scholarly journals Inequality and development: Evidence from semiparametric estimation with panel data

2011 ◽  
Vol 113 (3) ◽  
pp. 203-207 ◽  
Author(s):  
Xianbo Zhou ◽  
Kui-Wai Li
Keyword(s):  
Panel Data ◽  
Semiparametric Estimation

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.

scholarly journals Semiparametric Estimation and Panel Data Clustering Analysis Based on D-Vine and C-Vine

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Hong Li ◽  
Yuantao Xie ◽  
Juan Yang ◽  
Di Wang
Keyword(s):  
Panel Data ◽  
Clustering Analysis ◽  
Data Clustering ◽  
Semiparametric Estimation ◽  
Similarity Measurement ◽  
Model Estimation ◽  
Similarity Coefficients ◽  
Clustering Model ◽  
Compound Model

This paper proposed a panel data clustering model based on D-vine and C-vine and supported a semiparametric estimation for parameters. These models include a two-step inference function for margins, two-step semiparameter estimation, and stepwise semiparametric estimation. In similarity measurement, similarity coefficients are constructed by a multivariate Hierarchical Nested Archimedean Copula (HNAC) model and compound PCC models, which are HNAC and D-vine compound model and HNAC and C-vine compound model. Estimation solutions and models evaluation are given for these models. In the case study, the clustering results of HNAC and D-vine compound model and HNAC and C-vine compound model are given, and the effect of different copula families on clustering results is also discussed. The result shows the models are effective and useful.

Semiparametric estimation of generalized transformation panel data models with nonstationary error

2020 ◽  
Vol 23 (3) ◽  
pp. 386-402
Author(s):  
Xi Wang ◽  
Songnian Chen
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Bilateral Trade ◽  
Marginal Distribution ◽  
Semiparametric Estimation ◽  
Estimation Method ◽  
Data Models ◽  
Panel Data Models ◽  
Finite Sample ◽  
Foreign Countries

Summary Early studies of the generalized transformation panel data model resorted to the identical marginal distribution of the error term over time. This stationarity condition is restrictive for many applications, especially as the number of time periods increases. This paper considers nonstationary censored generalized transformation panel data models where the idiosyncratic error has an unknown nonseparable form and admits a flexible relationship between the observable and the unobservable. We propose an estimation method, and establish the consistency and asymptotic normality for the proposed estimator. Simulation results illustrate the good performance of our estimator in a finite sample. We apply the proposed method to bilateral trade issues of the U.S.A. and foreign countries.

Semiparametric Estimation of Regression Models for Panel Data

1996 ◽  
Vol 63 (1) ◽  
pp. 145 ◽  
Author(s):  
Joel L. Horowitz ◽  
Marianthi Markatou
Keyword(s):  
Panel Data ◽  
Regression Models ◽  
Semiparametric Estimation
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