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Stochastic simulations on the Romanian macroeconomic model

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  • Dobrescu, Emilian
  • Pauna, Bianca

Abstract

The paper presents the methodology for attaching probability distribution or intervals of variation to point forecasts. This methodology might prove significant for countries that have suffered deep structural transformations in their not very distant past. For these models, stability is more difficult to be achieved, because some coefficients lack accuracy of estimation, and this is not visible until intervals of variation are constructed. Forecasts consist traditionally in sets of values of key economic indicators, with no information regarding the associated uncertainty. Our assessment is that policy makers would benefit if they would be given probabilities as well as values, and the methodology of stochastic simulation, presented in this paper quantifies the uncertainty of the coefficients of the behavioural equations, on a reduced version of the Romanian Market Economy Model. In our paper we present the advantages of applying stochastic simulation on macromodels of emerging market economies, both from a cognitive and practical perspective. On one hand, researchers have an instrument to check the operational properties of a given model, and subsequently improve them, and, on the other hand, policy makers by incorporating the uncertainty into the decisional mechanism, have additional information which would help them in efficiently defining and promoting their targets.

Suggested Citation

  • Dobrescu, Emilian & Pauna, Bianca, 2007. "Stochastic simulations on the Romanian macroeconomic model," MPRA Paper 35723, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:35723
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    References listed on IDEAS

    as
    1. Dobrescu, Emilian, 2005. "Macromodel Estimations For The Updated 2004 Version Of The Romanian Pre-Accession Economic Programme - Working Paper," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 2(1), pages 5-29.
    2. Dobrescu, Emilian, 1996. "Macromodels of the Romanian transition Economy," MPRA Paper 35810, University Library of Munich, Germany.
    3. Boehm, Ernst A., 1993. "Business cycles: Theory, history, indicators, and forecasting : Victor Zarnowitz, 1992, (University of Chicago Press, Chicago), xvii + 593 pp., $70.00, ISBN 0-226-97890-7," International Journal of Forecasting, Elsevier, vol. 9(2), pages 275-277, August.
    4. Gajda, Jan B. & Markowski, Aleksander, 1998. "Model Evaluation Using Stochastic Simulations: The Case of the Econometric Model KOSMOS," Working Papers 61, National Institute of Economic Research.
    5. Fair, Ray C, 1980. "Estimating the Expected Predictive Accuracy of Econometric Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 21(2), pages 355-378, June.
    6. O'Brien, Anthony Patrick, 1993. "Business Cycles: Theory, History, Indicators, and Forecasting. By Victor Zarnowitz. Chicago: University of Chicago Press, 1992. Pp. xvii, 593. $70.00," The Journal of Economic History, Cambridge University Press, vol. 53(1), pages 211-213, March.
    7. James H. Stock & Mark W. Watson, 1993. "Business Cycles, Indicators, and Forecasting," NBER Books, National Bureau of Economic Research, Inc, number stoc93-1, March.
    8. Stock, James H. & Watson, Mark W. (ed.), 1993. "Business Cycles, Indicators, and Forecasting," National Bureau of Economic Research Books, University of Chicago Press, edition 1, number 9780226774886, December.
    9. Franz, Wolfgang & Göggelmann, Klaus & Schellhorn, Martin & Winker, Peter, 1998. "Quasi-Monte Carlo Methods in Stochastic Simulations: An Application to Fiscal Policy Simulations using an Aggregate Disequilibrium Model of the West German Economy," ZEW Discussion Papers 98-03, ZEW - Leibniz Centre for European Economic Research.
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    Cited by:

    1. Dobrescu, Emilian, 2010. "Macromodel Simulations for the Romanian Economy," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 7-28, July.

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    More about this item

    Keywords

    macromodel; uncertainty; bootstrap; simulation;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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