Panel VAR Models with Spatial Dependence

Jan Mutl

No 237, Economics Series from Institute for Advanced Studies

Abstract: I consider a panel vector-autoregressive model with cross-sectional dependence of the disturbances characterized by a spatial autoregressive process. I propose a three-step estimation procedure. Its first step is an instrumental variable estimation that ignores the spatial correlation. In the second step, the estimated disturbances are used in a multivariate spatial generalized moments estimation to infer the degree of spatial correlation. The final step of the procedure uses transformed data and applies standard techniques for estimation of panel vector-autoregressive models. I compare the small-sample performance of various estimation strategies in a Monte Carlo study.

Keywords: Spatial PVAR; Multivariate dynamic panel data model; Spatial GM; Spatial Cochrane-Orcutt transformation; Constrained maximum likelihood estimation (search for similar items in EconPapers)
JEL-codes: C13 C31 C33 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2009-03
New Economics Papers: this item is included in nep-ecm, nep-geo and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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https://irihs.ihs.ac.at/id/eprint/1916 First version, 2009 (application/pdf)

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Working Paper: Panel VAR Models with Spatial Dependence (2002)
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