Vector Autoregressive Models for Tax Forecasting

This paper explores the use of vector autoregressive (VAR) models to supplement the New Zealand Treasury’s tax forecasting models. The models are used to forecast both tax revenue and tax receipts. A suite of VAR models is developed for 20 different tax types with a focus on assessing the forecasting performance of six model specifications for each tax category. This paper shows that VAR models exhibit strong predictive performance for tax types with stable trends, such as total tax and source deductions. By contrast, models for corporate tax and other persons tax exhibit higher volatility and larger discrepancies. Several challenges were identified with these models. One challenge is that it is difficult to accommodate changes in tax rates through the sample period. A second challenge is that large shocks, such as the COVID-19 pandemic, introduce significant volatility and affect the accuracy of forecasts, particularly for tax receipts. Some model specifications also exhibit biases in their predictions for certain tax types. Comparing the forecasts to the official data release for 2024Q3, the VAR models for 13 out of 20 tax types produced forecasts within the range of the official tax release, while 7 tax types had discrepancies between $0.7 billion and $3.2 billion, with the largest discrepancies arising in tax receipts forecasts for total, indirect, and GST taxes.