SOURCE-LINKED INTELLIGENCE

Machine Learning Macroeconometric Methods for Dynamic Causal Inference

CORDIS · observation · Publication date unknown

Machine Learning Macroeconometric Methods for Dynamic Causal Inference Data lies at the heart of all economic decisions. Everyone — and especially central bankers, investors, and policymakers — processes data when making choices. Thanks to technological innovations, the speed at which (raw) data are generated and shared by businesses, public administrations, and scientific research (among others) have increased exponentially. Large amounts of data bring new opportunities and challenges to econometrics. The literature on microeconometric methods based on statistical learning techniques has grown substantially over the last decade, yet macroeconometrics literature lacks an understanding of such methods which could be applied to answer causal inference questions. The primary goal of the macroml research project is to put forward theory-driven methods for dynamic causal inference analysis b

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

230774.4

unit

EUR