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4In this study, we formulate the adjusted gradient tests when the alternative model used to construct tests deviates from the true data generating process for a spatial dynamic panel data 6 model (SDPD). Following Bera et al. (2010), we introduce these adjusted gradient tests along with the standard ones within a GMM framework. These tests can be used to detect the presence 8 of (i) the contemporaneous spatial lag terms, (ii) the time lag term, and (iii) the spatial time lag terms in an higher order SDPD model. These adjusted tests have two advantages: (i) 10 their null asymptotic distribution is a central chi-squared distribution irrespective of the misspecified alternative model, and (ii) their test statistics are computationally simple and require 12 only the ordinary least-squares (OLS) estimates from a non-spatial two-way panel data model. We investigate the finite sample size and power properties of these tests through Monte Carlo

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit organization supported by Deutsche Post Foundation. The center is associated with the University of Bonn and offers a stimulating research environment through its international network, workshops and conferences, data service, project support, research visits and doctoral program. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. Terms of use: Documents in D I S C U S S I O N P A P E R S E R I E S Rising Sea Levels and Sinking PropertyIZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author. Are coastal cities adjusting to rising sea levels? This paper argues that large-scale events have the potential to ignite the process. We examine the effects of hurricane Sandy on the New York City housing market. We assemble a large plot-level dataset with rich geographic data on housing sales in New York City for the period 2003-2015, along with information on which building structures were damaged by the hurricane, and to what degree. Our difference-in-difference estimates provide robust evidence of a negative impact on the price trajectories of houses that were directly affected by Sandy. Interestingly, this is also the case for houses that were not damaged but face high risk of coastal flooding. Our results suggest that Sandy has increased the perceived risk of living in those neighborhoods. We also show that the negative effects on housing prices appear to be highly persistent. JEL Classification:H56, K42, R33

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Researchers 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.