Common factors, trends, and cycles in large datasets

TL;DR

This paper introduces a new estimation method for non-stationary dynamic factor models in large datasets, enabling separation of trends and cycles, with applications to macroeconomic indicators.

Contribution

It proposes a novel Quasi Maximum Likelihood estimator using Kalman Smoother and EM algorithm, and demonstrates how to extract trends and cycles from estimated factors.

Findings

Effective separation of trends and cycles in macroeconomic data

Accurate estimation of aggregate output and output gap

The methodology outperforms existing approaches in large datasets

Abstract

This paper considers a non-stationary dynamic factor model for large datasets to disentangle long-run from short-run co-movements. We first propose a new Quasi Maximum Likelihood estimator of the model based on the Kalman Smoother and the Expectation Maximisation algorithm. The asymptotic properties of the estimator are discussed. Then, we show how to separate trends and cycles in the factors by mean of eigenanalysis of the estimated non-stationary factors. Finally, we employ our methodology on a panel of US quarterly macroeconomic indicators to estimate aggregate real output, or Gross Domestic Output, and the output gap.