Impact of COVID-19 on Financial Returns: A Spatial Dynamic Panel Data Model with Random Effects

2021 ◽  
Author(s):  
Anna Gloria Billé ◽  
Massimiliano Caporin
Keyword(s):  
Panel Data ◽  
Random Effects ◽  
Data Model ◽  
Dynamic Panel Data ◽  
Panel Data Model ◽  
Financial Returns ◽  
Spatial Dynamic ◽  
Dynamic Panel ◽  
Dynamic Panel Data Model ◽  
Spatial Dynamic Panel Data

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.

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.

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