Post-COVID recovery and long-run forecasting of Indian GDP with factor-augmented error correction model

Maiti, Dibyendu, Kumar, Naveen, Jha, Debajit, Sarkar, Soumyadipta and Re, Gallagher (2022) Post-COVID recovery and long-run forecasting of Indian GDP with factor-augmented error correction model. [Working papers (or Preprints)] (Unpublished)

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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 a macro variable. Availability of big data enables the extraction of some common factors from large dimensions, which essentially produces better precision of forecasting estimates. The method first extracts leading factors and then add proxy policy variables to establish their long-run relationship with the GDP and produces insignificant in-sample bias. The relationship has been employed to predict GDP for 2022-35. We found three major dynamic factors that capture80% of variations of 56 quarterly variables of the Indian economy. These three factors with four lags and four exogenous policy instruments have been included in the FECM model for forecasting estimation. We find that the economy is expected to grow at 4-8% annually, depending upon the actual realisation of external shocks and policies. The expected rise of temperature and oil price seems to be dampening the growth. But, the institutional reforms making effective public investment and the introduction of digital currency that reduces cash requirements could play an expansionary role. If the oil price and the temperature remains at the current level, the growth rate can go closer to 8%.

Item Type: Working papers (or Preprints)
Keywords: Forecasting | Dynamic factor model | FECM | ARIMA
Subjects: Social Sciences and humanities > Economics, Econometrics and Finance > Economics
Social Sciences and humanities > Social Sciences > Health (Social sciences)
JGU School/Centre: Jindal School of Government and Public Policy
Depositing User: Mr. Syed Anas
Date Deposited: 06 Apr 2022 07:33
Last Modified: 06 Apr 2022 07:33
Official URL:
Additional Information: This paper is an outcome of the report commissioned by EGROW Foundation in association with Niti Aayog and has been presented in an workshop held on 8-9 March 2022 and in the National Conference organised by Bengal Economic Association on 12-13th March 2022. The foundation helped in procuring the relevant data for the analysis. We are indebted to Anindya Banerjee for useful comments. Authors do not have any conflict of interests for this research.


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