A Random Matrix Approach to Dynamic Factors in macroeconomic data

TL;DR

This paper introduces a novel application of random matrix theory to extract dynamic factors from macroeconomic data, especially useful when dealing with large datasets where traditional methods struggle.

Contribution

It develops new algorithms based on free random variables theory to analyze macroeconomic time series with large N and T, providing a fresh approach to factor extraction.

Findings

Effective factor extraction in macroeconomic data

Application to Polish economic indicators

Demonstrates the utility of FRV in macroeconomics

Abstract

We show how random matrix theory can be applied to develop new algorithms to extract dynamic factors from macroeconomic time series. In particular, we consider a limit where the number of random variables N and the number of consecutive time measurements T are large but the ratio N / T is fixed. In this regime the underlying random matrices are asymptotically equivalent to Free Random Variables (FRV).Application of these methods for macroeconomic indicators for Poland economy is also presented.