MAXIMUM LIKELIHOOD ESTIMATION OF FACTOR MODELS ON DATASETS WITH ARBITRARY PATTERN OF MISSING DATA

2012/11/12 by Marta Bańbura, Michèle Modugno · 4 citations
Economics, Econometrics and Finance · Mathematics · #Monetary Policy and Economic Impact #Financial Risk and Volatility Modeling #Statistical Methods and Inference

paper · doi:10.1002/jae.2306

openalex publication_date 2012/11/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/11

Abstract

SUMMARY In this paper we modify the expectation maximization algorithm in order to estimate the parameters of the dynamic factor model on a dataset with an arbitrary pattern of missing data. We also extend the model to the case with a serially correlated idiosyncratic component. The framework allows us to handle efficiently and in an automatic manner sets of indicators characterized by different publication delays, frequencies and sample lengths. This can be relevant, for example, for young economies for which many indicators have been compiled only recently. We evaluate the methodology in a Monte Carlo experiment and we apply it to nowcasting of the euro area gross domestic product. Copyright © 2012 John Wiley & Sons, Ltd.

Citations

• Nowcasting: The real-time informational content of macroeconomic data
• The Generalized Dynamic-Factor Model: Identification and Estimation
• On Gibbs sampling for state space models
• Relative Goods' Prices, Pure Inflation, and The Phillips Correlation

Cited by

• Big brothering the economy: nowcasting and forecasting with port satellite images
• GDP nowcasting with machine learning and unstructured data
• Forecasting With Dynamic Factor Models Estimated by Partial Least Squares
• Nowcasting (economics) [wikipedia]

Related

• Maximum a Posteriori Estimation of Dynamic Factor Models with Incomplete Data
• Full information maximum likelihood estimation in factor analysis with a lot of missing values
• Common factors, trends, and cycles in large datasets
• Estimation of large approximate dynamic matrix factor models based on the EM algorithm and Kalman filtering
• Approximate Factor Models with Strongly Correlated Idiosyncratic Errors