Reference health
Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions
4 of 63 checkable references need attention · checked 2026-08-02
At the dated check, the references listed below either did not resolve in Crossref or DataCite, or carried a retraction notice. Each one is shown with the registry record that put it there.
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References needing attention
does not resolve to a known work10.1016/S0304-4076
does not resolve to a known work10.1016/S1574-0706(05
does not resolve to a known work10.2307/1392185
does not resolve to a known work10.1016/S0167-7152(01
The 59 checked references that resolve
resolves10.1016/j.red.2017.01.003
Financial conditions and density forecasts for US output and inflation
resolves10.2307/2951574
An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator
resolves10.1111/1467-9868.00391
Distributions Generated by Perturbation of Symmetry with Emphasis on a Multivariate Skew<i>t</i>-Distribution
resolves10.1016/j.jeconom.2019.04.024
Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors
resolves10.1198/jbes.2010.09248
Real-Time Density Forecasts From Bayesian Vector Autoregressions With Stochastic Volatility
resolves10.1002/jae.2379
Macroeconomic Forecasting Performance under Alternative Specifications of Time-Varying Volatility
resolves10.1016/j.jedc.2005.06.005
Bayesian fan charts for U.K. inflation: Forecasting and sources of uncertainty in an evolving monetary system
resolves10.1016/j.red.2004.10.009
Drifts and volatilities: monetary policies and outcomes in the post WWII US
resolves10.1198/jbes.2009.07214
Comparing Greenbook and Reduced Form Forecasts Using a Large Realtime Dataset
resolves10.1016/j.econlet.2020.109126
Vulnerable growth in the euro area: Measuring the financial conditions
resolves10.1198/jbes.2010.08110
Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules
resolves10.2139/ssrn.3332882
Improved Methods for Combining Point Forecasts for An Asymmetrically Distributed Variable
resolves10.1016/j.jmva.2012.05.004
Geometric ergodicity of the Gibbs sampler for Bayesian quantile regression
resolves10.1111/1467-937x.00050
Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models
resolves10.1016/j.ijforecast.2016.07.005
Quantile regression forecasts of inflation under model uncertainty
resolves10.1080/07350015.2018.1514309
Real-Time Macroeconomic Forecasting With a Heteroscedastic Inversion Copula
resolves10.1080/07350015.2014.937436
Forecasting the Distribution of Economic Variables in a Data-Rich Environment
resolves10.1016/j.ijforecast.2013.01.005
Are macroeconomic variables useful for forecasting the distribution of U.S. inflation?
resolves10.1111/obes.12065
Asymmetric Quantile Persistence and Predictability: the Case of US Inflation
resolves10.1111/j.1467-937x.2005.00353.x
Time Varying Structural Vector Autoregressions and Monetary Policy
resolves10.1080/07350015.2015.1044533
Asymmetric Forecast Densities for U.S. Macroeconomic Variables from a Gaussian Copula Model of Cross-Sectional and Serial Dependence
resolves10.1080/07350015.2017.1281815
Forecasting Value at Risk and Expected Shortfall Using a Semiparametric Approach Based on the Asymmetric Laplace Distribution
The 9 references without a DOI — listed, not checked
no DOI — not checkedref12
no DOI — not checkedref17
no DOI — not checkedref27
no DOI — not checkedExpected Shortfall is Jointly Elicitable with Value at Risk -Implications for Backtesting
no DOI — not checkedQuantile-based Inflation Risk Models
no DOI — not checkedref53
no DOI — not checkedref58
no DOI — not checkedin-sample and out-of-sample forecasts of GDP growth for 1985-2018. The top panel compares estimates from the QR model. The bottom panel compares estimates from the five-variable BVAR-SV model. Expected longrise and shortfall: GDP growth QR vs
no DOI — not checkedLong-rise and expected shortfall using 10 and 90 percent quantiles, respectively, insample forecasts of GDP growth for 1972-2018. The top panel compares estimates from the QR and 5-variable BVAR-SV models. The bottom panel compares estimates from the BVAR-SV and BVAR-SVF-M models. longrise and shortfall for artificial data set 21
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