Interdependence among mental health care providers: evidence from a spatial dynamic panel data model with interactive fixed effects

2021 ◽  
Author(s):  
Xu Lin ◽  
Lizi Wu
Keyword(s):  
Health Care Providers ◽  
Data Model ◽  
Fixed Effects ◽  
Dynamic Panel Data ◽  
Care Providers ◽  
Spatial Dynamic ◽  
Dynamic Panel ◽  
Dynamic Panel Data Model ◽  
Interactive Fixed Effects ◽  
Spatial Dynamic Panel Data

ESTIMATION OF UNIT ROOT SPATIAL DYNAMIC PANEL DATA MODELS

2010 ◽  
Vol 26 (5) ◽  
pp. 1332-1362 ◽  
Author(s):  
Jihai Yu ◽  
Lung-fei Lee
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Unit Root ◽  
Dynamic Panel Data ◽  
Spatial Effect ◽  
Panel Data Models ◽  
Spatial Dynamic ◽  
Dynamic Panel ◽  
Dynamic Panel Data Model ◽  
Spatial Dynamic Panel Data

This paper examines the asymptotics of the QMLE for unit root dynamic panel data models with spatial effect and fixed effects. We consider a unit root dynamic panel data model with spatially correlated disturbances and a unit root spatial dynamic panel data model. For both models the estimate of the dynamic coefficient is $\root \of {nT^3 }$ consistent and the estimates of other parameters are $\root \of {nT}$ consistent, and all of them are asymptotically normal. For the latter model the sum of the contemporaneous spatial effect and dynamic spatial effect converges at $\root \of {nT^3 }$ rate. We also propose a bias-correction procedure so that the asymptotic biases of those estimates are eliminated as long as n/T3 → 0.

scholarly journals Estimation and model selection in general spatial dynamic panel data models

2020 ◽  
Vol 117 (10) ◽  
pp. 5235-5241 ◽  
Author(s):  
Baisuo Jin ◽  
Yuehua Wu ◽  
Calyampudi Radhakrishna Rao ◽  
Li Hou
Keyword(s):  
Panel Data ◽  
Least Squares ◽  
Data Model ◽  
Dynamic Panel Data ◽  
Panel Data Model ◽  
Spatial Dynamic ◽  
Dynamic Panel ◽  
Dynamic Panel Data Model ◽  
Spatial Dynamic Panel ◽  
Spatial Dynamic Panel Data

Commonly used methods for estimating parameters of a spatial dynamic panel data model include the two-stage least squares, quasi-maximum likelihood, and generalized moments. In this paper, we present an approach that uses the eigenvalues and eigenvectors of a spatial weight matrix to directly construct consistent least-squares estimators of parameters of a general spatial dynamic panel data model. The proposed methodology is conceptually simple and efficient and can be easily implemented. We show that the proposed parameter estimators are consistent and asymptotically normally distributed under mild conditions. We demonstrate the superior performance of our approach via extensive simulation studies. We also provide a real data example.

A SPATIAL DYNAMIC PANEL DATA MODEL WITH BOTH TIME AND INDIVIDUAL FIXED EFFECTS

2009 ◽  
Vol 26 (2) ◽  
pp. 564-597 ◽  
Author(s):  
Lung-fei Lee ◽  
Jihai Yu
Keyword(s):  
Panel Data ◽  
Limit Distribution ◽  
Fixed Effects ◽  
Asymptotic Properties ◽  
Dynamic Panel Data ◽  
Spatial Dynamic ◽  
Dynamic Panel ◽  
Transformation Approach ◽  
Spatial Dynamic Panel ◽  
Spatial Dynamic Panel Data

This paper establishes asymptotic properties of quasi-maximum likelihood estimators for spatial dynamic panel data with both time and individual fixed effects when the number of individuals n and the number of time periods T can be large. We propose a data transformation approach to eliminate the time effects. When n / T → 0, the estimators are $\root \of {nT}$ consistent and asymptotically centered normal; when n is asymptotically proportional to T, they are $\root \of {nT}$ consistent and asymptotically normal, but the limit distribution is not centered around 0; when n / T → ∞, the estimators are consistent with rate T and have a degenerate limit distribution. We also propose a bias correction for our estimators. When n1/3 / T → 0, the correction will asymptotically eliminate the bias and yield a centered confidence interval. The estimates from the transformation approach can be consistent when n is a fixed finite number.

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