IDEAS home Printed from https://ideas.repec.org/a/rsr/supplm/v65y2017i3p40-48.html
   My bibliography  Save this article

Models and indicators used in macroeconomic forecast

Author

Listed:
  • Constantin ANGHELACHE

    (Bucharest University of Economic Studies/„Artifex„ University of Bucharest)

  • Mirela PANAIT

    (Petroleum-Gas University of Ploiesti)

  • Andreea - Ioana MARINESCU

    (Bucharest University of Economic Studies)

  • Georgiana NITA

    (Bucharest University of Economic Studies)

Abstract

In this article, the authors aim to analyze the links between certain macroeconomic indicators, using simple linear regression model and multiple. Thus, initially, will be addressed some general notions on macroeconomic forecast. Further extend the analysis will be used by applying simple linear regression models and multiple. The indicators used, GDP, consumption, export, import, is in fact variable interconnection. By using regression function, will be offered in terms of quantity, show the existence and intensity of existing interdependence and its analysis based on regression model. Using data series published by the National Statistics Institute, we look at the GDP in the period 1995-2015, the correlation between GDP and actual individual final consumption of households and links between GDP, on the one hand, and final consumption, the level of exports and imports, on the other hand, using multiple linear regression model.

Suggested Citation

  • Constantin ANGHELACHE & Mirela PANAIT & Andreea - Ioana MARINESCU & Georgiana NITA, 2017. "Models and indicators used in macroeconomic forecast," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 65(3), pages 40-48, March.
  • Handle: RePEc:rsr:supplm:v:65:y:2017:i:3:p:40-48
    as

    Download full text from publisher

    File URL: http://www.revistadestatistica.ro/supliment/wp-content/uploads/2017/03/A02_rrss_03_2017_EN.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Constantin ANGHELACHE & Janusz GRABARA & Alexandru MANOLE, 2016. "Using the Dynamic Model ARMA to Forecast the Macroeconomic Evolution," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 64(1), pages 3-13, January.
    2. Corbae,Dean & Durlauf,Steven N. & Hansen,Bruce E. (ed.), 2006. "Econometric Theory and Practice," Cambridge Books, Cambridge University Press, number 9780521807234, October.
    3. Ghysels,Eric & Osborn,Denise R., 2001. "The Econometric Analysis of Seasonal Time Series," Cambridge Books, Cambridge University Press, number 9780521565882, September.
    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. Constantin ANGHELACHE & Madalina-Gabriela ANGHEL & Gyorgy BODO, 2017. "Theoretical Aspects Of The Role Of Information In The Process Of Decisions/Risks Modeling," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 65(6), pages 102-111, June.
    2. Florin Paul Costel LILEA & Aurelian DIACONU & Radu Titus MARINESCU & Gyorgy BODO, 2017. "Structural Methods Used In Forecasting Studies," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 65(4), pages 66-74, April.
    3. Florin Paul Costel LILEA & Andreea – Ioana MARINESCU, 2017. "Macroeconomic Forecast Models – Concepts And Theoretical Notions," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 65(6), pages 118-123, June.

