IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i17p5458-d627486.html
   My bibliography  Save this article

Projection of Post-Pandemic Italian Industrial Production through Vector AutoRegressive Models

Author

Listed:
  • Antonio Oliva

    (MAHTEP Group, Dipartimento Energia “Galileo Ferraris”, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy)

  • Francesco Gracceva

    (Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Lungotevere Thaon di Revel, 76, 00196 Rome, Italy)

  • Daniele Lerede

    (MAHTEP Group, Dipartimento Energia “Galileo Ferraris”, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy)

  • Matteo Nicoli

    (MAHTEP Group, Dipartimento Energia “Galileo Ferraris”, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy)

  • Laura Savoldi

    (MAHTEP Group, Dipartimento Energia “Galileo Ferraris”, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy)

Abstract

Energy system models for the analysis of future scenarios are mainly driven by the set of energy service demands that define the broad outlines of socio-economic development throughout the model time horizon. Here, the long-term effects of the COVID-19 pandemic on the drivers of the industrial production in six energy-intensive subsectors are addressed using Vector AutoRegressive models. The model results are computed either considering or not considering the effects of the pandemic. The comparison to established pre-pandemic trends allows for validating the robustness of the selected model. The anticipated effect of the pandemic to 2040 shows a long-term reduction by 3% to 10%, according to the different subsector, in the industrial energy service demand. When the computed service demands are used as input to the TIMES-Italy model, which shows good capability to reproduce the energy consumption of the industrial sectors in the period 2006–2020, the impact of the pandemic on energy consumption forecasts can be assessed in a business-as-usual scenario. The results show how the long-term effects of the shock caused by the pandemic could lead, by 2040, to a total industrial energy consumption 5% lower than what was foreseen before the pandemic, while the energy mix remains almost unchanged.

