Title

Inference Based on Alternative Bootstrapping Methods in Spatial Models with an Application to County Income Growth in the United States

Campus Units

Economics, Center for Agricultural and Rural Development

Document Type

Article

Publication Version

Submitted Manuscript

Publication Date

3-24-2011

Journal or Book Title

Journal of Regional Science

Volume

51

Issue

5

First Page or Article ID Number

880

Last Page

896

DOI

10.1111/j.1467-9787.2011.00716.x

Abstract

This study examines aggregate county income growth across the 48 contiguous states from 1990 to 2005. To control for endogeneity, we estimate a two-stage spatial error model and implement a number of spatial bootstrap routines to infer parameter significance. Among the results, we find that outdoor recreation and natural amenities favor positive growth in rural counties and property taxes correlate negatively with rural growth. Comparing bootstrap inference with other models, including the recent General Moment heteroskedastic-robust spatial error estimator, we find similar conclusions suggesting bootstrapping can be effective in spatial models where asymptotic results are not well established.

Comments

This working paper was published as Monchuk, Daniel C., Dermot J. Hayes, John A. Miranowski and Dayton M. Lambert, "Inference based on alternative bootstrapping methods in spatial models with an application to county income growth in the United States," Journal of Regional Science 51 (2011): 880–896, doi:10.1111/j.1467-9787.2011.00716.x.