    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. Constantin ANGHELACHE & Alexandru MANOLE & Mădălina Gabriela ANGHEL, 2016. "The major economic evolution of Romania by the middle of 2016," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(4(609), W), pages 165-182, Winter.
    2. Altansukh, Gantungalag & Becker, Ralf & Bratsiotis, George J. & Osborn, Denise R., 2017. "What is the Globalisation of Inflation?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 74, pages 1-27.
    3. Florin Paul Costel LILEA & Andreea – Ioana MARINESCU, 2017. "Macroeconomic Forecast Models – Concepts And Theoretical Notions," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 65(6), pages 118-123, June.
    4. Gantungalag Altansukh & Ralf Becker & George Bratsiotis & Denise R. Osborn, 2018. "Structural Breaks in International Inflation Linkages for OECD Countries," Centre for Growth and Business Cycle Research Discussion Paper Series 240, Economics, The University of Manchester.
    5. Mario G.R. Pagliacci & Constantin Anghelache & Alexandru Manole & Madalina Gabriela Anghel, 2016. "The Econometric Model for the Economic and Financial Analysis of Romanian International Trade," Romanian Statistical Review, Romanian Statistical Review, vol. 64(3), pages 53-66, September.
    6. Constantin ANGHELACHE & Alexandru MANOLE & Mădălina Gabriela ANGHEL, 2016. "The major economic evolution of Romania by the middle of 2016," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(4(609), W), pages 165-182, Winter.
    7. Erdenebat Bataa & Denise R. Osborn & Marianne Sensier & Dick van Dijk, 2014. "Identifying Changes in Mean, Seasonality, Persistence and Volatility for G7 and Euro Area Inflation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(3), pages 360-388, June.
    8. Christoffersen, Peter & Ghysels, Eric & Swanson, Norman R., 2002. "Let's get "real" about using economic data," Journal of Empirical Finance, Elsevier, vol. 9(3), pages 343-360, August.
    9. Xilong Chen & Eric Ghysels, 2011. "News--Good or Bad--and Its Impact on Volatility Predictions over Multiple Horizons," The Review of Financial Studies, Society for Financial Studies, vol. 24(1), pages 46-81, October.
    10. Franses, Philip Hans, 2013. "Data revisions and periodic properties of macroeconomic data," Economics Letters, Elsevier, vol. 120(2), pages 139-141.
    11. Chambers, Marcus J. & Ercolani, Joanne S. & Taylor, A.M. Robert, 2014. "Testing for seasonal unit roots by frequency domain regression," Journal of Econometrics, Elsevier, vol. 178(P2), pages 243-258.
    12. Roberto Cellini & Tiziana Cuccia, 2013. "Museum and monument attendance and tourism flow: a time series analysis approach," Applied Economics, Taylor & Francis Journals, vol. 45(24), pages 3473-3482, August.
    13. del Barrio Castro, Tomás & Hecq, Alain, 2016. "Testing for deterministic seasonality in mixed-frequency VARs," Economics Letters, Elsevier, vol. 149(C), pages 20-24.
    14. repec:ebl:ecbull:v:30:y:2010:i:1:p:55-66 is not listed on IDEAS
    15. Cem Ertur & Antonio Musolesi, 2017. "Weak and Strong Cross‐Sectional Dependence: A Panel Data Analysis of International Technology Diffusion," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(3), pages 477-503, April.
    16. Curry, Bruce, 2007. "Neural networks and seasonality: Some technical considerations," European Journal of Operational Research, Elsevier, vol. 179(1), pages 267-274, May.
    17. Gospodinov, Nikolay & Otsu, Taisuke, 2012. "Local GMM estimation of time series models with conditional moment restrictions," Journal of Econometrics, Elsevier, vol. 170(2), pages 476-490.
    18. Oguzhan Cepni & I. Ethem Guney & Norman R. Swanson, 2020. "Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 18-36, January.
    19. Arouri, Mohamed El Hedi & Ben Youssef, Adel & M'henni, Hatem & Rault, Christophe, 2012. "Energy consumption, economic growth and CO2 emissions in Middle East and North African countries," Energy Policy, Elsevier, vol. 45(C), pages 342-349.
    20. Boldea, Otilia & Hall, Alastair R., 2013. "Estimation and inference in unstable nonlinear least squares models," Journal of Econometrics, Elsevier, vol. 172(1), pages 158-167.
    21. Christophe Rault & António Afonso, 2007. "Should we care for structural breaks when assessing fiscal sustainability?," Economics Bulletin, AccessEcon, vol. 3(63), pages 1-9.

    More about this item

    Keywords

    linear regression macro-economic indicator; GDP evolution; correlation; multiple regression; parameter;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E60 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General

    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:rsr:supplm:v:65:y:2017:i:3:p:40-48. 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: Adrian Visoiu (email available below). General contact details of provider: https://edirc.repec.org/data/stagvro.html .

    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.