Comparing Density Forecasts via Weighted Likelihood Ratio Tests: Asymptotic and Bootstrap Methods

By R. Giacomini. 2002

432 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/28c5c985390cf5f205fa41b0ea12cfcc3c539a66

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Citations in the corpus

Tests of Conditional Predictive Ability (2003, methodology): “Finally, we run the regressions (13), (14), (16), (17) and apply the Bayesian shrinkage method for t =1960:1,.”
New HEAVY Models for Fat-Tailed Realized Covariances and Returns (2017, methodology): “We use the log scoring rule (see Mitchell and Hall 2005; Amisano and Giacomini 2007) to differentiate between the density forecasts of the models.”
Adaptive models and heavy tails with an application to inflation forecasting (2017, background): “Those differences are large and always statistically significant according to the Amisano and Giacomini (2007) test.”
Modeling Realized Covariances and Returns (2013, background): “18Classical approaches to comparison of density forecasts include Amisano and Giacomini (2007), Bao, Lee, and Saltoglu (2007) and Weigend and Shi (2000).”
The Stochastic Volatility in Mean Model With Time-Varying Parameters: An Application to Inflation Modeling (2017, background): “…we also compute density forecasts—it is generally acknowledged that when assessing the quality of competing models, both the ability to accurately predict the central tendency and the uncertainty around the forecasts should be taken into account (see, e.g., Amisano and Giacomini 2007; Clark 2011).”
Predictive density and conditional confidence interval accuracy tests (2006, background): “White (1982), Vuong (1989), Giacomini (2002), and Kitamura (2004)), according to which we should choose Model 1 over Model 2 if”
Combining inflation density forecasts (2010, methodology): “(2) Thus, when ElnS1 > ElnS2 then KLIC1 KLIC2.”
Copula Methods for Forecasting Multivariate Time Series (2013, background): “Let YtjFt 1 s F ( jFt 1) with YitjFt 1 s Fi ( jFt 1) , i = 1; 2; :::; n then F (yjFt 1) = C (F1 (y1jFt 1) ; :::; Fn (ynjFt 1) jFt 1) (4)”
Common Drifting Volatility in Large Bayesian VARs (2012, neutral): “15 Compared to the BVAR, our proposed BVAR-CSV model lowers (14)For brevity, results based on AR(2) models as benchmarks are not reported.”
Multivariate High-Frequency-Based Volatility (HEAVY) Models (2012, methodology): “Similar approaches are proposed for out-of-sample model selection in Amisano and Giacomini (2007) and Diks et al. (2008).”
Real-Time Density Forecasts From Bayesian Vector Autoregressions With Stochastic Volatility (2011, methodology): “To help provide a rough gauge of the significance of score differences, I rely on the methodology developed by Amisano and Giacomini (2007), and report p-values for selected differences in mean scores, under the null of a zero mean.”
Forecasting: theory and practice (2020, methodology): “ility integral transform and the log predictive density scores (as main references, Wolters, 2015; Kolasa and Rubaszek, 2015a). The statistical signiﬁcance of these predictions is evaluated using the Amisano and Giacomini (2007) test that compares log predictive density scores from two competing mod”
Out-of-Sample Comparison of Copula Specifications in Multivariate Density Forecasts (2010, background): “Comparison of KLIC for assessing relative predictive accuracy has recently become popular for evaluation of univariate density forecasts, see Mitchell and Hall (2005), Amisano and Giacomini (2007) and Bao et al.”
Likelihood-based scoring rules for comparing density forecasts in tails (2011, methodology): “This problem of relative predictive accuracy has been considered by Sarno and Valente (2004); Mitchell and Hall (2005); Corradi and Swanson (2005, 2006b); Amisano and Giacomini (2007) and Bao et al. (2004, 2007).”