Modeling Spatial Externalities: A Panel Data Approach

27 Pages Posted: 1 May 2009 Last revised: 20 Apr 2010

See all articles by Christian Beer

Christian Beer

Vienna University of Economics and Business

Aleksandra Riedl

Vienna University of Economics and BA - Institute for Economic Geography and GIScience

Date Written: March 24, 2010

Abstract

In this paper we argue that the Spatial Durbin Model (SDM) is an appropriate framework to empirically quantify different kinds of externalities. Besides, it is attractive from an econometric point of view as it nests several other models frequently employed. Up to now the SDM has been applied in cross-sectional settings only, thereby ignoring individual heterogeneity. This paper extends the SDM to panel data allowing for non-spherical disturbances and proposes an estimator based on ML techniques. Results from a Monte Carlo study reveal that the estimator has satisfactory small sample properties also in cases when neither heteroskedasticity nor serial correlation is present in the data generating process. Moreover, we show that conventional testing procedures may wrongly reject the existence of spatial externalities. In particular, we show that the incidence of a type II error increases as the spatial weight matrix becomes denser.

Keywords: Spatial panel data, Spatial Durbin Model, Maximum Likelihood, AR(1) and heteroskedastic errors, Monte Carlo simulation

JEL Classification: C21, C23

Suggested Citation

Beer, Christian and Riedl, Aleksandra, Modeling Spatial Externalities: A Panel Data Approach (March 24, 2010). Available at SSRN: https://ssrn.com/abstract=1397106 or http://dx.doi.org/10.2139/ssrn.1397106

Christian Beer

Vienna University of Economics and Business ( email )

Welthandelsplatz 1
Vienna, Wien 1020
Austria

Aleksandra Riedl (Contact Author)

Vienna University of Economics and BA - Institute for Economic Geography and GIScience ( email )

Nordbergstra├če 15
A-1090 Wien
Austria

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