© 2017 The Department of Economics, University of Oxford and John Wiley & Sons Ltd.
OXFORD BULLETIN OF ECONOMICS AND STATISTICS, 80, 2 (2018) 0305–9049
Nowcasting Indian GDP*
Daniela Bragoli† and Jack Fosten‡
†Department of Mathematics, Financial Mathematics and Econometrics, Universit`a
Cattolica, via Necchi 9, 29100, Milano, Italy (e-mail: email@example.com)
‡Department of Economics, University of East Anglia, Norwich, NR4 7TJ, UK
Nowcasting has become a useful tool for making timely predictions of gross domestic
product (GDP) in a data-rich environment. However, in developing economies this is more
challenging due to substantial revisions in GDP data and the limited availability of predictor
variables.Taking India as a leading case, we use a dynamic factor model nowcasting method
to analyse these two issues. Firstly, we propose to compare nowcasts of the ﬁrst release
of GDP to those of the ﬁnal release to assess differences in their predictability. Secondly,
we expand a standard set of predictors typically used for nowcasting GDP with nominal
and international series, in order to proxy the variation in missing employment and service
sector variables in India. We ﬁnd that the factor model improves over several benchmarks,
including bridge equations, but only for the ﬁnal GDP release and not for the ﬁrst release.
Also, the nominal and international series improve predictions over and above real series.
This suggests that future studies of nowcasting in developing economies which have similar
issues of data revisions and availability as India should be careful in analysing ﬁrst- vs. ﬁnal-
release GDP data, and may ﬁnd that predictions are improved when additional variables
from more timely international data sources are included.
In recent years, nowcasting has emerged as an important tool for producing timely predic-
tions of economic activity variables such as gross domestic product (GDP). Since GDP
ﬁgures are only published on a quarterly basis, and typically with a publication lag of
more than a month, nowcasting provides policymakers and other market participants with
a timely snapshot of the current state of the economy with which to inform their policy or
investment decisions. In developed economies such as the United States and the eurozone,
where rich and timely economic datasets are available, papers such as Evans (2005), Gian-
none, Reichlin and Small (2008) and Ba´nbura et al. (2013) have established that suitable
nowcasting methods can provide predictions of GDP which are both more timely and at
least as accurate as surveys of professional forecasters or na¨ıve benchmark models.
JEL Classiﬁcation numbers: C38, C53, E37, O11, O47.
*We thank participants of several research meetings held at Now-Casting Economics Ltd., London, for their
feedback and advice. We also directly thank Now-Casting Economics Ltd. for access to data. This research did not
receive any speciﬁc grant from funding agencies in the public, commercial, or not-for-proﬁt sectors.