Hunter Ng, Yubo Tao
arXiv 6 Oct 2026 · Econometrics
arXiv:2610.07589 · PDF · Extracted main text
Joint-event forecasts often combine a dependence estimate based on past forecast errors with newly estimated marginal distributions. When each historical error retains the marginal fit available at its issue date, inference must account for an overlapping sequence of estimation errors. We derive their joint influence with the terminal forecast estimates in a stable Vine Copula VAR with normal innovation margins and a fixed, correctly specified Gaussian or positive Clayton vine. An intercept identity and the stable VAR filter reduce the historical correction to harmonically weighted innovation moments, while terminal slope uncertainty remains. The resulting covariance estimator gives asymptotically valid repeated-sample intervals for fixed one-sided event probabilities at the realized forecast state. In the Gaussian submodel, retaining issued transforms adds a positive semidefinite covariance term relative to refitting margins on the same observations. Monte Carlo simulations show that terminal-margin uncertainty is quantitatively more important than this additional term and that logit intervals improve lower-tail coverage in the designs studied. A real-time forecasting application to U.S. macroeconomic releases shows how marginal estimation contributes to uncertainty in predicted probabilities of joint contractions and identifies limitations of the stationary marginal model.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Nagler, Thomas and Krüger, Daniel and Min, Aleksey (2022) Stationary Vine Copula Models for Multivariate Time Series | 0.874 | 6 | 2 | 100% |
| 2 | Patton, Andrew J (2012) A Review of Copula Models for Economic Time Series | 0.843 | 3 | 3 | 100% |
| 3 | Clark, Todd E (2011) Real-Time Density Forecasts From Bayesian Vector Autoregressions With Stochastic Volatility | 0.737 | 3 | 2 | 100% |
| 4 | Aas, Kjersti and Czado, Claudia and Frigessi, Arnoldo and Bakken, He… (2009) Pair-copula constructions of multiple dependence | 0.644 | 2 | 2 | 100% |
| 5 | Clements, Michael P. and Galvão, Ana Beatriz (2023) Density Forecasting with Bayesian Vector Autoregressive Models under Macroeconomic Data Uncertainty | 0.644 | 2 | 2 | 100% |
| 6 | Croushore, Dean and Stark, Tom (2001) A real-time data set for macroeconomists | 0.644 | 2 | 2 | 100% |
| 7 | Czado, Claudia and Nagler, Thomas (2022) Vine Copula Based Modeling | 0.644 | 2 | 2 | 100% |
| 8 | Hobæk Haff, Ingrid (2013) Parameter Estimation for Pair-Copula Constructions | 0.644 | 2 | 2 | 100% |
| 9 | Joe, Harry (2005) Asymptotic Efficiency of the Two-Stage Estimation Method for Copula-Based Models | 0.644 | 2 | 2 | 100% |
| 10 | Koenig, Evan F. and Dolmas, Sheila and Piger, Jeremy (2003) The Use and Abuse of Real-Time Data in Economic Forecasting | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 33 scored citations.