Topic：Threshold Spatial Autoregressive Model
Speaker：Li Kunpeng, Capital University of Economics and Business
Time：13:30-15:00
Date: April 29, 2019
Venue：106B, zhonghui Building

Abstract:
This paper considers the estimation and inferential issues of threshold spatial autoregressive model, which is a hybrid of threshold model and spatial econometric model. We consider using the quasi maximum likelihood (QML) method to estimate the model. The asymptotic theory of the QML estimator is established under the framework that the threshold effect shrinks to zero along with an increasing sample size. Our analysis indicates that the limiting distribution of the QML estimator for the threshold value is pivotal up to a scale parameter which involves the skewness and kurtosis of the errors due to the misspecification on the distribution of errors. The QML estimators for the other parameters achieve the oracle property, that is, they have the same limiting distributions as the infeasible QML estimators, which are obtained supposing that the threshold value is observed a priori. We also consider the hypothesis testing on the presence of threshold effect, and the hypothesis testing on the threshold value equal to some pre-specified one. We run Monte carlo simulations to investigate the finite sample performance of the QML estimators and find that the QML estimators have good performance.