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Distributional Vector Autoregression: Eliciting Macro and Financial Dependence

Yunyun Wang et al.

Journal of Applied Econometrics2026https://doi.org/10.1002/jae.70049article
AJG 3ABDC A*
Weight
0.50

What the paper says

This paper extends the vector autoregression framework by introducing a flexible distributional regression that models multivariate time series without imposing restrictive parametric distribution assumptions. We develop a distributional impulse response function that captures the future effect of distributional disturbances within the system, providing a more detailed view of dynamic heterogeneity. We propose a straightforward estimation method and establish its asymptotic properties under weak dependence assumptions. Our model, in an application to U.S. economic data, exhibits strong forecasting capabilities compared to existing alternatives. By examining distributional interactions and monetary policy impacts, particularly during the Great Recession, we uncover complex macroeconomic dynamics.

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https://doi.org/https://doi.org/10.1002/jae.70049

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@article{yunyun2026,
  title        = {{Distributional Vector Autoregression: Eliciting Macro and Financial Dependence}},
  author       = {Yunyun Wang et al.},
  journal      = {Journal of Applied Econometrics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1002/jae.70049},
}

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Distributional Vector Autoregression: Eliciting Macro and Financial Dependence

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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