Title: Misspecification testing in spatial autoregressive models
Authors: Francesca Rossi - University of Verona (Italy) [presenting]
Jungyoon Lee - Royal Holloway, University of London (United Kingdom)
Peter CB Phillips - Yale University (United States)
Abstract: Spatial autoregressive (SAR) and related models offer flexible yet parsimonious ways to model spatial or network interaction. However, SAR specifications typically rely on a particular parametric functional form and an exogenous choice of the so-called spatial weight matrix with only limited guidance from theory in making these specifications. Moreover, the choice of a SAR model over other alternatives, such as Spatial Durbin (SD) or Spatial Lagged X (SLX) models, is often arbitrary, raising issues of potential specification error. To address such issues, we develop an omnibus specification test within the SAR framework that can detect general forms of misspecification including that of the spatial weight matrix, functional form and the model itself. The approach extends the framework of conditional moment testing to the general spatial setting. We derive the asymptotic distribution of our test statistic under the null hypothesis of correct SAR specification, show consistency of the test, and provide local power properties. A Monte Carlo study is conducted to study finite sample performance of the test.