GMM estimation of the covariance structure of longitudinal data on earnings
| dc.creator | Doris, Aedín | |
| dc.creator | O'Neill, Donal | |
| dc.creator | Sweetman, Olive | |
| dc.date | 2017-04-01T19:30:21Z | |
| dc.date.accessioned | 2026-07-09T08:44:02Z | |
| dc.description | In this article, we discuss generalized method of moments estimation of the covariance structure of longitudinal data on earnings, and we introduce and illustrate a Stata program that facilitates the implementation of the generalized method of moments approach in this context. The program, gmmcovearn,estimates a variety of models that encompass those most commonly used by labor economists. These include models where the permanent component of earnings follows a random growth or random walk process and where the transitory component can follow either an AR(1) or an ARMA(1,1) process. In addition, time-factor loadings and cohort-factor loadings may be incorporated in the transitory and permanent components. | |
| dc.identifier | Other:st0237 | |
| dc.identifier | doi:10.22004/ag.econ.196680 | |
| dc.identifier | https://ageconsearch.umn.edu/record/196680/files/sjart_st0237.pdf | |
| dc.identifier | http://ageconsearch.umn.edu/record/196680 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/602076 | |
| dc.language | eng | |
| dc.publisher | ||
| dc.source | http://ageconsearch.umn.edu/record/196680 | |
| dc.title | GMM estimation of the covariance structure of longitudinal data on earnings | |
| dc.type | Text |