A FRAMEWORK FOR USING MACHINE LEARNING AND DEEP LEARNING TECHNIQUES IN NOWCASTING: REAL-TIME DATA AND MULTIPLE FREQUENCY ECONOMIC DATA RELEASES FOR ECONOMISTS AND POLICY MAKERS

Abstract

This paper presents a comprehensive framework that integrates machine learning and deep learning techniques, specifically stacked Long Short-Term Memory (LSTM) models, to enhance nowcasting in economics. The framework focuses on incorporating real-time data and multiple frequency economic data releases, including quarterly, monthly, and weekly metrics. By leveraging stacked LSTM models to capture complex patterns and trends in data, the framework aims to provide more accurate and timely GDP forecasts. This enables economists and policymakers to make well-informed decisions, particularly during contingent situations such as pandemics, natural disasters, or war. The proposed framework is highly generalizable and can be scaled for creating economic forecasts for any country with sufficient historical data, making it a valuable tool for international organizations and governments.