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
International Journal of Trend
Volume 5 Issue 6, September-October
@ IJTSRD | Unique Paper ID – IJTSRD
A Two-Stage Estimator of
Regression for Panel Data with Time
School of Information, Beijing Wuzi
ABSTRACT
This paper proposes a two-stage instrumental variable quantile
regression (2S-IVQR) estimation to estimate the time
effects in panel data model. In the first stage, we introduce the
dummy variables to represent the time
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 estimator's 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.
KEYWORDS: Time-invariant effects; Panel data;
regression; Instrument variables
1. INTRODUCTION
Panel data not only reflects
the
individual
heterogeneity of cross-section data, but also shows
the dynamic information about the time series data.
Quantile regression can describe the independent
variable for the dependent variable
accurately, and capture systematic influences of
covariates on the location, scale and shape of the
conditional
distribution
of
the
response.
Additionally,
there is no need for quantile
regression model to make any assumptions on the
overall distribution, compared with the ordinary
least squares method.
Quantile regression model for panel data, can fully
describe the conditional distribution of the response
variable as well as control the variability. There are
three types of estimation of quantile regression for
panel data with
fixed effects:
the penalty
estimation, two-step estimation, minimum distance
estimation.
The penalty estimation mainly considers adding