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Authors = Jau-er Chen

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20 pages, 428 KiB  
Article
Exploring Industry-Distress Effects on Loan Recovery: A Double Machine Learning Approach for Quantiles
by Hui-Ching Chuang and Jau-er Chen
Econometrics 2023, 11(1), 6; https://doi.org/10.3390/econometrics11010006 - 14 Feb 2023
Cited by 2 | Viewed by 4557
Abstract
In this study, we explore the effect of industry distress on recovery rates by using the unconditional quantile regression (UQR). The UQR provides better interpretative and thus policy-relevant information on the predictive effect of the target variable than the conditional quantile regression. To [...] Read more.
In this study, we explore the effect of industry distress on recovery rates by using the unconditional quantile regression (UQR). The UQR provides better interpretative and thus policy-relevant information on the predictive effect of the target variable than the conditional quantile regression. To deal with a broad set of macroeconomic and industry variables, we use the lasso-based double selection to estimate the predictive effects of industry distress and select relevant variables. Our sample consists of 5334 debt and loan instruments in Moody’s Default and Recovery Database from 1990 to 2017. The results show that industry distress decreases recovery rates from 15.80% to 2.94% for the 15th to 55th percentile range and slightly increases the recovery rates in the lower and the upper tails. The UQR provide quantitative measurements to the loss given default during a downturn that the Basel Capital Accord requires. Full article
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18 pages, 760 KiB  
Article
Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions
by Jau-er Chen, Chien-Hsun Huang and Jia-Jyun Tien
Econometrics 2021, 9(2), 15; https://doi.org/10.3390/econometrics9020015 - 2 Apr 2021
Cited by 10 | Viewed by 7197
Abstract
In this study, we investigate the estimation and inference on a low-dimensional causal parameter in the presence of high-dimensional controls in an instrumental variable quantile regression. Our proposed econometric procedure builds on the Neyman-type orthogonal moment conditions of a previous study (Chernozhukov et [...] Read more.
In this study, we investigate the estimation and inference on a low-dimensional causal parameter in the presence of high-dimensional controls in an instrumental variable quantile regression. Our proposed econometric procedure builds on the Neyman-type orthogonal moment conditions of a previous study (Chernozhukov et al. 2018) and is thus relatively insensitive to the estimation of the nuisance parameters. The Monte Carlo experiments show that the estimator copes well with high-dimensional controls. We also apply the procedure to empirically reinvestigate the quantile treatment effect of 401(k) participation on accumulated wealth. Full article
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22 pages, 838 KiB  
Article
Causal Random Forests Model Using Instrumental Variable Quantile Regression
by Jau-er Chen and Chen-Wei Hsiang
Econometrics 2019, 7(4), 49; https://doi.org/10.3390/econometrics7040049 - 16 Dec 2019
Cited by 13 | Viewed by 11021
Abstract
We propose an econometric procedure based mainly on the generalized random forests method. Not only does this process estimate the quantile treatment effect nonparametrically, but our procedure yields a measure of variable importance in terms of heterogeneity among control variables. We also apply [...] Read more.
We propose an econometric procedure based mainly on the generalized random forests method. Not only does this process estimate the quantile treatment effect nonparametrically, but our procedure yields a measure of variable importance in terms of heterogeneity among control variables. We also apply the proposed procedure to reinvestigate the distributional effect of 401(k) participation on net financial assets, and the quantile earnings effect of participating in a job training program. Full article
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