Quantile regression for partially linear varying coefficient spatial autoregressive models

Dai, Xianhua; Li, Shaoyang; Tian, Maozai

doi:10.1080/03610918.2022.2154365

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…Jin et al (2016) explored the quantile regression approach for partially linear spatial autoregressive models with possibly varying coefficients using B-spline. (Dai et al, 2022) . Dai et al (2020) investigated fixed effects quantile regression for general spatial panel data models with both individual fixed effects and time effects based on the instrumental variable method.…”

Section: Introductionmentioning

confidence: 99%

Spatial Autoregressive Quantile Regression with Application on Open Unemployment Data

Yanuar

Abrari

et al. 2023

Sci. Technol. Indones

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The Open Unemployment Level (OUL) is the percentage of the unemployed to the total labor force. One of the provinces with the highest OUL score in Indonesia is West Java Province. If an object of observation is affected by spatial effects, namely spatial dependence and spatial diversity, then the regression model used is the Spatial Autoregressive (SAR) model. Quantile regression minimizes absolute weighted residuals that are not symmetrical. It is perfect for use on data distribution that is not normally distributed, dense at the ends of the data distribution, or there are outliers. The Spatial Autoregressive Quantile Regression (SARQR) is a model that combines spatial autoregressive models with quantile regression. This research used the data regarding OUR in West Java in 2020 from the Central Bureau of Statistics. This study develops to modeling the Open Unemployment Level in all province in Indonesia using modified spatial autoregressive model with the quantile regression approach. This study compares the estimation results based on SAR and SARQR models to obtain an acceptable model. In this study, it was found that the SARQR model is better than SAR at dealing with the problems of dependency and diversity in spatial data modeling and is not easily affected by the presence of outlier data.

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Section: Introductionmentioning

confidence: 99%

Spatial Autoregressive Quantile Regression with Application on Open Unemployment Data

Yanuar

Abrari

et al. 2023

Sci. Technol. Indones

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Estimation of Fixed Effects Partially Linear Varying Coefficient Panel Data Regression Model with Nonseparable Space-Time Filters

Chen

2023

Mathematics

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Space-time panel data widely exist in many research fields such as economics, management, geography and environmental science. It is of interest to study the relationship between response variable and regressors which come from above fields by establishing regression models. This paper introduces a new fixed effects partially linear varying coefficient panel data regression model with nonseparable space-time filters. On the basis of approximating the varying coefficient functions with a powerful B-spline method, the profile quasi-maximum likelihood estimators of parameters and varying coefficient functions are constructed. Under some regular conditions, we derive their consistency and asymptotic normality. Monte Carlo simulation shows that our estimates have good finite performance and ignoring spatial and serial correlations may lead to inefficiency of estimates. Finally, the driving forces of Chinese resident consumption rate are studied using our estimation method.

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Wild Bootstrap-Based Bias Correction for Spatial Quantile Panel Data Models with Varying Coefficients

et al. 2023

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This paper studies quantile regression for spatial panel data models with varying coefficients, taking the time and location effects of the impacts of the covariates into account, i.e., the implications of covariates may change over time and location. Smoothing methods are employed for approximating varying coefficients, including B-spline and local polynomial approximation. A fixed-effects quantile regression (FEQR) estimator is typically biased in the presence of the spatial lag variable. The wild bootstrap method is employed to attenuate the estimation bias. Simulations are conducted to study the performance of the proposed method and show that the proposed methods are stable and efficient. Further, the estimators based on the B-spline method perform much better than those of the local polynomial approximation method, especially for location-varying coefficients. Real data about economic development in China are also analyzed to illustrate application of the proposed procedure.

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Quantile regression for partially linear varying coefficient spatial autoregressive models

Cited by 3 publications

References 31 publications

Spatial Autoregressive Quantile Regression with Application on Open Unemployment Data

Spatial Autoregressive Quantile Regression with Application on Open Unemployment Data

Estimation of Fixed Effects Partially Linear Varying Coefficient Panel Data Regression Model with Nonseparable Space-Time Filters

Wild Bootstrap-Based Bias Correction for Spatial Quantile Panel Data Models with Varying Coefficients

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