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An Automatic Leading Indicator Based Growth Forecast For 2016-17 and The Outlook Beyond

Author

Listed:
  • Chakravartti, Parma

    (National Institute of Public Finance and Policy)

  • Mundle, Sudipto

    (National Institute of Public Finance and Policy)

Abstract

Building on the early work of Mitchell and Burns (1938,1946), the automatic leading indica-tor (ALI) approach has been developed over the last few decades by Geweke (1977), Sargent and Sims (1977), Stock and Watson (1988), Camba-Mendez et al. (1999) , Mongardini and Sedik (2003), Duo-Qin et al. (2006), Grenouilleau (2006) and others. It has come to be widely accepted as one of the most effective methods for macroeconomic forecasting. This paper uses the ALI approach to forecast aggregate and sectoral GDP growth for 2016-17. The approach uses a dy-namic factor model (DFM) in the form of state space representation to extract factors from a pool of variables and then the factors are incorporated into a VAR model to generate the forecast series. Three alternate models have been tried: demand side, supply side and combined model. The model with the lowest RMSE is selected for the forecast. Real GDP growth is forecast at 6.7% for 2016-17 without factoring in the impact of demonetisation. Incorporating that impact reduces the forecast to 6.1%.Length: 30

Suggested Citation

  • Chakravartti, Parma & Mundle, Sudipto, 2017. "An Automatic Leading Indicator Based Growth Forecast For 2016-17 and The Outlook Beyond," Working Papers 17/193, National Institute of Public Finance and Policy.
  • Handle: RePEc:npf:wpaper:17/193
    Note: Working Paper 193, 2017
    as

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    References listed on IDEAS

    as
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    2. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    3. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1, September.
    4. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, Oxford University Press, vol. 120(1), pages 387-422.
    5. Geweke, John F & Singleton, Kenneth J, 1981. "Maximum Likelihood "Confirmatory" Factor Analysis of Economic Time Series," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 22(1), pages 37-54, February.
    6. Sudipto Mundle, 2017. "Employment, Education and the State," The Indian Journal of Labour Economics, Springer;The Indian Society of Labour Economics (ISLE), vol. 60(1), pages 17-31, March.
    7. D.K. Srivastava & K.R. Shanmugam, 2012. "Stationarity Test for Aggregate Outputs in the Presence of Structural Breaks," Working Papers 2012-072, Madras School of Economics,Chennai,India.
    8. Auerbach, Alan J, 1982. "The Index of Leading Indicators: "Measurement without Theory," Thirty-Five Years Later," The Review of Economics and Statistics, MIT Press, vol. 64(4), pages 589-595, November.
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    Cited by:

    1. Bhattacharya, Rudrani & Chakravarti, Parma & Mundle, Sudipto, 2018. "Forecasting India's Economic Growth: A Time-Varying Parameter Regression Approach," Working Papers 18/238, National Institute of Public Finance and Policy.
    2. Regy, Prasanth V. & Roy, Shubho, 2017. "Understanding Judicial Delays in Debt Tribunals," Working Papers 17/195, National Institute of Public Finance and Policy.

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    More about this item

    Keywords

    Growth Rate ; Forecasting ; Automatic Leading Indicator ; Dynamic Factor Model ; Agriculture ; Industry ; Services ; GDP ; Demonetization;
    All these keywords.

    JEL classification:

    • 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
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • O4 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity

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