Likelihood-based Analysis for Dynamic Factor Models

Borus Jungbacker · Siem Jan Koopman

97 citations0/0 files storedRePEc: Research Papers in Economics

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Spatial and Panel Data Analysis Statistical Methods and Applications Statistical and numerical algorithms

Abstract

We present new results for the likelihood-based analysis of the dynamic factor model that possibly includes intercepts and explanatory variables.The latent factors are modelled by stochastic processes.The idiosyncratic disturbances are specified as autoregressive processes with mutually correlated innovations.The new results lead to computationally efficient procedures for the estimation of the factors and parameter estimation by maximum likelihood and Bayesian methods.An illustration is provided for the analysis of a large panel of macroeconomic time series.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 97 citations exist upstream.

References

2002Forecasting Using Principal Components From a Large Number of Predictors 2002Macroeconomic Forecasting Using Diffusion Indexes 2005Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach 2000The Generalized Dynamic-Factor Model: Identification and Estimation 2003Inferential Theory for Factor Models of Large Dimensions 2009Large Bayesian vector auto regressions 2005The macroeconomy and the yield curve: a dynamic latent factor approach 2005Implications of Dynamic Factor Models for VAR Analysis

Cited by

2014The SR approach: A new estimation procedure for non-linear and non-Gaussian dynamic term structure models 2009Estimating US Monetary Policy Shocks Using a Factor-Augmented Vector Autoregression: An EM Algorithm Approach 2018Dynamic Effects of Credit Shocks in a Data-Rich Environment 2009Estimating US Monetary Policy Shocks Using a Factor-Augmented Vector Autoregression: An EM Algorithm Approach 2010Real-Time Macroeconomic Monitoring: Real Activity, Inflation, and Interactions

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.