This paper proposes a two stage instrumental variable quantile regression 2S IVQR estimation to estimate the time invariant effects in panel data model. In the first stage, we introduce the dummy variables to represent the time invariant effects, and use quantile regression to estimate effects of individual covariates. The advantage of the first stage is that it can reduce calculations and the number of estimation parameters. Then in the second stage, we adapt instrument variables approach and 2SLS method. In addition, we present a proof of 2S IVQR estimators large sample properties. Monte Carlo simulation study shows that with increasing sample size, the Bias and RMSE of our estimator are decreased. Besides, our estimator has lower Bias and RMSE than those of the other two estimators. Tao Li "A Two-Stage Estimator of Instrumental Variable Quantile Regression for Panel Data with Time-Invariant Effects" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-6 , October 2021, URL: https://www.ijtsrd.com/papers/ijtsrd47716.pdf Paper URL : https://www.ijtsrd.com/other-scientific-research-area/other/47716/a-twostage-estimator-of-instrumental-variable-quantile-regression-for-panel-data-with-timeinvariant-effects/tao-li
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