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Asia and Pacific > India

You are looking at 1 - 3 of 3 items for :

  • Type: Journal Issue x
  • Forecasting x
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Cover IMF Working Papers
Identifying Optimal Indicators and Lag Terms for Nowcasting Models
Jing Xie
Many central banks and government agencies use nowcasting techniques to obtain policy relevant information about the business cycle. Existing nowcasting methods, however, have two critical shortcomings for this purpose. First, in contrast to machine-learning models, they do not provide much if any guidance on selecting the best explantory variables (both high- and low-frequency indicators) from the (typically) larger set of variables available to the nowcaster. Second, in addition to the selection of explanatory variables, the order of the autoregression and moving average terms to use in the baseline nowcasting regression is often set arbitrarily. This paper proposes a simple procedure that simultaneously selects the optimal indicators and ARIMA(p,q) terms for the baseline nowcasting regression. The proposed AS-ARIMAX (Adjusted Stepwise Autoregressive Moving Average methods with exogenous variables) approach significantly reduces out-of-sample root mean square error for nowcasts of real GDP of six countries, including India, Argentina, Australia, South Africa, the United Kingdom, and the United States.
Keywords:
Nowcasting; Mixed Frequency; Forecasting; Business Cycles; selection procedure; Annex I. AS-ARIMAX procedure; nowcasting method; evaluation comparison; baseline model; Global
Cover IMF Working Papers
Overcoming Data Sparsity: A Machine Learning Approach to Track the Real-Time Impact of COVID-19 in Sub-Saharan Africa
Karim Barhoumi
,
Seung Mo Choi
,
Tara Iyer
,
Jiakun Li
,
Franck Ouattara
,
Mr. Andrew J Tiffin
, and
Jiaxiong Yao
The COVID-19 crisis has had a tremendous economic impact for all countries. Yet, assessing the full impact of the crisis has been frequently hampered by the delayed publication of official GDP statistics in several emerging market and developing economies. This paper outlines a machine-learning framework that helps track economic activity in real time for these economies. As illustrative examples, the framework is applied to selected sub-Saharan African economies. The framework is able to provide timely information on economic activity more swiftly than official statistics.
Keywords:
Sub-Saharan Africa; Economic Activity; GDP; Machine Learning; Nowcasting; COVID-19; machine learning approach; data sparsity; GDP statistics; crisis in Sub-Saharan Africa; learning framework; Oil prices; Real effective exchange rates; Africa; Global
Cover IMF Working Papers
Growth: Now and Forever?
Giang Ho
and
Mr. Paolo Mauro
Forecasters often predict continued rapid economic growth into the medium and long term for countries that have recently experienced strong growth. Using long-term forecasts of economic growth from the IMF/World Bank staff Debt Sustainability Analyses for a panel of countries, we show that the baseline forecasts are more optimistic than warranted by past international growth experience. Further, by comparing the IMF’s World Economic Outlook forecasts with actual growth outcomes, we show that optimism bias is greater the longer the forecast horizon.
Keywords:
WP; economic growth; country; growth rate; economic growth forecast; growth forecast; optimism bias; forecast horizon; debt sustainability analysis; World Bank DSA exercise; IMF engagement status; teams' baseline forecast; per capita income; IMF definition; standard deviation; Personal income; Output gap; Western Hemisphere; Sub-Saharan Africa; Global