Testing for the presence of measurement error in Stata
Research output: Contribution to journal › Journal article › Research › peer-review
Documents
- 1536867x20931002
Final published version, 571 KB, PDF document
In this article, we describe how to test for the presence of measurement error in explanatory variables. First, we discuss the test of such hypotheses in parametric models such as linear regressions and then introduce a new command,dgmtest, for a nonparametric test proposed in Wilhelm (2018, Working Paper CWP45/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies). To illustrate the new command, we provide Monte Carlo simulations and an empirical application to testing for measurement error in administrative earnings data.
Original language | English |
---|---|
Journal | Stata Journal |
Volume | 20 |
Issue number | 2 |
Pages (from-to) | 382-404 |
Number of pages | 23 |
ISSN | 1536-867X |
DOIs | |
Publication status | Published - Jun 2020 |
- st0600, dgmtest, nonparametric test, measurement error, measurement error bias, NONLINEAR MODELS, NONPARAMETRIC-ESTIMATION, INSTRUMENTAL VARIABLES, SPECIFICATION TESTS, MISCLASSIFICATION, IDENTIFICATION, DYNAMICS, MICRO
Research areas
Number of downloads are based on statistics from Google Scholar and www.ku.dk
No data available
ID: 255045612