Non-Parametric Stochastic Simulations to Investigate Uncertainty around the OECD Indicator Model Forecasts

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.