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Forecasting Regional Employment with Shift-Share and ARIMA Modelling

Author

Listed:
  • Matias Mayor
  • Ana Jesus Lopez
  • Rigoberto Perez

Abstract

Mayor M., Lopez A. J. and Perez R. (2007) Forecasting regional employment with shift-share and ARIMA modelling, Regional Studies 41, 543-551. The analysis of different economic situations and risk factors is necessary in order to define forecasting scenarios properly. The paper focuses on the shift-share model as a useful tool in the definition of scenarios, based on the different components contributing to the change of a given economic variable (national, sectoral and competitive effects). Although the most commonly used methodology is based on the 'constant-shift' and 'constant-share' hypotheses, additional options can be considered to lead to more realistic scenarios. More specifically, a dynamic shift-share formulation is proposed that allows time changes in both the sectoral structure and the level of the considered variable. Once this new option has been developed, this approach is applied to define scenarios and forecast the regional employment in Asturias using the information provided by the Spanish Economically Active Population Survey (Encuesta de Poblacion Activa, EPA). Mayor M., Lopez A. J. et Perez R. (2007) La prevision de l'emploi regional a partir de la technique shift-share et de la modelisation ARIMI, Regional Studies 41, 543-551. Il faut l'analyse de differentes situations economiques et facteurs de risque pour definir avec precision des prospectives. Cet article porte sur la technique comme outil utile pour definir des prospectives, fonde sur la variation d'une variable economique donnee (effets nationaux, sectoriels, et concurrentiels). Bien que la methodologie la plus frequemment employee soit fondee sur les hypotheses dits 'constant-shift' et 'constant-share', on peut considerer d'autres possibilites, ce qui entraine des prospectives plus realistes. Plus particulierement, on propose une elaboration dynamique de la technique shift-share, permettant l'evolution temporelle de la struture sectorielle et du niveau de la variable en question. Une fois developpee, cette nouvelle possibilite se voit appliquer afin de definir des prospectives et de prevoir l'emploi regional en Asturias a partir des donnees provenant de la Encuesta de Poblacion Activa, EPA, (enquete sur la population active en Espagne). Prevision Shift-share Prospectives Enquete sur la population active (EPA) Mayor M., Lopez A. J. und Perez R. (2007) Prognose des regionalen Beschaftigungsniveaus durch Shift-Share- und ARIMA-Modelle, Regional Studies 41, 543-551. Fur eine brauchbare Definition von Prognoseszenarien ist eine Analyse verschiedener wirtschaftlicher Situationen und Risikofaktoren erforderlich. In diesem Beitrag konzentrieren wir uns auf das Shift-Share-Modell als nutzliches Instrument zur Definition von Szenarien anhand der verschiedenen Komponenten, die zur Veranderung einer gegebenen wirtschaftlichen Variablen (nationale, sektorale und wettbewerbsbezogene Auswirkungen) beitragen. Die gangige Methodologie basiert auf den Hypothesen einer konstanten Veranderung ('Shift') und eines konstanten Anteils ('Share'), doch es lassen sich auch weitere Optionen berucksichtigen, die zu realistischeren Szenarien fuhren. Insbesondere schlagen wir eine dynamische Shift-Share-Formulierung vor, durch die zeitliche Veranderungen hinsichtlich der sektoralen Struktur und der Hohe der untersuchten Variablen berucksichtigt werden konnen. Nach Entwicklung dieser neuen Option wird dieser Ansatz zur Definition von Szenarien und zur Prognose des regionalen Beschaftigungsniveaus in Asturien anhand der Informationen der spanischen Erhebung uber die erwerbstatige Bevolkerung (Encuesta de Poblacion Activa, EPA) angewandt. Prognosen Shift-Share Szenarien Erhebung uber erwerbstatige Bevolkerung (EPA) Mayor M., Lopez A. J. y Perez R. (2007) Prediccion del empleo regional con modelos shift-share y ARIMA, Regional Studies 41, 543-551. Para poder definir adecuadamente los escenarios de prediccion es necesario analizar las diferentes situaciones economicas y los factores de riesgo. En este articulo destacamos que el modelo shift-share es una herramienta util para definir escenarios, basados en los diferentes componentes que contribuyen a cambiar una determinada variable economica (efectos nacionales, sectoriales y competitivos). Aunque la metodologia que se usa con mas frecuencia se basa en las hipotesis 'constant-shift' y 'constant-share', podemos considerar opciones adicionales que nos llevan a situaciones mas reales. En concreto proponemos una formulacion dinamica shift-share incorporando los cambios en el periodo analizado en la estructura sectorial y en el nivel de la variable considerada. Una vez desarrollada esta nueva opcion, aplicamos este enfoque para definir los escenarios y el empleo regional en Asturias usando la informacion proporcionada por la Encuesta de Poblacion Activa (EPA). Prediccion Shift-share Ejemplos Encuesta de Poblacion Activa (EPA)

Suggested Citation

  • Matias Mayor & Ana Jesus Lopez & Rigoberto Perez, 2007. "Forecasting Regional Employment with Shift-Share and ARIMA Modelling," Regional Studies, Taylor & Francis Journals, vol. 41(4), pages 543-551.
    • Handle: RePEc:taf:regstd:v:41:y:2007:i:4:p:543-551
    DOI: 10.1080/00343400601120205
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    References listed on IDEAS

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    2. Chico, José Ruiz & Sánchez, Antonio Rafael Peña & García, Mercedes Jiménez, 2021. "Análisis de competitividad de las exportaciones españolas con destino a la Unión Europea: especial énfasis en el sector agroalimentario," Revista de Economia e Sociologia Rural (RESR), Sociedade Brasileira de Economia e Sociologia Rural, vol. 59(4), January.
    3. Robert Lehmann & Klaus Wohlrabe, 2014. "Regional economic forecasting: state-of-the-art methodology and future challenges," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 218-231.
    4. Ahasanul Haque & Naila Anwar & Zariyah Ibrahim, 2013. "Export of Furniture Product from Malaysia: Market Prospects and Challenges," Journal of Economics and Behavioral Studies, AMH International, vol. 5(7), pages 406-419.
    5. Federico Lampis, 2016. "Forecasting the sectoral GVA of a small Spanish region," Economics and Business Letters, Oviedo University Press, vol. 5(2), pages 38-44.
    6. Hyun‐kyung Lee & Hong‐bae Kim, 2020. "Weighted spatial dynamic shift‐share model as a forecasting tool for a regional economy: The case of South Korea," Growth and Change, Wiley Blackwell, vol. 51(2), pages 734-748, June.

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