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Methods And Techniques For Preparing Forecasts


  • Constantin ANGHELACHE

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

  • Madalina-Gabriela ANGHEL

    („Artifex" University of Bucharest)

  • Tudor SAMSON

    (Bucharest University of Economic Studies)

  • Radu STOICA

    (Bucharest University of Economic Studies)


In this article, the authors suggest, from the importance it has forecast (economic forecast) to present the main issues that we encounter in this process and, more importantly, to present the main methods and techniques for developing these studies . If any economy it is important to know how the economy evolved under the influence of various internal and external factors. For this it needs to clarify some aspects of the methodology of economic forecasting in general and the macroeconomic, in particular. In developing the methodological framework of macroeconomic forecasting has clarified some aspects such as structure prediction, forecasting and providing substantiation of the logical flow of elaboration. Logical work flow forecasting must consider the diagnosis, prognosis and qualification of these studies. In other words, it requires an analysis and diagnosis, and analysis of program and finally, macroeconomic and socio-economic program. These issues the authors have proposed to give them the required significance and precise in order to clarify the context in which these predictions can be made.The authors emphasize and clarify the methods and forecasting techniques so that, according to the existence of a large number of such techniques, you can select the ones you are significant in achieving such a study. With regard to the prediction methods, attention is paid to the extrapolation method, using random variables, the method of interpolation, as well as structural prediction methods. Within each of these authors give precise meanings systems, offering an important guide for those who want such studies. In particular, economic-mathematical model used in economic forecast shows interest. Thus, attention is forecasting models based on production functions from the general pattern of Cobb-Douglas, continue with synthesizing aspects of forecasting simulation models, focusing on Monte Carlo simulation or simulation type game. The paper provides a number of elements on which can be reached from a study forecasting that meets all functions in the sense of being a prediction guaranteed to contain steps and elements necessary to be based on a system of indicators economic (macro), to include a work flow, control and possible adjustment thereof.

Suggested Citation

  • Constantin ANGHELACHE & Madalina-Gabriela ANGHEL & Tudor SAMSON & Radu STOICA, 2017. "Methods And Techniques For Preparing Forecasts," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 65(4), pages 26-36, April.
  • Handle: RePEc:rsr:supplm:v:65:y:2017:i:4:p:26-36

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    References listed on IDEAS

    1. Serena Ng & Jonathan H. Wright, 2013. "Facts and Challenges from the Great Recession for Forecasting and Macroeconomic Modeling," Journal of Economic Literature, American Economic Association, vol. 51(4), pages 1120-1154, December.
    2. Julieta Fuentes & Pilar Poncela & Julio Rodríguez, 2015. "Sparse Partial Least Squares in Time Series for Macroeconomic Forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 576-595, June.
    3. Olivier J. Blanchard & Daniel Leigh, 2013. "Growth Forecast Errors and Fiscal Multipliers," American Economic Review, American Economic Association, vol. 103(3), pages 117-120, May.
    4. S. Boragan Aruoba & Francis X. Diebold, 2010. "Real-Time Macroeconomic Monitoring: Real Activity, Inflation, and Interactions," American Economic Review, American Economic Association, vol. 100(2), pages 20-24, May.
    5. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    6. Catherine C. Eckel & Philip J. Grossman, 2008. "Forecasting Risk Attitudes: An Experimental Study Using Actual and Forecast Gamble Choices," Monash Economics Working Papers archive-01, Monash University, Department of Economics.
    7. Wilms, Ines & Croux, Christophe, 2016. "Forecasting using sparse cointegration," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1256-1267.
    8. Oller, Lars-Erik & Teterukovsky, Alex, 2007. "Quantifying the quality of macroeconomic variables," International Journal of Forecasting, Elsevier, vol. 23(2), pages 205-217.
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