Panel Data Partially Linear Varying-Coefficient Model with Both Spatially and Time-Wise Correlated Errors

2014 ◽  
Author(s):  
Yang Bai ◽  
Jianhua Hu ◽  
Jinhong You
Keyword(s):  
Panel Data ◽  
Varying Coefficient Model ◽  
Correlated Errors ◽  
Varying Coefficient ◽  
Partially Linear

Kernel-based spatial error model for analyzing spatial panel data

2020 ◽  
Vol 15 (3) ◽  
pp. 239-248
Author(s):  
Jooyong Shim ◽  
Sang Bum Lee ◽  
Daiwon Kim ◽  
Jung-Suk Yu ◽  
Chanha Hwang
Keyword(s):  
Panel Data ◽  
Error Model ◽  
Support Vector ◽  
Spatial Error Model ◽  
Varying Coefficient Model ◽  
Spatial Error ◽  
Spatial Panel ◽  
Varying Coefficient ◽  
Proposed Model ◽  
Spatial Panel Data

Spatial panel data model captures spatial interactions across spatial units and over time. Lots of effort have been devoted to develop effective estimation methods for parametric and nonparametric spatial panel data models. Varying coefficient model has received a great deal of attention as an important tool for modeling panel data. In this paper we propose a kernel-based spatial error model for the purpose of analyzing spatial panel data. This model is based on the idea of fixed effect time-varying coefficient model and the kernel technique of support vector machine along with the technique of regularization. A generalized cross validation method is also considered for choosing the hyperparameters which affect the performance of the proposed model. The proposed model is evaluated through numerical studies.

scholarly journals B-spline estimation in varying coefficient models with correlated errors

2021 ◽  
Vol 7 (3) ◽  
pp. 3509-3523
Author(s):  
Yanping Liu ◽  
◽  
Juliang Yin
Keyword(s):  
Convergence Rates ◽  
Regression Function ◽  
Estimation Methods ◽  
Varying Coefficient Model ◽  
Correlated Errors ◽  
Computationally Efficient ◽  
Smooth Functions ◽  
B Spline ◽  
Varying Coefficient ◽  
Spline Estimation

<abstract><p>The varying coefficient model assumes that the regression function depends linearly on some regressors, and that the regression coefficients are smooth functions of other predictor variables. It provides an appreciable flexibility in capturing the underlying dynamics in data and avoids the so-called "curse of dimensionality" in analyzing complex and multivariate nonlinear structures. Existing estimation methods usually assume that the errors for the model are independent; however, they may not be satisfied in practice. In this study, we investigated the estimation for the varying coefficient model with correlated errors via B-spline. The B-spline approach, as a global smoothing method, is computationally efficient. Under suitable conditions, the convergence rates of the proposed estimators were obtained. Furthermore, two simulation examples were employed to demonstrate the performance of the proposed approach and the necessity of considering correlated errors.</p></abstract>

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