Regional unemployment forecasts with spatial interdependencies

Schanne, Norbert; Wapler, Rüdiger; Weyh, Antje

doi:10.1016/j.ijforecast.2009.07.002

Cited by 42 publications

(21 citation statements)

References 32 publications

(25 reference statements)

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“…A dynamic spatial panel data model is used by Khoodilin et al (2008) conclude that accounting for spatial effects improves forecast performance, and this improvement is more important when the forecasting horizon is longer. Schanne et al (2010) reach a similar conclusion comparing a univariate spatial GVAR model with univariate time series methods.…”

Section: Introductionsupporting

confidence: 64%

“…Following the same idea, Di Giacinto (2003) defines parameter constraints in a structural VAR model based on neighbouring structure, allowing the identification and estimation of the spatial VAR model. A further option is developed by Schanne et al (2010), based on the Global VAR (GVAR) model proposed by Pesaran et al (2004), where geographical information is used to include spatial connections between regions. One of the novelties (or advantages) of the GVAR model consists in the inclusion of a temporal dimension within the spatial dependence process.…”

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

confidence: 99%

See 1 more Smart Citation

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

Mayor

Patuelli

2012

Defining the Spatial Scale in Modern Regional Analysis

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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

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Section: Introductionsupporting

confidence: 64%

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

confidence: 99%

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

Mayor

Patuelli

2012

Defining the Spatial Scale in Modern Regional Analysis

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show abstract

“…This strategy enables countries not only to solve special economic tasks but also build upon the forecasted equalization of social, political, and other living conditions of the population. It is assumed that that "similarity" of economic structure will result in "similarity" of employment structure [6][7][8], which is due to ensure similarity of social structure. This approach leads to equal proportions of the formation and distribution of collectivized financial funds and thus to equal access to budget services.…”

Section: Literature Reviewmentioning

confidence: 99%

Transformation of the Structure of the Gross Regional Product in Siberian Regions

Gamukin¹,

Belomyttseva²,

Grinkevich³

2017

Proceedings of the International Conference on Trends of Technologies and Innovations in Economic and Social Studies 2017

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Abstract-Stability of the national economy is determined by the capacity of regional economies to adapt to the phenomenon of damping external and internal risk factors. In this paper, the authors consider the following main strategies for developing the structure of regional economies: (1) uniforming of the structure of regional economies; (2) individualization of the structure of regional economies; and (3) integration of regions with different structures of their economies into macroregions, where equalization occurs. For the purpose of selection, we resort to the calculation of Ryabtsev, Szalai indices, and the index of structure using the gross regional product of subjects of the Siberian Federal District for the period of 2005-2014. A universal method for the assessment of convergence of regional economies' structures, which is based on gross regional product, has been developed and proposed for testing on the example of other Russian macroregions.

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“…Since the underlying paper is not interested in modeling regions (as it is done in Schanne et al (2010), for instance) but rather in constructing a new unemployment leading indicator for Germany, our focus is on the aggregate approach. As a consequence, the natural way to condense the answers from the local agencies is to average over all cross-section units using some sort of weights in order to account for the different sizes of the local agencies.…”

Section: The Novel Unemployment Leading Indicatormentioning

confidence: 99%

Constructing a new leading indicator for unemployment from a survey among German employment agencies

Hütter

Weber

2015

Applied Economics

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Regional unemployment forecasts with spatial interdependencies

Cited by 42 publications

References 32 publications

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

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

Transformation of the Structure of the Gross Regional Product in Siberian Regions

Constructing a new leading indicator for unemployment from a survey among German employment agencies

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