Bias assessment and reduction for the 2SLS estimator in general dynamic simultaneous equations models

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dc.contributor.author Phillips, Garry David Alan
dc.contributor.author Wang, Dandan
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned 2019-04-29T15:22:13Z
dc.date.available 2019-04-29T15:22:13Z
dc.date.issued 2019-04-29
dc.identifier.issn 2387-0303
dc.identifier.uri http://hdl.handle.net/10016/28322
dc.description.abstract We consider the bias of the 2SLS estimator in general dynamic simultaneousequation models with g endogenous regressors. By using asymptotic expansion techniques we approximate 2SLS coefficient estimation bias under innovation errors, p lagged-dependent variables and strongly-exogenous explanatory variables. Large-T approximations bias of the structural form is then used to construct corrected estimators for the parameters of interest in the general DSEM (C2SLS). Simulations show that the C2SLS gives almost unbiased estimators and low mean squared errors. Alternatively, the numerical bootstrap method results suggest that the non-parametric bootstrap could be used in 2SLS for improving estimation in general DSEM.
dc.language.iso eng
dc.relation.ispartofseries Working paper. Statistics and Econometrics
dc.relation.ispartofseries 19-06
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other General Dynamic Simultaneous Equations Model
dc.subject.other Asymptotic Approximations
dc.subject.other Bias Correction
dc.subject.other Bootstrap
dc.subject.other Monte Carlo Simulations
dc.subject.other 2sls
dc.subject.other C2sls
dc.title Bias assessment and reduction for the 2SLS estimator in general dynamic simultaneous equations models
dc.type workingPaper
dc.subject.jel C13
dc.subject.jel C32
dc.identifier.uxxi DT/0000001703
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