scholarly journals Efficient minimum distance estimator for quantile regression fixed effects panel data

2015 ◽  
Vol 133 ◽  
pp. 1-26 ◽  
Author(s):  
Antonio F. Galvao ◽  
Liang Wang
Keyword(s):  
Panel Data ◽  
Quantile Regression ◽  
Fixed Effects ◽  
Minimum Distance ◽  
Minimum Distance Estimator ◽  
Distance Estimator

scholarly journals Minimum distance quantile regression for spatial autoregressive panel data models with fixed effects

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261144
Author(s):  
Xiaowen Dai ◽  
Libin Jin
Keyword(s):  
Panel Data ◽  
Quantile Regression ◽  
Fixed Effects ◽  
Minimum Distance ◽  
Asymptotic Properties ◽  
Data Set ◽  
Spatial Panel ◽  
Quantile Regression Model ◽  
Spatial Panel Data ◽  
Spatial Autoregressive

This paper considers the quantile regression model with individual fixed effects for spatial panel data. Efficient minimum distance quantile regression estimators based on instrumental variable (IV) method are proposed for parameter estimation. The proposed estimator is computational fast compared with the IV-FEQR estimator proposed by Dai et al. (2020). Asymptotic properties of the proposed estimators are also established. Simulations are conducted to study the performance of the proposed method. Finally, we illustrate our methodologies using a cigarettes demand data set.

Quantile regression for panel data models with fixed effects under random censoring

2019 ◽  
Vol 49 (18) ◽  
pp. 4430-4445
Author(s):  
Dai Xiaowen ◽  
Jin Libin ◽  
Tian Yuzhu ◽  
Tian Maozai ◽  
Tang Manlai
Keyword(s):  
Panel Data ◽  
Quantile Regression ◽  
Fixed Effects ◽  
Data Models ◽  
Panel Data Models ◽  
Random Censoring

scholarly journals Asymptotic Distribution of Cramer-von Mises Statistic When Contamination Exists

2015 ◽  
Vol 5 (1) ◽  
pp. 90
Author(s):  
Mayumi Naka ◽  
Ritei Shibata
Keyword(s):  
Maximum Likelihood ◽  
Asymptotic Distribution ◽  
Minimum Distance ◽  
Goodness Of Fit ◽  
Likelihood Estimator ◽  
Goodness Of Fit Test ◽  
Minimum Distance Estimator ◽  
Von Mises Statistic ◽  
Von Mises ◽  
Distance Estimator

In this paper, asymptotic distribution of Cram\'er-von Mises goodness-of-fit test statistic is investigated when contamination exists.<br />We first derive the asymptotic distribution of the Cram\'er-von Mises statistic when the observations are contaminated with noise as a mixture.<br />The result is extended to the case where the parameters are estimated by the minimum distance estimator,<br />which minimizes the Cram\'er-von Mises statistic.<br />In both cases the asymptotic distribution of the Cram\'er-von Mises statistic is given by that of the weighted infinite sum of non-central $\chi^2_1$ variables and the effect of contamination appears only in the non-centrality of the variables.<br />We also demonstrate the robustness of the goodness-of-fit test by Monte Carlo simulations when the parameters are estimated<br />by the minimum distance estimator and the maximum likelihood estimator.<br />Numerical experiments indicate that the use of the minimum distance estimator makes the test insensitive to contamination whereas the power is retained almost the same as that of the maximum likelihood estimator.

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