Post-COVID Recovery and Long-Run Forecasting of Indian GDP with Factor-Augmented Error Correction Model (FECM)

Dibyendu Maiti (), Naveen Kumar, Debajit Jha and Soumyadipta Sarkar
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Naveen Kumar: University of Delhi
Debajit Jha: O.P. Jindal Global University
Soumyadipta Sarkar: Munich Re

Computational Economics, 2024, vol. 63, issue 3, No 7, 1095-1120

Abstract: Abstract This paper attempts to estimate long-run forecasting of Indian GDP for the post-COVID period using the factor error correction model (FECM). The model builds on a dynamic factor model that directly and indirectly captures many dimensions affecting the cycles of GDP. The availability of big data enables the extraction of a few common factors from large dimensions, which essentially produces better precision in forecasting estimates. We first extract leading factors and then add proxy policy variables to establish their long-run relationship with the GDP which produces insignificant in-sample bias. The estimated long-run relationship has been employed to predict GDP for 2022–35. We found three major dynamic factors that capture 80% of variations from 56 quarterly variables of the Indian economy. These three factors with their four lags and four exogenous policy instruments have been included in the FECM model for forecasting estimation. We find that the real GDP is expected to grow at 4–8% annually, depending upon the actual realisation of external shocks and policies. The expected rise in temperature and oil prices seems to be dampening the growth. Whereas, the institutional reforms for making more effective public investment and the currency digitalisation that reduces cash requirements could play an expansionary role. If the oil price and the temperature remain at the current level, the growth rate can go closer to approximately 8%.

Keywords: Forecasting; Dynamic factor model; FECM; ARIMA; India (search for similar items in EconPapers)
JEL-codes: C53 O40 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10614-023-10414-2

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