Palgrave Handbook of Econometrics: Applied Econometrics

Luc Anselin and Nancy Lozano-Gracia 1245

and forms the basis for flexible policy simulation experiments. While the field has
made significant progress to date, several important methodological issues remain
to be addressed satisfactorily. Foremost among these are the treatment of spa-
tial scale, endogeneity and sub-market heterogeneity. It is hoped that the current
chapter has raised awareness of these issues and will stimulate further progress.

Notes

1. For a more extensive discussion, see Anselin (2002, pp. 256–60) and Anselin (2006,
   pp. 909–10).


2. Commonly used theoretical specifications include the negative exponential, spherical and
   Gaussian semi-variograms. A detailed discussion of specific functional forms is given in
   Dubinet al. (1999).


3. Note that, rather than estimating the more traditional inverse demand function, Brasing-
   ton and Hite (2005) estimate a direct demand function in the second stage of the hedonic
   model.


4. Bitteret al. (2007) also introduce a spatially lagged variable into the GWR specification,
   but it is doubtful that the OLS estimation procedure used would yield consistent estimates
   of this lag term.

References

Allen, M.T., T.M. Springer and N.G. Waller (1995) Implicit pricing across residential rental
submarkets.Journal of Real Estate Finance and Economics 11 , 137–51.
Andrews, D.W. (1991) Heteroscedasticity and autocorrelation consistent covariance matrix
estimation.Econometrica 59 , 817–58.
Anselin, L. (1988)Spatial Econometrics: Methods and Models. Dordrecht: Kluwer Academic
Publishers.
Anselin, L. (1990) Spatial dependence and spatial structural instability in applied regression
analysis.Journal of Regional Science 30 , 185–207.
Anselin, L. (1992) Spatial dependence and spatial heterogeneity: model specification issues
in the spatial expansion paradigm. In J.P. Jones and E. Casetti (eds.),Applications of the
Expansion Method, pp. 334–54. London: Routledge.
Anselin, L. (1998) GIS research infrastructure for spatial analysis of real estate markets.Journal
of Housing Research 9 (1), 113–33.
Anselin, L. (2001a) Rao’s score test in spatial econometrics.Journal of Statistical Planning and
Inference 97 , 113–39.
Anselin, L. (2001b) Spatial effects in econometric practice in environmental and resource
economics.American Journal of Agricultural Economics 83 (3):705–10.
Anselin, L. (2002) Under the hood. Issues in the specification and interpretation of spatial
regression models.Agricultural Economics 27 (3), 247–67.
Anselin, L. (2006) Spatial econometrics. In T. Mills and K. Patterson (eds.),Palgrave Handbook
of Econometrics: Volume 1, Econometric Theory, pp. 901–69. Basingstoke: Palgrave Macmillan.
Anselin, L. and A. Bera (1998) Spatial dependence in linear regression models with an intro-
duction to spatial econometrics. In A. Ullah and D.E. Giles (eds.),Handbook of Applied
Economic Statistics, pp. 237–89. New York: Marcel Dekker.
Anselin, L. and D.A. Griffith (1988) Do spatial effects really matter in regression analysis.
Papers, Regional Science Association 65 , 11–34.
Anselin, L. and J. Le Gallo (2006) Interpolation of air quality measures in hedonic house price
models: spatial aspects.Spatial Economic Analysis 1 , 31–52.
Anselin, L. and N. Lozano-Gracia (2008) Errors in variables and spatial effects in hedonic
house price models of ambient air quality.Empirical Economics 34 , 5–34.