Abstract
In this paper, we introduce the one-step generalized method of moments (GMM) estimation methods considered in Lee (2007a) and Liu, Lee, and Bollinger (2010) to spatial models that impose a spatial moving average process for the disturbance term. First, we determine the set of best linear and quadratic moment functions for GMM estimation. Second, we show that the optimal GMM estimator (GMME) formulated from this set is the most efficient estimator within the class of GMMEs formulated from the set of linear and quadratic moment functions. Our analytical results show that the one-step GMME can be more efficient than the quasi maximum likelihood (QMLE), when the disturbance term is simply i.i.d. With an extensive Monte Carlo study, we compare its finite sample properties against the MLE, the QMLE and the estimators suggested in Fingleton (2008a).
| Original language | English |
|---|---|
| Pages (from-to) | 903-926 |
| Number of pages | 24 |
| Journal | Regional Science and Urban Economics |
| Volume | 43 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - Nov 2013 |
| Externally published | Yes |
Funding
We are grateful to the editor, Daniel McMillen, and two anonymous referees for their helpful comments for the improvement of this study. We would like to thank Wim Vijverberg for his instructive comments on this study. Any remaining errors are our own. This research was supported, in part, by a grant of computer time from the City University of New York High Performance Computing Center under NSF grants CNS-0855217 and CNS-0958379 .
| Funders | Funder number |
|---|---|
| City University of New York High Performance Computing Center | |
| National Science Foundation | CNS-0958379, CNS-0855217 |
Keywords
- Asymptotics
- GMM
- SARMA
- SMA
- Spatial autocorrelation
- Spatial dependence
- Spatial moving average process