India's GDP Growth from 1971-71 to 2019-20: A Semi-Logarithmic Polynomial Analysis

This study analyses the long-term growth pattern of India’s economy by examining its logarithmic GDP (LN-GDP) from 1970–71 to 2019–20. The analysis begins with a linear trend regression, which reveals a consistent annual growth rate of 5.52%, explaining 99.04% of the variation in LN-GDP, indicating steady economic growth over the period. The quadratic model improves upon this with an R² of 0.9991 with higher adjusted R square, showing that the economy’s growth has accelerated over time. Moving to the cubic regression, which further refines the fit with an R² of 0.9994, we observe a slight deceleration in growth, as indicated by the negative cubic term. Higher-order polynomial regressions (degree four to six) continue to improve the model fit, but the significance F values and the R² indicate diminishing returns with the addition of more terms. While the fifth-degree model yields the highest adjusted R², the cubic model remains the best fit based on its low significance F value. The higher-order polynomial functions confirm that India’s GDP growth is nearly cubic in nature.

Publication Details

Published
2025-01-01
DOI
https://doi.org/10.52783/eel.v15i2.3100
Primary Topic
Agricultural Economics and Practices
Type
article
Field-Weighted Citation Impact
0.00

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India's GDP Growth from 1971-71 to 2019-20: A Semi-Logarithmic Polynomial Analysis

Agricultural Economics and Practices
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India's GDP Growth from 1971-71 to 2019-20: A Semi-Logarithmic Polynomial Analysis

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Abstract

This study analyses the long-term growth pattern of India’s economy by examining its logarithmic GDP (LN-GDP) from 1970–71 to 2019–20. The analysis begins with a linear trend regression, which reveals a consistent annual growth rate of 5.52%, explaining 99.04% of the variation in LN-GDP, indicating steady economic growth over the period. The quadratic model improves upon this with an R² of 0.9991 with higher adjusted R square, showing that the economy’s growth has accelerated over time. Moving to the cubic regression, which further refines the fit with an R² of 0.9994, we observe a slight deceleration in growth, as indicated by the negative cubic term. Higher-order polynomial regressions (degree four to six) continue to improve the model fit, but the significance F values and the R² indicate diminishing returns with the addition of more terms. While the fifth-degree model yields the highest adjusted R², the cubic model remains the best fit based on its low significance F value. The higher-order polynomial functions confirm that India’s GDP growth is nearly cubic in nature.

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