The New Zealand Treasury forecasts tax revenue for the twice-yearly Economic and Fiscal Updates. The accuracy of these forecasts is important for the government's annual budget decisions as they affect key fiscal aggregates such as the operating balance and debt levels. Good decision-making in this area is important for macroeconomic stability and sustainability, one of the Treasury's outcomes. Over the past six years, Treasury tax forecasts, and the macroeconomic forecasts on which they are based, have underestimated the actual outturns. This report presents an analysis of the Treasury's tax revenue forecast errors, both in aggregate and disaggregated by individual tax type. The analysis focuses primarily on the annual one-year-ahead Budget forecasts that are typically based on rating up past tax revenues by growth rates in related macroeconomic variables such as GDP. The objective of the analysis is to better determine the major sources of tax revenue forecast error and to identify the potential for methodological improvements. A review of the Treasury's tax forecasting methods is given and a general class of models proposed that encompasses these methods. Adopting one of the simplest of these as a benchmark, the individual tax revenue forecast errors are first disaggregated into component errors due to forecasting the macroeconomic drivers used as a proxy for the tax base, and a component due to forecasting the tax ratio, or ratio of tax revenue to proxy tax base. The tax ratio is further disaggregated into a component error due to forecasting the tax ratio trend and random error. The latter provides a measure of the best accuracy that can be achieved using the benchmark models adopted. Among other findings, the report shows that the main source of tax revenue underforecasting is the underforecasting of the macroeconomic variables used as taxbase proxies. The tax ratio forecasts were generally unbiased, but less precisely determined than the macroeconomic forecasts. This and other evidence indicate that better tax ratio forecasts are likely to be achieved, even with the simple benchmark model used here. The benchmark models have merit as competing models that could be investigated further alongside other simple structural time series models in a systematic evaluation using historical data.
