Measuring the uncertainty in predicting public revenue

This paper provides an assessment of the uncertainty surrounding revenue predictions, through an ex post analysis of European Commission's forecasts over the last 15 years. It estimates the forecast errors affecting revenue for all 28 Member States, using the different vintages of the autumn and spring Commission forecasts. It analyses both the direction and magnitude of errors, using standard summary statistics. The paper looks into the various components of forecast errors to better understand their drivers (forecasting error related to real GDP, inflation or revenue-to-GDP ratio) and which types of revenues (direct tax, indirect tax or social security contributions) are particularly affected. The paper also examines the pattern of revenue errors over time and in particular how revenue forecasts perform before, during and after the crisis. To further deepen the analysis, a set of tests are carried out on the quality of the prediction (serial correlation, unbiasedness, weak and informational efficiency). The estimator-based tests confirm the sound track record of the European Commission's forecasts. This is also shown by a comparison with the OECD's revenue forecasts. Lastly, the paper reviews various possible determinants of forecast errors and examines their significance by means of a pooled time series technique. The econometric study allows for the identification of factors which increase or reduce the risk of over-forecasting revenue.