 Indirect Inference with endogenously missing exogenous variablesSaraswata Chaudhuri, David T. Frazier and Eric Renault Journal of Econometrics, 2018, vol. 205, issue 1, 55-75 Abstract: We consider consistent estimation of parameters in a structural model by Indirect Inference (II) when the exogenous variables can be missing at random (MAR) endogenously. We demonstrate that II procedures that simply discard sample units with missing observations can yield inconsistent estimates of the true structural parameters. By inverse probability weighting (IPW) the “complete case” observations, i.e., sample units with no missing variables for the observed and simulated samples, we propose a new method of II to consistently estimate the structural parameters of interest. Asymptotic properties of the new estimator are discussed. We consider a multinomial probit model to illustrate this approach and subsequently consider simulation studies in a variety of discrete choice models with and without dynamics in terms of lagged dependent variables and serially correlated errors. The simulation results demonstrate the severe bias incurred by existing II estimators, and its correction by our new II estimator. Keywords: Indirect Inference; Missing at random; Inverse probability weighting; Discrete choice models (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:205:y:2018:i:1:p:55-75 DOI: 10.1016/j.jeconom.2018.03.005 Access Statistics for this article Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson More articles in Journal of Econometrics from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.