Next Article in Journal
Ten Things You Should Know about the Dynamic Conditional Correlation Representation
Previous Article in Journal
Outlier Detection in Regression Using an Iterated One-Step Approximation to the Huber-Skip Estimator
Open AccessArticle

Generalized Spatial Two Stage Least Squares Estimation of Spatial Autoregressive Models with Autoregressive Disturbances in the Presence of Endogenous Regressors and Many Instruments

by 1 and 2,*
1
School of Economics, Shanghai University of Finance and Economics, Shanghai 200433, China
2
Department of Economics, The Ohio State University, Columbus, OH 43210, USA
*
Author to whom correspondence should be addressed.
Econometrics 2013, 1(1), 71-114; https://doi.org/10.3390/econometrics1010071
Received: 25 March 2013 / Revised: 25 April 2013 / Accepted: 25 April 2013 / Published: 27 May 2013
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. View Full-Text
Keywords: spatial autoregressive; spatial error; 2SLS; endogenous regressor; instrumental variable selection spatial autoregressive; spatial error; 2SLS; endogenous regressor; instrumental variable selection
MDPI and ACS Style

Jin, F.; Lee, L.-F. Generalized Spatial Two Stage Least Squares Estimation of Spatial Autoregressive Models with Autoregressive Disturbances in the Presence of Endogenous Regressors and Many Instruments. Econometrics 2013, 1, 71-114.

Show more citation formats Show less citations formats

Article Access Map

1
Only visits after 24 November 2015 are recorded.
Back to TopTop