Abstract
Stimulated by the Boston house price data, in this paper, we propose a semiparametric spatial dynamicmodel, which extends the ordinary spatial autoregressive models to accommodate the effects of some covariates associated with the house price. A profile likelihood based estimation procedure is proposed. The asymptotic normality of the proposed estimators are derived. We also investigate how to identify the parametric/nonparametric components in the proposed semiparametric model. We show how many unknown parameters an unknown bivariate function amounts to, and propose an AIC/BIC of nonparametric version for model selection. Simulation studies are conducted to examine the performance of the proposed methods. The simulation results show our methods work very well. We finally apply the proposed methods to analyze the Boston house price data, which leads to some interesting findings. © Institute of Mathematical Statistics, 2014.
| Original language | English |
|---|---|
| Pages (from-to) | 700-727 |
| Journal | Annals of Statistics |
| Volume | 42 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - Apr 2014 |
| Externally published | Yes |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Research Keywords
- AIC/BIC
- Local linear modeling
- Profile likelihood
- Spatial interaction
Fingerprint
Dive into the research topics of 'A semiparametric spatial dynamic model'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver