Arbeitspapier
Panel VAR models with spatial dependence
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Reihe Ökonomie / Economics Series ; No. 237
- Klassifikation
-
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
- Thema
-
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
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Mutl, Jan
- Ereignis
-
Veröffentlichung
- (wer)
-
Institute for Advanced Studies (IHS)
- (wo)
-
Vienna
- (wann)
-
2009
- Handle
- Letzte Aktualisierung
-
12.01.2024, 20:12 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Mutl, Jan
- Institute for Advanced Studies (IHS)
Entstanden
- 2009
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