On vector autoregressive modeling in space and time

Valter Di Giacinto

No 746, Temi di discussione (Economic working papers) from Bank of Italy, Economic Research and International Relations Area

Abstract: Despite the fact that it provides a potentially useful analytical tool, allowing for the joint modeling of dynamic interdependencies within a group of connected areas, until lately the VAR approach had received little attention in regional science and spatial economic analysis. This paper aims to contribute in this field by dealing with the issues of parameter identification and estimation and of structural impulse response analysis. In particular, there is a discussion of the adaptation of the recursive identification scheme (which represents one of the more common approaches in the time series VAR literature) to a space-time environment. Parameter estimation is subsequently based on the Full Information Maximum Likelihood (FIML) method, a standard approach in structural VAR analysis. As a convenient tool to summarize the information conveyed by regional dynamic multipliers with a specific emphasis on the scope of spatial spillover effects, a synthetic space-time impulse response function (STIR) is introduced, portraying average effects as a function of displacement in time and space. Asymptotic confidence bands for the STIR estimates are also derived from bootstrap estimates of the standard errors. Finally, to provide a basic illustration of the methodology, the paper presents an application of a simple bivariate fiscal model fitted to data for Italian NUTS 2 regions.

Keywords: structural VAR model; spatial econometrics; identification; space-time impulse response analysis (search for similar items in EconPapers)
JEL-codes: C32 C33 R10 (search for similar items in EconPapers)
Date: 2010-02
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-geo and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

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Journal Article: On vector autoregressive modeling in space and time (2010)
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