| Speaker: |
Dalei Yu, Yunnan University of Economics and Finance |
| Title: |
Corrected Akaike information criterion with general covariance matrix |
| Time & Venue: |
2022.06.28 19:00-20:00; 腾讯会议：96248182651 |
| Abstract: |
In this paper, within the framework of Stein’s identity, we propose a new corrected Akaike information criterion for the finite sample setting. The new criterion applies to the situation where very general covariance structures are involved. Under certain regularity conditions, we establish the asymptotic efficiency of the proposed model selection criterion. Simulations in the spatial regression model with autoregressive errors show that our method is promising when the difference between the candidate models and the true data generating process is small. Our method becomes particularly competitive with its competitors when such difference becomes larger. The proposed model selection criterion is also applied to the analysis of a set of real data (the Neighborhood Crimes Data) and the results further support the use of our method in practical situations. |
