Topic: Threshold spatial autoregressive model
Speaker: LI Kunpeng
Abstract: This paper consider 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 setup 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 prespecified 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.
Date: Tuesday,May 14, 2019
Location: Room 501, Main Building, Shahe Campus, CUFE