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A Generalized Space-Time ARMA Model with an Application to Regional Unemployment Analysis in Italy

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  • Valter Di Giacinto

    (Banca d’Italia, L’Aquila Regional Economic Research Unit, L’Aquila, Italy, digiacinto.valter@bancaditalia.it)

Abstract

The STARMA (space-time autoregressive moving average) model class was introduced in the mid-1970s as a spatio-temporal extension to the ARMA time series model class. To enhance the model’s ability in dealing with spatial dependence and heterogeneity of observations, the article extends the STARMA model specification by augmenting the set of explanatory variables with simultaneous spatial lags of the observed process and unobservable shocks and by letting model parameters vary with location in space. Having introduced the extended specification,the space time impulse response function is subsequently presented as a useful tool in addressing structural issues. The article then deals with maximum likelihood estimation and hypothesis testing; in particular, Lagrange multiplier tests are proposed for spatial heterogeneity in the intercept, conditional variance and ARMA coefficients. The article closes with an application to the analysis of the series of the regional unemployment rate in Italy, aimed at evaluating the extent of the spatial propagation of regional specific shocks to unemployment and the degree of spatial heterogeneity in the process parameters.

Suggested Citation

  • Valter Di Giacinto, 2006. "A Generalized Space-Time ARMA Model with an Application to Regional Unemployment Analysis in Italy," International Regional Science Review, , vol. 29(2), pages 159-198, April.
    • Handle: RePEc:sae:inrsre:v:29:y:2006:i:2:p:159-198
    DOI: 10.1177/0160017605279457
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    References listed on IDEAS

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    Cited by:

    1. Svetlana Borovkova & Hendrik P. Lopuhaä & Budi Nurani Ruchjana, 2008. "Consistency and asymptotic normality of least squares estimators in generalized STAR models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 62(4), pages 482-508, November.
    2. Pablo Fernández & Marcos Herrera Gómez, 2023. "Regresiones SUR Espaciales. Análisis Espacio-temporal del Empleo Sectorial en Argentina," Working Papers 279, Red Nacional de Investigadores en Economía (RedNIE).
    3. Ryan H. L. Ip & Dmitry Demskoi & Azizur Rahman & Lihong Zheng, 2021. "Evaluation of COVID-19 Mitigation Policies in Australia Using Generalised Space-Time Autoregressive Intervention Models," IJERPH, MDPI, vol. 18(14), pages 1-17, July.
    4. Valter Di Giacinto, 2013. "The dynamics of knowledge production in European regions," ERSA conference papers ersa13p543, European Regional Science Association.
    5. Valter Giacinto, 2010. "On vector autoregressive modeling in space and time," Journal of Geographical Systems, Springer, vol. 12(2), pages 125-154, June.
    6. Di Caro, Paolo, 2014. "Regional recessions and recoveries in theory and practice: a resilience-based overview," MPRA Paper 60300, University Library of Munich, Germany.
    7. Jesús Mur & Fernando López & Marcos Herrera, 2010. "Testing for Spatial Effects in Seemingly Unrelated Regressions," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(4), pages 399-440.
    8. Fernando López & Jesús Mur & Ana Angulo, 2014. "Spatial model selection strategies in a SUR framework. The case of regional productivity in EU," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 53(1), pages 197-220, August.
    9. Julian Ramajo & Miguel A. Marquez & Geoffrey J.D. Hewings, 2013. "Spatio-temporal Analysis of Regional Systems: A Multiregional Spatial Vector Autoregressive Model for Spain," ERSA conference papers ersa13p159, European Regional Science Association.
    10. Massimiliano Agovino & Antonio Garofalo, 2013. "Dipendenza spaziale contemporanea e non contemporanea nei tassi di disoccupazione: un tentativo di analisi empirica dei dati provinciali italiani," RIVISTA DI ECONOMIA E STATISTICA DEL TERRITORIO, FrancoAngeli Editore, vol. 2013(3), pages 45-82.
    11. Yang, Yang & Zhang, Honglei, 2019. "Spatial-temporal forecasting of tourism demand," Annals of Tourism Research, Elsevier, vol. 75(C), pages 106-119.
    12. Gehman, Andrew & Wei, William W.S., 2020. "Optimal spatial aggregation of space–time models and applications," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
    13. Miguel A. Márquez & Julián Ramajo & Geoffrey JD. Hewings, 2015. "Regional growth and spatial spillovers: Evidence from an SpVAR for the Spanish regions," Papers in Regional Science, Wiley Blackwell, vol. 94, pages 1-18, November.
    14. Safikhani, Abolfazl & Kamga, Camille & Mudigonda, Sandeep & Faghih, Sabiheh Sadat & Moghimi, Bahman, 2020. "Spatio-temporal modeling of yellow taxi demands in New York City using generalized STAR models," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1138-1148.
    15. Sergio Destefanis & Valter Di Giacinto, 2023. "EU structural funds and GDP per capita: spatial VAR evidence for the European regions," Temi di discussione (Economic working papers) 1409, Bank of Italy, Economic Research and International Relations Area.
    16. Paelinck, Jean H.P. & Mur, Jesús, 2018. "Some Issues on the Concept of Causality in Spatial Econometric Models/Algunos aspectos del concepto de causalidad en modelos econométricos espaciales," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 36, pages 107-118, Enero.
    17. López-Hernández, Fernando A. & Chasco, Coro, 2007. "TIME-TREND IN SPATIAL DEPENDENCE: SPECIFICATION STRATEGY IN THE FIRST-ORDER SPATIAL AUTOREGRESSIVE MODEL/Tendencia temporal en la dependencia espacial: estrategia de modelización en el modelo autorreg," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 25, pages 631-650, Abril.
    18. Valter Di Giacinto, 2011. "Foreign trade, home linkages and the spatial transmission of economic fluctuations in Italy," Temi di discussione (Economic working papers) 827, Bank of Italy, Economic Research and International Relations Area.

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