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100			_aArbia, Giuseppe _956517
245			_aTesting Impact Measures in Spatial Autoregressive Models/
260			_bSage, _c2020.
300			_aVol 43, Issue 1-2, 2020( 40–75 p.)
520			_aResearchers often make use of linear regression models in order to assess the impact of policies on target outcomes. In a correctly specified linear regression model, the marginal impact is simply measured by the linear regression coefficient. However, when dealing with both synchronic and diachronic spatial data, the interpretation of the parameters is more complex because the effects of policies extend to the neighboring locations. Summary measures have been suggested in the literature for the cross-sectional spatial linear regression models and spatial panel data models. In this article, we compare three procedures for testing the significance of impact measures in the spatial linear regression models. These procedures include (i) the estimating equation approach, (ii) the classical delta method, and (iii) the simulation method. In a Monte Carlo study, we compare the finite sample properties of these procedures.
700			_aBera, Anil K. _956518
700			_aDogan, Osman _956519
700			_aTaspınar, Suleyman _956520
773	0		_011129 _917016 _dSage, 2019. _tInternational regional science review
856			_uhttps://doi.org/10.1177/0160017619826264
942			_2ddc _cEJR
999			_c14099 _d14099