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

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  • Parma Chakravartti
  • Sudipto Mundle

Abstract

Building on the early work of Mitchell and Burns (1938,1946), the automatic leading indicator (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 dynamic 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%.

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  • Parma Chakravartti & Sudipto Mundle, 2017. "An Automatic Leading Indicator Based Growth Forecast For 2016-17 and The Outlook Beyond," Working Papers id:11773, eSocialSciences.
    • Handle: RePEc:ess:wpaper:id:11773
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    8. Daniel Grenouilleau, 2006. "The Stacked Leading Indicators Dynamic Factor Model: A Sensitivity Analysis of Forecast Accuracy using Bootstrapping," European Economy - Economic Papers 2008 - 2015 249, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
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    Cited by:

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    3. 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; Mature; Economies; Macroeconomic; Autoregression; Structural; Techniques.;
    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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