Estimation and Inference of FAVAR Models

Jushan Bai · Kunpeng Li · Lina Lu

62 citations0/0 files storedJournal of Business and Economic Statistics

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Monetary Policy and Economic Impact Financial Risk and Volatility Modeling Spatial and Panel Data Analysis

Abstract

The factor-augmented vector autoregressive (FAVAR) model is now widely used in macroeconomics and finance. In this model, observable and unobservable factors jointly follow a vector autoregressive process, which further drives the comovement of a large number of observable variables. We study the identification restrictions for FAVAR models, and propose a likelihood-based two-step method to estimate the model. The estimation explicitly accounts for factors being partially observed. We then provide an inferential theory for the estimated factors, factor loadings, and the dynamic parameters in the VAR process. We show how and why the limiting distributions are different from the existing results. Supplementary materials for this article are available online.

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References

1980Macroeconomics and Reality 2002Determining the Number of Factors in Approximate Factor Models 2002Forecasting Using Principal Components From a Large Number of Predictors 1998Monetary Policy Shocks: What Have We Learned and to What End?1992Interpreting the macroeconomic time series facts 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

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2016Econometric Analysis of Large Factor Models 2020Revisiting the location of FDI in China: A panel data approach with heterogeneous shocks 2018Bootstrap inference for impulse response functions in factor‐augmented vector autoregressions 2023Estimation and Inference on Time-Varying FAVAR Models

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