Estimation of fixed effects spatial dynamic panel data models with small T and unknown heteroskedasticity
We consider the estimation and inference of fixed effects (FE) spatial dynamic panel data (SDPD) models under small T and unknown heteroskedasticity by extending the M-estimation strategy for homoskedastic FE-SDPD model of Yang (2018, Journal of Econometrics). Unbiased estimating equations are obtai...
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sg-smu-ink.soe_research-33592021-05-31T04:38:42Z Estimation of fixed effects spatial dynamic panel data models with small T and unknown heteroskedasticity LI, Liyao YANG, Zhenlin We consider the estimation and inference of fixed effects (FE) spatial dynamic panel data (SDPD) models under small T and unknown heteroskedasticity by extending the M-estimation strategy for homoskedastic FE-SDPD model of Yang (2018, Journal of Econometrics). Unbiased estimating equations are obtained by adjusting the conditional quasi-score functions given the initial observations, leading to M-estimators that are free from the initial conditions and robust against unknown cross-sectional heteroskedasticity. Consistency and asymptotic normality of the proposed M-estimator are established. The standard errors are obtained by representing the estimating equations as sums of martingale differences. Monte Carlo results show that the proposed M-estimators have good finite sample performance. The practical importance and relevance of allowing for heteroskedasticity in the model is illustrated using data on sovereign risk spillover. 2020-03-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/2360 info:doi/10.1016/j.regsciurbeco.2020.103520 https://ink.library.smu.edu.sg/context/soe_research/article/3359/viewcontent/LiYang2020.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Adjusted quasi score Dynamic panels Fixed effects Initial-condition Martingale difference Short panels Spatial effects Unknown heteroskedasticity Econometrics |
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Adjusted quasi score Dynamic panels Fixed effects Initial-condition Martingale difference Short panels Spatial effects Unknown heteroskedasticity Econometrics LI, Liyao YANG, Zhenlin Estimation of fixed effects spatial dynamic panel data models with small T and unknown heteroskedasticity |
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We consider the estimation and inference of fixed effects (FE) spatial dynamic panel data (SDPD) models under small T and unknown heteroskedasticity by extending the M-estimation strategy for homoskedastic FE-SDPD model of Yang (2018, Journal of Econometrics). Unbiased estimating equations are obtained by adjusting the conditional quasi-score functions given the initial observations, leading to M-estimators that are free from the initial conditions and robust against unknown cross-sectional heteroskedasticity. Consistency and asymptotic normality of the proposed M-estimator are established. The standard errors are obtained by representing the estimating equations as sums of martingale differences. Monte Carlo results show that the proposed M-estimators have good finite sample performance. The practical importance and relevance of allowing for heteroskedasticity in the model is illustrated using data on sovereign risk spillover. |
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LI, Liyao YANG, Zhenlin |
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LI, Liyao YANG, Zhenlin |
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LI, Liyao |
title |
Estimation of fixed effects spatial dynamic panel data models with small T and unknown heteroskedasticity |
title_short |
Estimation of fixed effects spatial dynamic panel data models with small T and unknown heteroskedasticity |
title_full |
Estimation of fixed effects spatial dynamic panel data models with small T and unknown heteroskedasticity |
title_fullStr |
Estimation of fixed effects spatial dynamic panel data models with small T and unknown heteroskedasticity |
title_full_unstemmed |
Estimation of fixed effects spatial dynamic panel data models with small T and unknown heteroskedasticity |
title_sort |
estimation of fixed effects spatial dynamic panel data models with small t and unknown heteroskedasticity |
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Institutional Knowledge at Singapore Management University |
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2020 |
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https://ink.library.smu.edu.sg/soe_research/2360 https://ink.library.smu.edu.sg/context/soe_research/article/3359/viewcontent/LiYang2020.pdf |
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