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This paper presents a convenient shortcut method for implementing the Heckman estimator of the dynamic random effects probit model using standard software. It then compares the three estimators proposed by Heckman, Orme and Wooldridge based on three alternative approximations, first in an...
Persistent link: https://www.econbiz.de/10010268488
This paper compares various forecasts using panel data with spatial error correlation. The true data generating process … rather than homogeneous panel data models. …
Persistent link: https://www.econbiz.de/10010268987
Since little is known about the degree of bias in estimated fixed effects in panel data models, we run Monte Carlo …
Persistent link: https://www.econbiz.de/10010269265
estimation of the popular linear fixed effects panel data model, and to supply Stata code for it. In an application from the …
Persistent link: https://www.econbiz.de/10010269751
clean the bias in the unpaired sample. In a second step, corrected unpaired observations are used jointly with panel data …
Persistent link: https://www.econbiz.de/10010271885
The paper re-examines existing estimators for the panel data fixed effects ordered logit model, proposes a new one, and …
Persistent link: https://www.econbiz.de/10010276790
applying panel data, a new and more accurate fatality risk measure, and systematic selection of panel estimator in our research …
Persistent link: https://www.econbiz.de/10010277313
This paper develops a model for dynamic binary choice panel data that allows for unobserved heterogeneity to be …
Persistent link: https://www.econbiz.de/10010291322
Using data on annual individual labor income from three representative panel datasets (German SOEP, British BHPS …
Persistent link: https://www.econbiz.de/10010324270
In this paper a new method to estimate the equivalence scale elasticity using individual panel data on income … cardinal individual welfare function has to be specified. In addition, panel data enables different scale use by the …
Persistent link: https://www.econbiz.de/10010262430