Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/103634
Authors: 
Jin, Fei
Lee, Lung-fei
Year of Publication: 
2013
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Publisher:] MDPI [Place:] Basel [Volume:] 1 [Year:] 2013 [Issue:] 1 [Pages:] 71-114
Abstract: 
This paper studies the generalized spatial two stage least squares (GS2SLS) estimation of spatial autoregressive models with autoregressive disturbances when there are endogenous regressors with many valid instruments. Using many instruments may improve the efficiency of estimators asymptotically, but the bias might be large in finite samples, making the inference inaccurate. We consider the case that the number of instruments K increases with, but at a rate slower than, the sample size, and derive the approximate mean square errors (MSE) that account for the trade-offs between the bias and variance, for both the GS2SLS estimator and a bias-corrected GS2SLS estimator. A criterion function for the optimal K selection can be based on the approximate MSEs. Monte Carlo experiments are provided to show the performance of our procedure of choosing K.
Subjects: 
spatial autoregressive
spatial error
2SLS
endogenous regressor
instrumental variable selection
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/3.0/
Document Type: 
Article
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