Forecasting India’s GDP Growth Using Sectoral Data: A Comparative Study of Statistical Models and Machine Learning Approaches

India is the world’s seventh-largest economy by nominal GDP and
the third largest by purchasing power parity (PPP), this has been
possible due to the important transformations in the economic
development. This paper showcases a complete analysis of India’s
GDP by focusing on its two critical periods: the pre-liberalization
era (1947-1991) and the post-liberalization era (1991-2008). Each of
which is signified with its own distinct economic methods.
Over time, India’s economy has observed a prominent shift. In the pre
liberalization period, a large share of GDP is because of agriculture.
However, in the post-liberalization period, it decreased significantly,
whereas the services sector, especially the IT and finance expanded.
This played a key role in the rapid economic growth, making India a
global leader in the IT sector.
Several machine learning and statistical models such as Ordinary
Least Squares (OLS) regression for analyzing linear relationships,
Multilayer Perceptrons (MLP) and Autoregressive Integrated Moving
Average (ARIMA) models are used to analyse India’s GDP growth.
Gradient Boosting, Elastic Net, and Random Forest techniques are
also utilized to enhance the accuracy of predictions and to better
understand the dynamics of India’s GDP.