OECD Economics Department Working Papers

ISSN :
1815-1973 (online)
DOI :
10.1787/18151973
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Working papers from the Economics Department of the OECD that cover the full range of the Department’s work including the economic situation, policy analysis and projections; fiscal policy, public expenditure and taxation; and structural issues including ageing, growth and productivity, migration, environment, human capital, housing, trade and investment, labour markets, regulatory reform, competition, health, and other issues.

The views expressed in these papers are those of the author(s) and do not necessarily reflect those of the OECD or of the governments of its member countries.

 

Non-Parametric Stochastic Simulations to Investigate Uncertainty around the OECD Indicator Model Forecasts You or your institution have access to this content

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Author(s):
Elena Rusticelli1
Author Affiliations
  • 1: OECD, France

Publication Date
27 July 2012
Bibliographic information
No.:
979
Pages
33
DOI
10.1787/5k94kq50b2jd-en

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The forecasting uncertainty around point macroeconomic forecasts is usually measured by the historical performance of the forecasting model, using measures such as root mean squared forecasting errors (RMSE). This measure, however, has the major drawback that it is constant over time and hence does not convey any information on the specific source of uncertainty nor the magnitude and balance of risks in the immediate conjuncture. Moreover, specific parametric assumptions on the probability distribution of forecasting errors are needed in order to draw confidence bands around point forecasts. This paper proposes an alternative time-varying simulated RMSE, obtained by means of non-parametric stochastic simulations, which combines the uncertainty around the model’s parameters and the structural errors term to construct asymmetric confidence bands around point forecasts. The procedure is applied, by way of example, to the short-term real GDP growth forecasts generated by the OECD Indicator Model for Germany. The empirical probability distributions of the GDP growth forecasts, derived through the bootstrapping technique, allow the ex ante probability of, for example, a negative GDP growth forecast for the current quarter to be estimated. The results suggest the presence of peaks of higher uncertainty related to economic recession events, with a balance of risks which became negative in the immediate aftermath of the global financial crisis.
Keywords:
Forecasting uncertainty, stochastic simulations, empirical probability distribution, GDP
JEL Classification:
  • C12: Mathematical and Quantitative Methods / Econometric and Statistical Methods and Methodology: General / Hypothesis Testing: General
  • 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 Methods; Simulation Methods