On vector autoregressive modeling in space and time
AbstractDespite 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.
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Bibliographic InfoPaper provided by Bank of Italy, Economic Research and International Relations Area in its series Temi di discussione (Economic working papers) with number 746.
Date of creation: Feb 2010
Date of revision:
structural VAR model; spatial econometrics; identification; space-time impulse response analysis;
Other versions of this item:
- Valter Giacinto, 2010. "On vector autoregressive modeling in space and time," Journal of Geographical Systems, Springer, vol. 12(2), pages 125-154, June.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
- R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-03-20 (All new papers)
- NEP-ECM-2010-03-20 (Econometrics)
- NEP-ETS-2010-03-20 (Econometric Time Series)
- NEP-GEO-2010-03-20 (Economic Geography)
- NEP-URE-2010-03-20 (Urban & Real Estate Economics)
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