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A Global Vector Autoregression (GVAR) model for regional labour markets and its forecasting performance with leading indicators in Germany

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  • Schanne, Norbert

    (IAB)

Abstract

"It is broadly accepted that two aspects regarding the modeling strategy are essential for the accuracy of forecast: a parsimonious model focusing on the important structures, and the quality of prospective information. Here, we establish a Global VAR framework, a technique that considers a variety of spatio-temporal dynamics in a multivariate setting, that allows for spatially heterogeneous slope coefficients, and that is nevertheless feasible for data without extremely long time dimension. Second, we use this framework to analyse the prospective information regarding the economy due to spatial co-development of regional labour markets in Germany. The predictive content of the spatially interdependent variables is compared with the information content of various leading indicators which describe the general economic situation, the tightness of labour markets and environmental impacts like weather. The forecasting accuracy of these indicators is investigated for German regional labour-market data in simulated forecasts at different horizons and for several periods. Germany turns out to have no economically dominant region (which reflects the polycentric structure of the country). The regions do not follow a joint stable long run trend which could be used to implement cointegration. Accounting for spatial dependence improves the forecast accuracy compared to a model without spatial linkages while using the same leading indicator. Amongst the tested leading indicators, only few produce more accurate forecasts when included in a GVAR model, than the GVAR without indicator. IAB-" (Author's abstract, IAB-Doku) ((en))

Suggested Citation

  • Schanne, Norbert, 2015. "A Global Vector Autoregression (GVAR) model for regional labour markets and its forecasting performance with leading indicators in Germany," IAB-Discussion Paper 201513, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    • Handle: RePEc:iab:iabdpa:201513
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    Cited by:

    1. Wiemers, Jürgen, 2015. "Endogenizing take-up of social assistance in a microsimulation model : a case study for Germany," IAB-Discussion Paper 201520, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    2. Chen, Y. & He, M. & Rudkin, S., 2017. "Understanding Chinese provincial real estate investment: A Global VAR perspective," Economic Modelling, Elsevier, vol. 67(C), pages 248-260.
    3. Christoph, Bernhard, 2015. "Empirische Maße zur Erfassung von Armut und materiellen Lebensbedingungen : Ansätze und Konzepte im Überblick," IAB-Discussion Paper 201525, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    4. Weigand Roland & Wanger Susanne & Zapf Ines, 2018. "Factor Structural Time Series Models for Official Statistics with an Application to Hours Worked in Germany," Journal of Official Statistics, Sciendo, vol. 34(1), pages 265-301, March.
    5. Zapf, Ines, 2015. "Who profits from working-time accounts? : empirical evidence on the determinants of working-time accounts on the employers' and employees' side," IAB-Discussion Paper 201523, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    6. Barbosa, Bruno Tebaldi de Queiroz & Marçal, Emerson Fernandes, 2018. "Modeling how macroeconomic shocks a ect regional employment: analyzing the Brazilian formal labor market using the global VAR approach," Textos para discussão 468, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).

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    More about this item

    Keywords

    Bundesrepublik Deutschland ; Indikatorenbildung ; Prognosegenauigkeit ; Prognosemodell ; regionale Faktoren;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

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