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Tracking India Growth in Real Time

Author

Listed:
  • Bhattacharya, Rudrani

    () (National Institute of Public Finance and Policy)

  • Pandey, Radhika

    () (National Institute of Public Finance and Policy)

  • Veronese, Giovanni

    (Bank of Italy)

Abstract

Tracking growth in the Indian economy would be best performed using a measure like GDP. Unfortunately official estimates of this indicator are released with quarterly frequency and with considerable delay. This paper compares different approaches to the short term forecasting (nowcasting) of real GDP growth in India and evaluates methods to optimally gauge the current state of the economy. Univariate quarterly models are compared with bridge models that exploit the available monthly indicators containing information on current quarter developments. In the forecasting exercise we perform a pseudo real-time simulation: by properly taking into account the actual publication lags of the series, we replicate the information set available to the policymaker at each point of time. We find that bridge models perform satisfactorily in predicting current quarter GDP growth. This result follows from the actual estimation technique used to construct the official quarterly national accounts, still largely dependent on a narrow information set. Our analysis also suggests mixed evidences about the additional predictive power of Indian survey data with respect to the hard data already used in the national accounts.

Suggested Citation

  • Bhattacharya, Rudrani & Pandey, Radhika & Veronese, Giovanni, 2011. "Tracking India Growth in Real Time," Working Papers 11/90, National Institute of Public Finance and Policy.
  • Handle: RePEc:npf:wpaper:11/90
    Note: Working Paper 90, 2011
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    References listed on IDEAS

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    1. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    2. G. Rünstler & K. Barhoumi & S. Benk & R. Cristadoro & A. Den Reijer & A. Jakaitiene & P. Jelonek & A. Rua & K. Ruth & C. Van Nieuwenhuyze, 2009. "Short-term forecasting of GDP using large datasets: a pseudo real-time forecast evaluation exercise," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 595-611.
    3. Cecilia Frale & Massimiliano Marcellino & Gian Luigi Mazzi & Tommaso Proietti, 2010. "Survey data as coincident or leading indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 109-131.
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    5. Michael Pedersen, 2013. "Extracting GDP signals from the monthly indicator of economic activity: Evidence from Chilean real-time data," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2013(1), pages 1-16.
    6. Troy Matheson & James Mitchell & Brian Silverstone, 2007. "Nowcasting and predicting data revisions in real time using qualitative panel survey data," Reserve Bank of New Zealand Discussion Paper Series DP2007/02, Reserve Bank of New Zealand.
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    Cited by:

    1. Deicy J. Cristiano & Manuel D. Hernández & José David Pulido, 2012. "Pronósticos de corto plazo en tiempo real para la actividad económica colombiana," Borradores de Economia 724, Banco de la Republica de Colombia.
    2. repec:eee:intfor:v:33:y:2017:i:4:p:915-935 is not listed on IDEAS
    3. Dahlhaus, Tatjana & Guénette, Justin-Damien & Vasishtha, Garima, 2017. "Nowcasting BRIC+M in real time," International Journal of Forecasting, Elsevier, vol. 33(4), pages 915-935.
    4. Daniela Bragoli & Jack Fosten, 2016. "Nowcasting Indian GDP," University of East Anglia School of Economics Working Paper Series 2016-06, School of Economics, University of East Anglia, Norwich, UK..

    More about this item

    Keywords

    Nowcasting ; Bridge model ; Factor model ; Emerging markets ; India;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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