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Activity Number:
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73
- Modeling Spatial and Statio-Temporal Data
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Type:
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Contributed
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Date/Time:
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Monday, August 3, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Business and Economic Statistics Section
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Abstract #313945
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Title:
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Estimating a SARAR Model Based on the Indirect Inference Principle
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Author(s):
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Xiaotian Liu*
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Companies:
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Keywords:
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SARAR(1,1);
indirect inference
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Abstract:
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This paper proposes a new estimation procedure for the first-order spatial autoregressive model, where the disturbance term also follows a first-order autoregression and its innovations may be heteroscedastic. The estimation procedure is based on the principle of indirect inference (II) that matches the simple OLS estimator of the two spatial autoregressive coecients (one in the outcome equation and the other in the disturbance equation) with its approximate analytical expectation. The new estimator is shown to be consistent, asymptotically normal, and robust to unknown heteroscedasticity. A Monte Carlo study shows its good finite-sample performance in comparison with existing estimators that are based on the GMM principle.
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Authors who are presenting talks have a * after their name.
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