Test for the covariance matrix in time-varying coefficients panel data models with fixed effects

2020 ◽  
Author(s):  
Yuping Hu ◽  
Sanying Feng ◽  
Jing Zhao
Keyword(s):  
Panel Data ◽  
Covariance Matrix ◽  
Fixed Effects ◽  
Data Models ◽  
Panel Data Models ◽  
Time Varying ◽  
Varying Coefficients ◽  
Time Varying Coefficients

scholarly journals Estimating latent group structure in time-varying coefficient panel data models

2019 ◽  
Author(s):  
Jia Chen
Keyword(s):  
Panel Data ◽  
Data Models ◽  
Panel Data Models ◽  
Time Varying ◽  
Clustering Method ◽  
Varying Coefficient ◽  
Varying Coefficients ◽  
Time Varying Coefficients ◽  
Latent Group ◽  
Group Structures

Summary This paper studies the estimation of latent group structures in heterogeneous time-varying coefficient panel data models. While allowing the coefficient functions to vary over cross-sections provides a good way to model cross-sectional heterogeneity, it reduces the degree of freedom and leads to poor estimation accuracy when the time-series length is short. On the other hand, in a lot of empirical studies, it is not uncommon to find that heterogeneous coefficients exhibit group structures where coefficients belonging to the same group are similar or identical. This paper aims to provide an easy and straightforward approach for estimating the underlying latent groups. This approach is based on the hierarchical agglomerative clustering (HAC) of kernel estimates of the heterogeneous time-varying coefficients when the number of groups is known. We establish the consistency of this clustering method and also propose a generalised information criterion for estimating the number of groups when it is unknown. Simulation studies are carried out to examine the finite-sample properties of the proposed clustering method as well as the post-clustering estimation of the group-specific time-varying coefficients. The simulation results show that our methods give comparable performance to the penalised-sieve-estimation-based classifier-LASSO approach by Su et al. (2018), but are computationally easier. An application to a panel study of economic growth is also provided.

Fitting partially linear functional-coefficient panel-data models with Stata

2020 ◽  
Vol 20 (4) ◽  
pp. 976-998
Author(s):  
Kerui Du ◽  
Yonghui Zhang ◽  
Qiankun Zhou
Keyword(s):  
Monte Carlo ◽  
Panel Data ◽  
Fixed Effects ◽  
Linear Functional ◽  
Semiparametric Estimation ◽  
Data Models ◽  
Panel Data Models ◽  
Panel Models ◽  
Partially Linear ◽  
Functional Coefficient

In this article, we describe the implementation of fitting partially linear functional-coefficient panel models with fixed effects proposed by An, Hsiao, and Li [2016, Semiparametric estimation of partially linear varying coefficient panel data models in Essays in Honor of Aman Ullah ( Advances in Econometrics, Volume 36)] and Zhang and Zhou (Forthcoming, Econometric Reviews). Three new commands xtplfc, ivxtplfc, and xtdplfc are introduced and illustrated through Monte Carlo simulations to exemplify the effectiveness of these estimators.

Sign in / Sign up

Export Citation Format

Share Document