Panel VAR models with spatial dependence

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Reihe Ökonomie / Economics Series ; No. 237

Classification and Topics

• Classification: Wirtschaft ; Estimation: General ; Multiple or Simultaneous Equation Models: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models ; Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models
• Subject: spatial PVAR ; multivariate dynamic panel data model ; spatial GM ; spatial Cochrane-Orcutt transformation ; constrained maximum likelihood estimation ; VAR-Modell ; Panel ; Momentenmethode ; Maximum-Likelihood-Methode ; Monte-Carlo-Methode

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Mutl, Jan
• Event: Veröffentlichung
• (who): Institute for Advanced Studies (IHS)
• (where): Vienna
• (when): 2009

Further information

• Handle: http://hdl.handle.net/10419/72670
• Last update: 10.03.2025, 11:42 AM CET