Suggested Citation

  • Antonio Oliva & Francesco Gracceva & Daniele Lerede & Matteo Nicoli & Laura Savoldi, 2021. "Projection of Post-Pandemic Italian Industrial Production through Vector AutoRegressive Models," Energies, MDPI, vol. 14(17), pages 1-18, September.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:17:p:5458-:d:627486
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/17/5458/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/17/5458/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Editorial, 2020. "Covid-19 and Climate Change," Journal, Review of Agrarian Studies, vol. 10(1), pages 5-6, January-J.
    2. Pu Chen & Joachim Frohn, 2006. "On the Specification and Estimation of Large Scale Simultaneous Structural Models," Springer Books, in: Olaf Hübler & Jachim Frohn (ed.), Modern Econometric Analysis, chapter 2, pages 7-24, Springer.
    3. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    4. Zhang, Runsen & Zhang, Junyi, 2021. "Long-term pathways to deep decarbonization of the transport sector in the post-COVID world," Transport Policy, Elsevier, vol. 110(C), pages 28-36.
    5. Hunt, Lester C. & Ryan, David L., 2015. "Economic modelling of energy services: Rectifying misspecified energy demand functions," Energy Economics, Elsevier, vol. 50(C), pages 273-285.
    6. Jiang, Peng & Fan, Yee Van & Klemeš, Jiří Jaromír, 2021. "Impacts of COVID-19 on energy demand and consumption: Challenges, lessons and emerging opportunities," Applied Energy, Elsevier, vol. 285(C).
    7. Sims, Christopher A & Zha, Tao, 1998. "Bayesian Methods for Dynamic Multivariate Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 949-968, November.
    8. Lerede, D. & Bustreo, C. & Gracceva, F. & Saccone, M. & Savoldi, L., 2021. "Techno-economic and environmental characterization of industrial technologies for transparent bottom-up energy modeling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    9. Foroni, Claudia & Marcellino, Massimiliano & Stevanovic, Dalibor, 2022. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," International Journal of Forecasting, Elsevier, vol. 38(2), pages 596-612.
    10. DeCarolis, Joseph F. & Hunter, Kevin & Sreepathi, Sarat, 2012. "The case for repeatable analysis with energy economy optimization models," Energy Economics, Elsevier, vol. 34(6), pages 1845-1853.
    11. Pu Chen & Joachim Frohn, 2006. "On the specification and estimation of large scale simultaneous structural macroeconometric models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 9-25, March.
    12. P. Capros & Denise Van Regemorter & Leonidas Paroussos & P. Karkatsoulis & C. Fragkiadakis & S. Tsani & I. Charalampidis & Tamas Revesz, 2013. "GEM-E3 Model Documentation," JRC Research Reports JRC83177, Joint Research Centre.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Matteo Nicoli & Francesco Gracceva & Daniele Lerede & Laura Savoldi, 2022. "Can We Rely on Open-Source Energy System Optimization Models? The TEMOA-Italy Case Study," Energies, MDPI, vol. 15(18), pages 1-37, September.
    2. Clio Ciaschini & Margherita Carlucci & Francesco Maria Chelli & Giuseppe Ricciardo Lamonica & Luca Salvati, 2023. "COVID-19 and decreasing consumption: a multisectoral assesment for Italy," Economics Bulletin, AccessEcon, vol. 43(2), pages 1162-1171.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez, 2001. "Comparing dynamic equilibrium economies to data," FRB Atlanta Working Paper 2001-23, Federal Reserve Bank of Atlanta.
    2. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2018. "Bayesian Vector Autoregressions," The Warwick Economics Research Paper Series (TWERPS) 1159, University of Warwick, Department of Economics.
    3. Scholl, Almuth & Uhlig, Harald, 2008. "New evidence on the puzzles: Results from agnostic identification on monetary policy and exchange rates," Journal of International Economics, Elsevier, vol. 76(1), pages 1-13, September.
    4. Carlo A. Favero, 2007. "Model Evaluation in Macroeconometrics: from early empirical macroeconomic models to DSGE models," Working Papers 327, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    5. Marek Rusnak & Tomas Havranek & Roman Horvath, 2013. "How to Solve the Price Puzzle? A Meta-Analysis," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(1), pages 37-70, February.
    6. Tomasz Woźniak, 2016. "Bayesian Vector Autoregressions," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 49(3), pages 365-380, September.
    7. Leeper, Eric M. & Zha, Tao, 2003. "Modest policy interventions," Journal of Monetary Economics, Elsevier, vol. 50(8), pages 1673-1700, November.
    8. Jarociński, Marek & Marcet, Albert, 2019. "Priors about observables in vector autoregressions," Journal of Econometrics, Elsevier, vol. 209(2), pages 238-255.
    9. Espinosa Acuña, Óscar A. & Vaca González, Paola A. & Avila Forero, Raúl A., 2013. "Elasticidades de demanda por electricidad e impactos macroecon_omicos del precio de la energía eléctrica en Colombia || Elasticity of Electricity Demand and Macroeconomics Impacts of Electricity Price," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 16(1), pages 216-249, December.
    10. Ricco, Giovanni & Callegari, Giovanni & Cimadomo, Jacopo, 2014. "Signals from the Government: Policy Uncertainty and the Transmission of Fiscal Shocks," MPRA Paper 56136, University Library of Munich, Germany.
    11. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015. "Prior Selection for Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 436-451, May.
    12. Bin Jiang & Anastasios Panagiotelis & George Athanasopoulos & Rob Hyndman & Farshid Vahid, 2016. "Bayesian Rank Selection in Multivariate Regression," Monash Econometrics and Business Statistics Working Papers 6/16, Monash University, Department of Econometrics and Business Statistics.
    13. Demeshev, Boris & Malakhovskaya, Oxana, 2016. "BVAR mapping," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 43, pages 118-141.
    14. Warne, Anders & Coenen, Günter & Christoffel, Kai, 2010. "Forecasting with DSGE models," Working Paper Series 1185, European Central Bank.
    15. Marek Jarocinski, 2010. "Responses to monetary policy shocks in the east and the west of Europe: a comparison," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(5), pages 833-868.
    16. Chaofeng Tang & Kentaka Aruga, 2021. "Effects of the 2008 Financial Crisis and COVID-19 Pandemic on the Dynamic Relationship between the Chinese and International Fossil Fuel Markets," JRFM, MDPI, vol. 14(5), pages 1-11, May.
    17. Kaabia, Olfa & Abid, Ilyes & Guesmi, Khaled, 2013. "Does Bayesian shrinkage help to better reflect what happened during the subprime crisis?," Economic Modelling, Elsevier, vol. 31(C), pages 423-432.
    18. Florian Huber & Luca Rossini, 2020. "Inference in Bayesian Additive Vector Autoregressive Tree Models," Papers 2006.16333, arXiv.org, revised Mar 2021.
    19. Koop, Gary & Korobilis, Dimitris, 2010. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(4), pages 267-358, July.
    20. Uhlig, Harald, 2005. "What are the effects of monetary policy on output? Results from an agnostic identification procedure," Journal of Monetary Economics, Elsevier, vol. 52(2), pages 381-419, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:17:p:5458-:d:627486. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.