Spatial Vector Autoregressions

Since there is so far no estimator that allows to estimate a dynamic panel model that includes a spatial lag as well as other potential endogenous variables. This paper wants to determine if it is suitable to instrument the spatial lag variable (which is by de…nition endogenous/simultaneous) using the instruments proposed by system GMM, i.e. lagged spatial lag values. The Monte Carlo investigation highlights the possibility to estimate a dynamic spatial lag model using the extended GMM proposed by Arellano and Bover (1995) and Blundell and Bover (1998), especially when N and T are large.

Section: Introductionmentioning

confidence: 99%

Spatial Dynamic Panel Model and System GMM: A Monte Carlo Investigation

Kukenova

Monteiro

2008

“…We focus on two particular ones: an SVAR model proposed by Beenstock and Felsenstein (2007), and a dynamic heterogeneous-coefficients panel data model based on eigenvector SF (Griffith 2000(Griffith , 2003Patuelli et al 2012). We deliberately select two models belonging to two separate traditions: VAR models come from the time-series forecasting tradition, and are widely used, in macroeconomics, to study adjustment processes; the SF-augmented dynamic panel data model connects the panel data modelling tradition [e.g., least squares dummy variables (LSDV) models] to the spatial statistics one, within a semi-parametric framework.…”

Section: Methodsology: Spatial Vector-autoregressive Models and Spatiamentioning

confidence: 99%

Spatial Panel Data Forecasting over Different Horizons, Cross-Sectional and Temporal Dimensions

Mayor

Patuelli

2013

Empirical assessments of the forecasting power of spatial panel data econometric models are still scarcely available. Moreover, several methodological contributions rely on simulated data to showcase the potential of proposed methods. While simulations may be useful to evaluate the properties of a single estimator, the empirical set-ups of simulation studies are often based on strong assumptions regarding the shape and regularity of the statistical distribution of the variables involved. It is then valuable to have, next to simulation studies, empirical assessments of competing econometric models based on real data. In this paper, we evaluate competing spatial (dynamic) panel methods, selecting a number of data sets characterized by a range of different crosssectional and temporal dimensions, as well as different levels of spatial autocorrelation. We carry out our empirical exercise on regional unemployment data for France, Spain and Switzerland. Additionally, we test different forecasting horizons, in order to investigate the speed of deterioration of forecasting quality. We compare two classes of methods: spatial vector autoregressive (SVAR) models and dynamic panel models making use of eigenvector spatial filtering (SF). We find that, as it could be expected, the unbalance between the temporal and cross-sectional dimension (T >> n) does play in favour of the SVAR model. On the other hand, the advantage of the SVAR model over the SF model appears to diminish as the forecasting horizon widens, eventually leading the SF model to being preferred for more distant forecasts.

“…Spatial weights matrices are positive, non-stochastic and their elements show the intensity of interdependence between pairs of spatial units, that is, eventually specify the neighbouring set for each spatial unit. In this paper, we follow the SVAR approach proposed by Beenstock and Felsenstein (2007) …”

Section: Modelling Spatio-temporal Data: Spatial Var Models and Spatimentioning

confidence: 99%

“…Our interest here is to exploit the strong heterogeneity in the size (e.g., in terms of population or area) of NUTS regions at the same level of aggregation across countries to investigate the variation in the performance of different spatial econometric methods. We analyse the forecasting performance of two competing econometric methods: a spatial vector autoregressive (SVAR) model (Beenstock and Felsenstein 2007;Kuethe and Pede 2011) and a dynamic heterogeneous-coefficients panel data model based on an eigenvector-decomposition spatial filtering (SF) procedure (Griffith 2000(Griffith , 2003. The two models chosen belong to two separate traditions: VAR models represent the mainstream (time-series) forecasting tradition, while the SF-enhanced dynamic panel model attempts to merge the panel data modelling tradition to the spatial statistics one, within a semi-parametric framework.…”

Section: Introductionmentioning

confidence: 99%

Short-Run Regional Forecasts: Spatial Models Through Varying Cross-Sectional and Temporal Dimensions

Patuelli

Mayor

2012

In any economic analysis, regions or municipalities should not be regarded as isolated spatial units, but rather as highly interrelated small open economies. These spatial interrelations must be considered also when the aim is to forecast economic variables. For example, policy makers need accurate forecasts of the unemployment evolution in order to design short-or long-run local welfare policies. These predictions should then consider the spatial interrelations and dynamics of regional unemployment. In addition, a number of papers have demonstrated the improvement in the reliability of long-run forecasts when spatial dependence is accounted for. We estimate a heterogeneouscoefficients dynamic panel model employing a spatial filter in order to account for spatial heterogeneity and/or spatial autocorrelation in both the levels and the dynamics of unemployment, as well as a spatial vector-autoregressive (SVAR) model. We compare the short-run forecasting performance of these methods, and in particular, we carry out a sensitivity analysis in order to investigate if different number and size of the administrative regions influence their relative forecasting performance. We compute short-run unemployment forecasts in two countries with different administrative territorial divisions and data frequency: Switzerland (26 regions, monthly data for 34 years) and Spain (47 regions, quarterly data for 32 years) 2