Vector autoregressions with dynamic factor coefficients and conditionally heteroskedastic errors

Paolo Gorgi, Siem Jan Koopman and Julia Schaumburg

Journal of Econometrics, 2024, vol. 244, issue 2

Abstract: We introduce a new and general methodology for analyzing vector autoregressive models with time-varying coefficient matrices and conditionally heteroskedastic disturbances. The proposed approach is transparent and simple to implement. It allows the derivation of well-defined impulse response functions that rely on the overall stability of the system. We present the finite sample properties of the model in a simulation study. In an empirical illustration we investigate the possibly time-varying relationships between U.S. industrial production, inflation, and bond spread. We empirically identify a time-varying linkage between economic and financial variables which are effectively described by a common dynamic factor. The impulse response analysis identifies substantial differences in the effects of financial shocks on output and inflation during crisis and non-crisis periods. The results also illustrate how the widely-used approach of fixing the VAR coefficients in the derivation of the impulse responses leads to a sizeable underestimation of the impact of a financial shock on output and inflation during some of the crises in our sample.

Keywords: Time-varying parameters; Vector autoregressive model; Dynamic factor model; Kalman filter; Generalized autoregressive conditional heteroskedasticity; Orthogonal impulse response functions (search for similar items in EconPapers)
JEL-codes: C32 E31 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:244:y:2024:i:2:s0304407624000964

DOI: 10.1016/j.jeconom.2024.105750

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