IDEAS home Printed from https://ideas.repec.org/p/nca/ncaerw/115.html
   My bibliography  Save this paper

A new approach to nowcast Indian Gross Value Added

Author

Listed:
  • Soumya Bhadury
  • Sanjib Pohit

    (National Council of Applied Economic Research)

  • Robert C. M. Beyer

    (The World Bank)

Abstract

In India, quarterly growth of Gross Value Added (GVA) is published with a large lag and nowcasts are exacerbated by data challenges typically faced by emerging market economies, such as big data revisions, mixed frequencies data publication, small sample size, non-synchronous nature of data releases, and data releases with varying lags. In this paper, we present a new framework to nowcast India’s GVA that incorporates information of mixed data frequencies and other data characteristics. In addition, we add evening-hour luminosity as a crucial high-frequency indicator. Changes in nightlight intensity contain information about economic activity, especially in countries with a large informal sector and significant data challenges, including in India. We illustrate our framework for the ‘trade, hotels, transport, communication and services related to broadcasting’ bloc of the Indian GVA.

Suggested Citation

  • Soumya Bhadury & Sanjib Pohit & Robert C. M. Beyer, 2018. "A new approach to nowcast Indian Gross Value Added," NCAER Working Papers 115, National Council of Applied Economic Research.
    • Handle: RePEc:nca:ncaerw:115
    as

    Download full text from publisher

    File URL: https://www.ncaer.org/publication/a-new-approach-to-nowcasting-indian-gross-value-added
    File Function: First version, 2018
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bhadury, Soumya & Ghosh, Saurabh & Kumar, Pankaj, 2019. "Nowcasting GDP Growth Using a Coincident Economic Indicator for India," MPRA Paper 96007, University Library of Munich, Germany.
    2. Pradeep Mishra & Khder Alakkari & Mostafa Abotaleb & Pankaj Kumar Singh & Shilpi Singh & Monika Ray & Soumitra Sankar Das & Umme Habibah Rahman & Ali J. Othman & Nazirya Alexandrovna Ibragimova & Gulf, 2021. "Nowcasting India Economic Growth Using a Mixed-Data Sampling (MIDAS) Model (Empirical Study with Economic Policy Uncertainty–Consumer Prices Index)," Data, MDPI, vol. 6(11), pages 1-15, November.

    More about this item

    Keywords

    Nowcasting; India; Gross Value Added; Evening-hour Luminosity; Nightlight data; Dynamic Factor Analysis; EM Algorithm;
    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nca:ncaerw:115. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: B Ramesh (email available below). General contact details of provider: https://edirc.repec.org/data/ncaerin.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.