Ds-Heck
Double-lasso estimation of Heckman selection model
Bibliographic Data
| ID | 21542214 |
|---|---|
| Authors | Masayuki Hirukawa (0000-0001-7532-8746, Ryukoku University), Di Liu (0000-0002-6015-1965, StataCorp (United States)), Irina Murtazashvili (Drexel University), Artem Prokhorov (0000-0003-4046-7994, The University of Sydney, corresponding author) |
| Year | 2023 |
| Volume | 64 |
| Issue | 6 |
| Pages | 3167-3195 |
| Publication date | 2023-06-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Empirical Economics (JOURNAL) |
| Journal identifiers | ISSN: 0377-7332 • E-ISSN: 1435-8921 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s00181-023-02406-w |
| OpenAlex | W4362638454 |
| Language | EN |
| References cited | 32 |
We extend the Heckman (1979) sample selection model by allowing for a large number of controls that are selected using lasso under a sparsity scenario. The standard lasso estimation is known to under-select causing an omitted variable bias in addition to the sample selection bias. We outline the required adjustments needed to restore consistency of lasso-based estimation and inference for vector-valued parameters of interest in such models. The adjustments include double lasso for both the selection equation and main equation and a correction of the variance matrix. We also connect the estimator with results on redundancy of moment conditions. We demonstrate the effect of the adjustments using simulations and we investigate the determinants of female labor market participation and earnings in the US using the new approach. The paper comes with , a dedicated Stata command for estimating double-selection Heckman models
Econometrics · Estimator · Feature selection · Model selection · Selection bias · Statistics · Computer Science · Economics of Agriculture and Food Markets · Labor market dynamics and wage inequality · Mathematics · Monetary Policy and Economic Impact · Artificial Intelligence
Selection, Investment, and Women's Relative Wages Over Time
Estimating Models with Sample Selection Bias
Double/debiased machine learning for treatment and structural parameters
Inference on Treatment Effects after Selection among High-Dimensional Controls
Shadow Prices, Market Wages, and Labor Supply
Some Thoughts on the Distribution of Earnings 1
Root-N-Consistent Semiparametric Regression
The Empirical Content of the Roy Model
Regression Shrinkage and Selection Via the Lasso
Sample Selection Bias as a Specification Error
The Sensitivity of an Empirical Model of Married Women's Hours of Work to Economic and Statistical Assumptions
Inference in High-Dimensional Panel Models With an Application to Gun Control
Post-Selection Inference for Generalized Linear Models With Many Controls
Assessing the “Engines of Liberation”
Gender Wage Gap Accounting
Wage Comparisons--A Selectivity Bias
High-Dimensional Methods and Inference on Structural and Treatment Effects
| Highly cited | No |
|---|