# The Predictive Grey Forecasting Approach for Measuring Tax Collection
In recent years, the central and several state governments have implemented diverse policy changes and streamlined processes to enhance predictability, equity, and automation. Examining the tax-growth link in a budget-constrained country such as India allows us to devise appropriate policy measures for a more inclusive and fair growth process ([PERSON] and [PERSON], 2024). Lowering tax liabilities and increasing returns on investment in the context of corporate investments is a great impetus for countries like India ([PERSON] et al., 2024).Thus, it is crucial to forecast direct and indirect tax collection in the Indian scenario for revenue planning and budget allocation, economic stability and policy adjustments ([PERSON] and [PERSON], 2018), and financing of welfare and infrastructure initiatives, in addition to forecasting the impacts of policy reforms and global economic fluctuations to regulate inflation ([PERSON] and [PERSON], 2015), enhance investor confidence, and promote social justice. Therefore, understanding the anticipated tax collection for the above reasons allows for the realization of the benefits mentioned above and serves as a basis for more effective planning and decision making. To obtain optimal results, this study focused on forecasting Indian direct and indirect tax by employing the grey forecasting model(GM) (1, 1) ([PERSON] et al., 2023; [PERSON] et al., 2024; [PERSON] and [PERSON], 2024). Grey forecasting is based on a system comprising both partially known and unknown data, backed by the theory of grey systems, which can be used in various fields to solve problems ([PERSON] et al., 2023). Moreover, for examination of time series, the GM (1, 1) is beneficial, as it can be used with restricted data and short-term forecasts that yield precise forecasts; additionally, this approach ignores the statistical dissemination of data processing. It acts as a grey prediction used in this study to estimate the trend of the amount of collected electronic waste in emerging economies. Thus, the importance of this study lies in understanding the forecasting of Indian direct and indirect tax collection based on a well-established grey forecasting model. Forecasting tax collection from 2022-23 to 2029-30 not only addresses our research objective but also fills a gap in the literature. Although there are limited studies on tax forecasting in India, the existing literature includes a forecast of corporate tax ([PERSON] and [PERSON], 2023), GST forecasting ([PERSON] et al., 2023), factors deriving the effective tax rate (ETR) for Indian manufacturing firms in different sectors ([PERSON] and [PERSON], 2020), and effects of analysts' tax expense forecast accuracy on corporate tax avoidance ([PERSON], 2021). Thus, this is the first study to investigate the forecasting of direct and indirect tax collection in India by focusing on the following four research objectives (ROs):  
RO-1: To understand past direct tax collection trends and patterns in India;  
RO-2: To forecast direct tax (representing personal tax, income tax, and other tax) collection in India;  
RO-3: To forecast indirect tax collection in India;  
RO-4: To unveil the relationship between indirect and direct taxes by analyzing the proportion of direct tax as a percentage of the total tax. This study's findings reveal a consistent increase in direct tax collection and its major components, i.e., personal income tax and corporate tax. Total direct tax stood at INR 0.68 trillion in the financial year of 2000-01, which increased 24 times by 2022-23 to INR 16.63 trillion. The proportion of personal income tax decreased from 46.5% in the year 2000-01 to the lowest ever in 2015-16, at 38.8%. After 2015-16, personal tax had a complete turnaround, and the proportion increased to around 50% of the total direct tax. As far as the cost of tax collection is concerned, it reduced from 1.36% of the collection in 2000-01 to just 0.51% in 2022-23.