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Investigating uncertainty in macroeconomic forecasts by stochastic simulation

Author

Listed:
  • Debby Lanser

    () (CPB Netherlands Bureau for Economic Policy Analysis)

  • Henk Kranendonk

    () (CPB Netherlands Bureau for Economic Policy Analysis)

Abstract

We investigate four sources of uncertainty with CPB’s macroeconomic model SAFFIER: provisional data, exogenous variables, model parameters and residuals of behavioural equations. Uncertainty is an inherent attribute of any forecast. We apply a Monte Carlo simulation technique to calculate standard errors for the short-term and medium-term horizon for GDP and eight other macroeconomic variables. The results demonstrate that the main contribution to the total variance of a medium-term forecast emanates from the uncertainty in the exogenous variables. For the short-term forecast both exogenous variables and provisional data are most relevant.

Suggested Citation

  • Debby Lanser & Henk Kranendonk, 2008. "Investigating uncertainty in macroeconomic forecasts by stochastic simulation," CPB Discussion Paper 112, CPB Netherlands Bureau for Economic Policy Analysis.
  • Handle: RePEc:cpb:discus:112
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    File URL: http://www.cpb.nl/sites/default/files/publicaties/download/investigating-uncertainty-macroeconomic-forecasts-stochastic-simulation.pdf
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    References listed on IDEAS

    as
    1. Alexei Onatski & Noah Williams, 2003. "Modeling Model Uncertainty," Journal of the European Economic Association, MIT Press, vol. 1(5), pages 1087-1122, September.
    2. Garrat, A. & Lee, K. & Pesaran, M.H. & Shin, Y., 2000. "Forecast Uncertainties in Macroeconometric Modelling: An Application to the UK Economy," Cambridge Working Papers in Economics 0004, Faculty of Economics, University of Cambridge.
    3. Fair Ray C, 2003. "Bootstrapping Macroeconometric Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 7(4), pages 1-26, December.
    4. Nigel Pain & Ray Barrell & Dawn Holland, 2001. "Openness, integration and transition: prospects and policies for economies in transition," National Institute of Economic and Social Research (NIESR) Discussion Papers 177, National Institute of Economic and Social Research.
    5. Canova, Fabio, 1995. "Sensitivity Analysis and Model Evaluation in Simulated Dynamic General Equilibrium Economies," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 36(2), pages 477-501, May.
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    Cited by:

    1. BRATU SIMIONESCU, Mihaela, 2012. "Two Quantitative Forecasting Methods For Macroeconomic Indicators In Czech Republic," Annals of Spiru Haret University, Economic Series, Universitatea Spiru Haret, vol. 3(1), pages 71-87.
    2. Mihaela SIMIONESCU, 2013. "The Assessment Of Parameter Uncertainty In A Vector Error Correction Model For Romania," Romanian Journal of Economics, Institute of National Economy, vol. 37(2(46)), pages 124-134, December.
    3. Henk Kranendonk & Johan Verbruggen, 2009. "Reaction to Philip Hans Franses' Note 'Why is GDP typically revised upwards?'," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 63(2), pages 133-134.

    More about this item

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E20 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - General (includes Measurement and Data)
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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