IDEAS home Printed from https://ideas.repec.org/a/dug/journl/y2012i4p106-128.html
   My bibliography  Save this article

Short run and alternative macroeconomic forecasts for Romania and strategies to improve their accuracy

Author

Listed:
  • BRATU (SIMIONESCU) MIHAELA

    (Academy of Economic Studies Bucharest)

  • MARIN ERIKA

    (Academy of Economic Studies Bucharest)

Abstract

For the same macroeconomic variables more predictions can be made, using different forecasting forecasting. The most important step is the choice of the prediction with the highest degree of accuracy, this being used in establishing the governmental policies or the monetary policy by the central bank. We made short run forecasts (January 2012-March 2012) for variables as inflation rate, unemployment rate and interest rate for Romania using techniques like: econometric modeling, exponential smoothing technique and moving average method. In order to improve the forecasts accuracy, we used two empirical strategies: making combined prognosis and building the forecasts based on historical accuracy indicators. The predictions based on exponential smoothing technique have the highest degree of acuracy, being superior to those got applying the strategies of improving the accuracy.

Suggested Citation

  • Bratu (Simionescu) Mihaela & Marin Erika, 2012. "Short run and alternative macroeconomic forecasts for Romania and strategies to improve their accuracy," EuroEconomica, Danubius University of Galati, issue 4(31), pages 106-128, November.
    • Handle: RePEc:dug:journl:y:2012:i:4:p:106-128
    as

    Download full text from publisher

    File URL: http://journals.univ-danubius.ro/index.php/euroeconomica/article/view/1426/1418
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    2. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    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. Frank, Johannes, 2023. "Forecasting realized volatility in turbulent times using temporal fusion transformers," FAU Discussion Papers in Economics 03/2023, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    2. Cameron Roach & Rob Hyndman & Souhaib Ben Taieb, 2021. "Non‐linear mixed‐effects models for time series forecasting of smart meter demand," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1118-1130, September.
    3. Nikitopoulos, Christina Sklibosios & Thomas, Alice Carole & Wang, Jianxin, 2023. "The economic impact of daily volatility persistence on energy markets," Journal of Commodity Markets, Elsevier, vol. 30(C).
    4. Martin Guth, 2022. "Predicting Default Probabilities for Stress Tests: A Comparison of Models," Papers 2202.03110, arXiv.org.
    5. Kunze, Frederik, 2017. "Predicting exchange rates in Asia: New insights on the accuracy of survey forecasts," University of Göttingen Working Papers in Economics 326, University of Goettingen, Department of Economics.
    6. Drachal, Krzysztof, 2021. "Forecasting crude oil real prices with averaging time-varying VAR models," Resources Policy, Elsevier, vol. 74(C).
    7. Blaskowitz, Oliver & Herwartz, Helmut, 2011. "On economic evaluation of directional forecasts," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1058-1065, October.
    8. Martin Magris, 2019. "A Vine-copula extension for the HAR model," Papers 1907.08522, arXiv.org.
    9. Isabel Figuerola‐Ferretti & Alejandro Rodríguez & Eduardo Schwartz, 2021. "Oil price analysts' forecasts," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(9), pages 1351-1374, September.
    10. Bekiroglu, Korkut & Duru, Okan & Gulay, Emrah & Su, Rong & Lagoa, Constantino, 2018. "Predictive analytics of crude oil prices by utilizing the intelligent model search engine," Applied Energy, Elsevier, vol. 228(C), pages 2387-2397.
    11. Potočnik, Primož & Soldo, Božidar & Šimunović, Goran & Šarić, Tomislav & Jeromen, Andrej & Govekar, Edvard, 2014. "Comparison of static and adaptive models for short-term residential natural gas forecasting in Croatia," Applied Energy, Elsevier, vol. 129(C), pages 94-103.
    12. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
    13. Oscar Claveria, 2018. "“A new metric of consensus for Likert scales”," IREA Working Papers 201821, University of Barcelona, Research Institute of Applied Economics, revised Oct 2018.
    14. Huang, Tao & Fildes, Robert & Soopramanien, Didier, 2019. "Forecasting retailer product sales in the presence of structural change," European Journal of Operational Research, Elsevier, vol. 279(2), pages 459-470.
    15. Eckert, Florian & Hyndman, Rob J. & Panagiotelis, Anastasios, 2021. "Forecasting Swiss exports using Bayesian forecast reconciliation," European Journal of Operational Research, Elsevier, vol. 291(2), pages 693-710.
    16. Mihaela Bratu, 2012. "A Strategy to Improve the Survey of Professional Forecasters (SPF) Predictions Using Bias-Corrected-Accelerated (BCA) Bootstrap Forecast Intervals," International Journal of Synergy and Research, ToKnowPress, vol. 1(2), pages 45-59.
    17. Krzysztof Drachal, 2022. "Forecasting the Crude Oil Spot Price with Bayesian Symbolic Regression," Energies, MDPI, vol. 16(1), pages 1-29, December.
    18. Xu, Kunliang & Wang, Weiqing, 2023. "Limited information limits accuracy: Whether ensemble empirical mode decomposition improves crude oil spot price prediction?," International Review of Financial Analysis, Elsevier, vol. 87(C).
    19. Anders Eriksson & Daniel P. A. Preve & Jun Yu, 2019. "Forecasting Realized Volatility Using a Nonnegative Semiparametric Model," JRFM, MDPI, vol. 12(3), pages 1-23, August.
    20. Gneiting, Tilmann, 2011. "Making and Evaluating Point Forecasts," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 746-762.

    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:dug:journl:y:2012:i:4:p:106-128. 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: Florian Nuta (email available below). General contact details of provider: https://edirc.repec.org/data/fedanro.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.