# The Predictive Grey Forecasting Approach for Measuring Tax Collection
## 2 Literature Review and Conceptual Framework  
This study conducted a systematic literature review process for previous studies by utilizing core keywords, i.e., \"Tax\", \"India\", and \"Forecasting\". We identified 28 documents (including15 articles, 8 conference papers, 3 book chapters, 1 erratum, and 1 review) from 1986 to 2024 in Scopus. We included the five most relevant articles (Appendix A) (as the remaining documents were excluded because they focused on electronic vehicle taxation, carbon taxation, tobacco taxation, etc.)to recognize core previous studies. Moreover, a concurrent study in the context of methodologies is covered in Appendix B.  
Referring to other previous studies, [PERSON] and [PERSON] (2023) forecasted CIT revenue using Vector Auto-Regression (VAR) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA) models. Their findings revealed that the VAR model outperformed SARIMA in terms of accuracy, with their inequality index for CIT revenue growth being 0.17 for in-sample forecasts and 0.25 for out-of-sample projections. These findings highlight the value of utilizing multivariate models to achieve greater precision in tax revenue forecasting.  
[PERSON] et al. (2023) used sophisticated machine learning models, including the Hybrid Theta-TBATS and neural networks, including Artificial Neural Networks (ANN) and Neural Networks for Auto-Regression (NNAR)in the field of GST revenue forecasting. Their study demonstrated the Hybrid Theta-TBATS model's superiority in producing precise GST revenue estimates, highlighting the function of hybrid approaches in handling trend dynamics and seasonality in tax data.  
Establishing a framework for fiscal policy, particularly taxation policy, influences growth performance ([PERSON] et al., 2021). The level and structure of taxation influence house hold saving behavior and investment in human capital ([PERSON] and [PERSON], 2021). Conversely, a firm modifies its investment strategies and innovations in response to tax policies ([PERSON] et al., 2021).Tax policies are influenced by institutional factors such as economic conditions, political frameworks, and social contexts ([PERSON] et al., 2024). Policymakers must consider tax rates and broader institutional factors when designing tax systems ([PERSON] et al., 2024). These decisions and incentives in building physical and human capital generate differences in growth among countries and state economies ([PERSON], 2020). Another study by [PERSON] et al. (2023) predicted tax collection in China using GM (1,1) and the LSSVM model. Accurate tax revenue forecasting is crucial for governments to execute fiscal programs effectively. Another study employed an agent-based model with a heterogeneous population and incorporated genetic algorithm-based decision making to simulate an economy and analyze taxation policy dynamics ([PERSON] et al., 2024).There is a lack of studies addressing the issue in the context of India. This study aims to address this gap by analyzing India's direct and indirect tax collections by employing a reliable forecasting method, i.e., the grey forecasting method.