A Survey On Forecasting of GDP: An Issue of Relevance in Macro-economics

The present paper is a review of various models concerned with forecasting of GDP, both in the short and long run. Certain parameters that influence growth of GDP, are identified and analyzed from quarterly, monthly or yearly data sets. Regression has been heavily used in this context, be it linear or auto-regressive integrated moving average (ARIMA) model. Small bridge equations play an important role to bridge the gap between monthly and quarterly data sets, and between quarterly and yearly data sets, in literature of forecasting and nowcasting GDP in economics.