The growth rate of real Gross Domestic Product (GDP), as measured by the National Statistical Office of India, is an important metric for monetary policy making. Because GDP is released with a significant lag, particularly for the emerging market economies, this article presents various methodologies for nowcasting and forecasting GDP, using both traditional time series and machine learning methods. Further, considering the importance of forward-looking information, our nowcasting model incorporates financial market data and an economic uncertainty index, in addition to high-frequency traditional macroeconomic indicators. Our findings suggest an improvement in the performance of nowcasting using a hybrid of machine learning and conventional time series methods.
Ghosh, Saurabh and Ranjan, Abhishek
"A MACHINE LEARNING APPROACH TO GDP NOWCASTING:
AN EMERGING MARKET EXPERIENCE,"
Bulletin of Monetary Economics and Banking: Vol. 26:
0, Article 3.
Available at: https://bulletin.bmeb-bi.org/bmeb/vol26/iss0/3