Artikel
Indirect inference estimation of spatial autoregressions
The ordinary least squares (OLS) estimator for spatial autoregressions may be consistent as pointed out by Lee (2002), provided that each spatial unit is influenced aggregately by a significant portion of the total units. This paper presents a unified asymptotic distribution result of the properly recentered OLS estimator and proposes a new estimator that is based on the indirect inference (II) procedure. The resulting estimator can always be used regardless of the degree of aggregate influence on each spatial unit from other units and is consistent and asymptotically normal. The new estimator does not rely on distributional assumptions and is robust to unknown heteroscedasticity. Its good finite-sample performance, in comparison with existing estimators that are also robust to heteroscedasticity, is demonstrated by a Monte Carlo study.
- Sprache
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Englisch
- Erschienen in
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Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 8 ; Year: 2020 ; Issue: 3 ; Pages: 1-26 ; Basel: MDPI
- Klassifikation
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Wirtschaft
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Econometric and Statistical Methods and Methodology: General
Estimation: General
- Thema
-
indirect inference
OLS
spatial autoregression
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Bao, Yong
Liu, Xiaotian
Yang, Lihong
- Ereignis
-
Veröffentlichung
- (wer)
-
MDPI
- (wo)
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Basel
- (wann)
-
2020
- DOI
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doi:10.3390/econometrics8030034
- Handle
- Letzte Aktualisierung
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10.03.2025, 11:42 MEZ
Datenpartner
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Objekttyp
- Artikel
Beteiligte
- Bao, Yong
- Liu, Xiaotian
- Yang, Lihong
- MDPI
Entstanden
- 2020
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