IDEAS home Printed from https://ideas.repec.org/p/wiw/wiwrsa/ersa98p62.html

Measuring regional manufacturing production: An analysis for the Spanish regions

Author

Listed:
  • Miquel Clar

  • Raul Ramos

  • Jordi Suri?ach

Abstract

In a big amount of economies (NUTS-I) the evolution of manufacturing production is analysed using Gross Domestic Product (GDP) and Gross Added Value (GAV) data from National Accounts. In Spain, the problem of using these data is that they are not available as soon as it would be desirable. In consequence, it is not possible to analyse the short term evolution of the industrial output through them. To solve these problems the Institute of Statistics of Spain (Instituto Nacional de Estadistica -INE-) constructs a monthly Industrial Production Index (IPI) from data belonging to a survey addressed to firms. At a regional level (NUTS-II), the difficulties to monitor the evolution of manufacturing production are even bigger due to the nearly absence of official data. During the last years, different public and private institutions have started to construct indices for some Spanish regions, but they do not use an homogeneus methodology and the indices are not directly comparable. In this paper, we summarize and extent the main results of previous studies about the possibility of using different indirect methods to analyse the short term evolution of regional industrial production. In concrete, two statistic and an econometric method are considered. First, we study the possibility of extending the methodology proposed by the Regional Institute of Statistics of Catalonia (Institut d'Estadistica de Catalunya -IEC-) to other Spanish Regions. Second, we analyse the relationships between electric energy consumption for industrial purposes and industrial production. Third, following Israilevich and Kuttner (1993), we apply a state-space model to obtain estimates of the industrial production indices using the Kalman Filter and the method of maximum likelihood. Next, to validate the indices obtained through these three methods we compare them with regional indices obtained by direct methods for the regions where they exist. Finally, we expose the main conclusions remarking the implications for public policy in relation with elaboration of regional statistics.

Suggested Citation

  • Miquel Clar & Raul Ramos & Jordi Suri?ach, 1998. "Measuring regional manufacturing production: An analysis for the Spanish regions," ERSA conference papers ersa98p62, European Regional Science Association.
    • Handle: RePEc:wiw:wiwrsa:ersa98p62
    as

    Download full text from publisher

    File URL: https://www-sre.wu.ac.at/ersa/ersaconfs/ersa98/papers/62.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. R. H. Shumway & D. S. Stoffer, 1982. "An Approach To Time Series Smoothing And Forecasting Using The Em Algorithm," Journal of Time Series Analysis, Wiley Blackwell, vol. 3(4), pages 253-264, July.
    2. Andrew C. Harvey, 1990. "The Econometric Analysis of Time Series, 2nd Edition," MIT Press Books, The MIT Press, edition 2, volume 1, number 026208189x, December.
    Full references (including those not matched with items on IDEAS)

    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. Mazzocchi, Mario, 2006. "Time patterns in UK demand for alcohol and tobacco: an application of the EM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2191-2205, May.
    2. Rocha, Roberto de Rezende, 1991. "Inflation and stabilization in Yugoslavia," Policy Research Working Paper Series 752, The World Bank.
    3. Campos, Nauro & Nugent, Jeffrey B, 2000. "Investment and Instability," CEPR Discussion Papers 2609, Centre for Economic Policy Research.
    4. Tobias Hartl & Roland Jucknewitz, 2022. "Approximate state space modelling of unobserved fractional components," Econometric Reviews, Taylor & Francis Journals, vol. 41(1), pages 75-98, January.
    5. David de Antonio Liedo, 2014. "Nowcasting Belgium," Working Paper Research 256, National Bank of Belgium.
    6. Tommaso Proietti, 2008. "Estimation of Common Factors Under Cross-Sectional and Temporal Aggregation Constraints: Nowcasting Monthly GDP and Its Main Components," Springer Books, in: Paula Brito (ed.), Compstat 2008, pages 547-558, Springer.
    7. Matteo Barigozzi & Marc Hallin, 2023. "Dynamic Factor Models: a Genealogy," Papers 2310.17278, arXiv.org, revised Jan 2024.
    8. Alexander Tsyplakov, 2011. "An introduction to state space modeling (in Russian)," Quantile, Quantile, issue 9, pages 1-24, July.
    9. Loriana Pelizzon & Roberto Casarin & Andrea Piva, 2008. "Italian Equity Funds: Efficiency and Performance Persistence," Working Papers 2008_12, Department of Economics, University of Venice "Ca' Foscari".
    10. Isabel Figuerola-Ferretti, 2005. "Prices and production cost in aluminium smelting in the short and the long run," Applied Economics, Taylor & Francis Journals, vol. 37(8), pages 917-928.
    11. Sayat R. Baronyan & İ. İlkay Boduroğlu & Emrah Şener, 2010. "Investigation Of Stochastic Pairs Trading Strategies Under Different Volatility Regimes," Manchester School, University of Manchester, vol. 78(s1), pages 114-134, September.
    12. Hindrayanto, Irma & Koopman, Siem Jan & de Winter, Jasper, 2016. "Forecasting and nowcasting economic growth in the euro area using factor models," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1284-1305.
    13. Chris Gardiner & John Henneberry, 1995. "Analysing the property-gilts yield differential," Applied Economics Letters, Taylor & Francis Journals, vol. 2(1), pages 12-15.
    14. Garsztka Przemysław & Hołubowicz Krzysztof, 2015. "The Application of Asymmetric Liquidity Risk Measure in Modelling the Risk of Investment," Folia Oeconomica Stetinensia, Sciendo, vol. 15(1), pages 83-100, June.
    15. Lauridsen, J. & Kosfeld, R., 2004. "A wald Test for Spatial Nonstationarity," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 22, pages 1-12, Diciembre.
    16. Charles Engel & James Morley, 2000. "The Adjustment of Prices and the Adjustment of the Exchange Rate," Working Papers 0009, University of Washington, Department of Economics.
    17. Muhammad, Andrew & Meade, Birgit Gisela Saager & Regmi, Anita & Seale, James L., 2011. "International Evidence on Food Consumption Patterns: An Update Using 2005 International Comparison Program Data," Technical Bulletins 120252, United States Department of Agriculture, Economic Research Service.
    18. Peña, Daniel & Poncela, Pilar, 1996. "Pooling information and forecasting with dynamic factor analysis," DES - Working Papers. Statistics and Econometrics. WS 10709, Universidad Carlos III de Madrid. Departamento de Estadística.
    19. Pantelis Kalaitzidakis & George Korniotis, 2000. "The Solow growth model: vector autoregression (VAR) and cross-section time-series analysis," Applied Economics, Taylor & Francis Journals, vol. 32(6), pages 739-747.
    20. Daan Opschoor & Dick van Dijk, 2025. "Slow Expectation–Maximization Convergence in Low‐Noise Dynamic Factor Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(7), pages 829-845, November.

    More about this item

    Statistics

    Access and download statistics

    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:wiw:wiwrsa:ersa98p62. 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: Gunther Maier (email available below). General contact details of provider: http://www.ersa.org .

    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.