.png){kind=icon}
.png){kind=icon}
Return
ISSUES IN SPATIAL DATA ANALYSIS
.png){kind=icon}
PRE
AI
DOI：10.1111/j.1467-9787.2009.00656.x.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Misspecified functional forms tend to produce biased estimates and spatially correlated errors. Imposing less structure than standard spatial lag models while being more amenable to large datasets, nonparametric and semiparametric methods offer significant advantages for spatial modeling. Fixed effect estimators have significant advantages when spatial effects are constant within well-defined zones, but their flexibility can produce variable, inefficient estimates while failing to account adequately for smooth spatial trends. Though estimators that are designed to measure treatment effects can potentially control for unobserved variables while eliminating the need to specify a functional form, they may be biased if the variables are not constant within discrete zones.
Keywords:
LAND VALUES
STRATEGIC INTERACTION
WEIGHTED REGRESSION
LOCAL-GOVERNMENTS
COMPETITION
LOCATION
CHICAGO
PRICES
POLICY
MODEL
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.7
Papers:
2.1K
Citations:
3.2K
