Nowcasting GDP Growth Using a Coincident Economic Indicator for India

Bhadury, Soumya and Ghosh, Saurabh and Kumar, Pankaj (2019): Nowcasting GDP Growth Using a Coincident Economic Indicator for India.

Abstract

In India, the first official estimate of quarterly GDP is released approximately 7-8 weeks after the end of the reference quarter. To provide an early estimate of the current quarter GDP growth, we construct a Coincident Economic Indicator for India (CEII) using 6, 9 and 12 high-frequency indicators. These indicators represent various sectors, display high contemporaneous correlation with GDP, and track GDP turning points well. While CEII-6 includes domestic economic activity indicators, CEII-9 combines indicators on trade and services along with the indicators used in CEII-6. Finally, CEII-12 adds financial indicators to the indicators used in CEII-9. In addition to the conventional economic activity indicators, we include a financial block in CEII-12 to reflect the growing influence of the financial sector on economic activity. CEII is estimated using a dynamic factor model to extract a common trend underlying the highfrequency indicators. We use the underlying trend to gauge the state of the economy and to identify sectors contributing to economic fluctuations. Further, CEIIs are used to nowcast GDP growth, which closely tracks the actual GDP growth, both in-sample and out-of-sample.

• Item Type: MPRA Paper
• Language: English
• Keywords: Nowcast, Gross Domestic Product, Economic Cycle, Dynamic Factor Model, Turning Point Analysis, Jagged Edge Data
• Subjects: C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods

References

Bańbura, M., & Modugno, M. (2014). “Maximum likelihood estimation of factor models on datasets with arbitrary pattern of missing data”. Journal of Applied Econometrics, 29(1), 133-160.

Bańbura, M., Giannone, D. and L. Reichlin (2010). “Large Bayesian vector auto regressions”. Journal of Applied Econometrics, 25(1), 71-92.

Banbura, Marta and Giannone, Domenico and Reichlin, Lucrezia, (2010) “Nowcasting”. European Central Bank Working Paper No. 1275: https://ssrn.com/abstract=1717887

Bhadury S & S Pohit & R. Beyer, (2018). "A New Approach to Nowcasting Indian Gross Value Added," NCAER Working Papers 115, National Council of Applied Economic Research.

Bhattacharya, R., Pandey, R., & Veronese, G. (2011). “Tracking India growth in real time” National Institute of Public Finance and Policy. (pp. 2011-90).

Bok, Brandyn, et al. (2018) "Macroeconomic nowcasting and forecasting with big data." Annual Review of Economics 10: 615-643.

Bragoli, D., & Fosten, J. (2018). “Nowcasting Indian GDP”. Oxford Bulletin of Economics and Statistics, 80(2), 259-282.

Burns, A. F., & Mitchell, W. C. (1946). “The basic measures of cyclical behavior in Measuring Business Cycles”. National Bureau of Economic Research. (pp. 115-202).

Caruso, Alberto. (2015) "Nowcasting Mexican GDP." European Center for Advanced Research in Economics and Statistics working paper No. 40

Kyosuke Chikamatsu, Naohisa Hirakata, Yosuke Kido, Kazuki Otaka, 2018. "Nowcasting Japanese GDPs," Bank of Japan, Bank of Japan Working Paper Series

Dahlhaus, T., Guénette, J. D., & Vasishtha, G. (2017). “Nowcasting BRIC+ M in real time”. International Journal of Forecasting, 33(4), 915-935.

Dua, P., & Banerji, A. (2000). “An indicator approach to business and growth rate cycles: The case of India”. Indian Economic Review, 55-78.

Dua, P., & Sharma, V. (2016). “A comparison of economic indicator analysis and Markov switching methods concerning the cycle phase dynamics”. OECD Journal: Journal of Business Cycle Measurement and Analysis, 2015(2), 1-27.

Ghysels, Eric, Santa-Clara, Pedro and Valkanov, Rossen, (2004), The MIDAS Touch: Mixed Data Sampling Regression Models, CIRANO Working Papers, CIRANO, https://EconPapers.repec.org/RePEc:cir:cirwor:2004s-20.

Giannone, D., Reichlin, L., & Small, D. (2008). “Nowcasting: The real-time informational content of macroeconomic data”. Journal of Monetary Economics, 55(4), 665-676.

Kumar, Gitanjali (2013) : High-frequency real economic activity indicator for Canada, Bank of Canada Working Paper, No. 2013-42, Bank of Canada, Ottawa

Luciani, M., Pundit, M., Ramayandi, A., & Veronese, G. (2018). “Nowcasting Indonesia”. Empirical Economics, 55(2), 597-619.

RBI (2002). “Report of the Technical Advisory Group on Development of Leading Economic Indicators for Indian Economy”.

RBI(2007). “Report of the Technical Advisory Group on Development of Leading Economic Indicators for Indian Economy.”

Stock, J. H., & Watson, M. W. (1989). “New indexes of coincident and leading economic indicators”. NBER Macroeconomics Annual, 4, 351-394.