This paper documents our attempt to apply machine learning techniques to predict payroll tax and land transfer duty revenue in Victoria. We compare the effectiveness of these techniques against simpler econometric models by utilising different loss functions, forecast horizons, and sample periods.
Our findings suggest that machine learning methods do not outperform simpler models for payroll tax forecasting but might be useful for land transfer duty forecasting.
This conclusion aligns with a recent study by Chung, Williams and Do (2022), which shows no significant improvement when using machine learning to forecast government revenue in the United States, except for the prediction of land transfer duty revenue.
Overall, our findings indicate that simple methods can be equally effective as advanced approaches when forecasting tax lines that are more stable, whereas sophisticated methods may offer added benefits when dealing with a more volatile tax line.
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