The study investigates the predictive efficacy of various machine learning methodologies, encompassing Random Forest (RF) regression, Gradient Boosting (GB), Xtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Least Absolute Shrinkage and Selection Operator (LASSO) regression, and a deep learning technique, specifically Long Short-Term Memory (LSTM). The benchmark method employed is the autoregressive (AR) model of order 1. With a focus on forecasting money demand for the Indian economy, a crucial component for achieving the Cent­ral Bank of India's inflation targeting objective, a comprehensive monthly dataset from 1997 to 2021 is utilized.

The obtained results underline the robust predictive capabilities of the employed models concerning both narrow and broad money demand forecasts. By em­ploying a range of evaluation metrics, the study rigorously compares the predictive performance of these models. Using the expanding window cross validation with time series split, the models are cross-validated to ensure accurate forecasts of monetary aggregates. Moreover, the Diebold – Mariano test is utilized to evaluate and compare the quality of forecasts.