Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics

418 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes (2019, methodology): “Several multivariate extensions of classical univariate methods are widely used, such as vector autoregressions (VAR) extending autoregressive models [19], multivariate state-space models [7], or ∗Work done while being at Amazon Research”
Macroeconomic Shocks and Their Propagation (2016, background): “Stock et al. (2002) argue that the first-stage F-statistic should be above 10 for the IV estimates to be reliable, but their threshold applies only to first-stage regressions with serially uncorrelated error terms.”
Local Projections vs. VARs: Lessons From Thousands of DGPs (2021, methodology): “As in Stock & Watson (2016), we fit AR( p v ) processes by OLS to each idiosyncratic residual after removing the estimated factors, separately for each i .”
Oil price elasticities and oil price fluctuations (2016, neutral): “literature—surveyed in Stock and Watson (2016)—studying the macroeconomic effects of oil shocks.”
Identification and Estimation of Dynamic Causal Effects in Macroeconomics Using External Instruments (2018, positive): “As discussed in Stock and Watson (2016), the unit effect normalisation also allows for direct extension of SVAR methods to structural dynamic factor models.”