 | CWP27/14
Maximum score estimation with nonparametrically generated regressors

The estimation problem in this paper is motivated by maximum score estimation of preference parameters in […]

Sokbae (Simon) Lee, Myung Jae Sung, Le-Yu Chen
28 May 2014 | CWP27/14

Latest version

Maximum score estimation with nonparametrically generated regressors
Sokbae (Simon) Lee, Myung Jae Sung, Le-Yu Chen
1 October 2014 | Journal Article

Previous version

Maximum score estimation of preference parameters for a binary choice model under uncertainty
Myung Jae Sung, Le-Yu Chen, Sokbae (Simon) Lee
23 April 2013 | CWP14/13
Testing multiple inequality hypotheses: a smoothed indicator approach

This paper proposes a class of origin-smooth approximators of indicators underlying the sum-of-negative-part statistic for testing […]

Jerzy Szroeter, Le-Yu Chen
31 January 2014 | Journal Article

Previous version

Testing multiple inequality hypotheses: a smoothed indicator approach
Jerzy Szroeter, Le-Yu Chen
6 July 2012 | CWP16/12
Maximum score estimation of preference parameters for a binary choice model under uncertainty

This paper develops maximum score estimation of preference parameters in the binary choice model under uncertainty […]

Myung Jae Sung, Le-Yu Chen, Sokbae (Simon) Lee
23 April 2013 | CWP14/13

Latest version

Maximum score estimation with nonparametrically generated regressors
Sokbae (Simon) Lee, Myung Jae Sung, Le-Yu Chen
28 May 2014 | CWP27/14
Testing multiple inequality hypotheses: a smoothed indicator approach

This paper proposes a class of origin-smooth approximators of indicators underlying the sum-of-negative-part statistic for testing […]

Jerzy Szroeter, Le-Yu Chen
6 July 2012 | CWP16/12

Latest version

Testing multiple inequality hypotheses: a smoothed indicator approach
Jerzy Szroeter, Le-Yu Chen
31 January 2014 | Journal Article

Previous version