Tests of Conditional Predictive Ability

By R. Giacomini, Halbert White. 2003

1,598 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/584af1e9a45301b82acab2f5dc49bfd2f276e6b4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Management Science and Operations Research

Citations in the corpus

Survey Data as Coincident or Leading Indicators (2010, neutral): “It is worth to clarify that, although the FPMM and FPMM-survey models are nested, rolling estimation 5Estimation results for the ADL are reported in the Appendix 14 validates the applicability of the Diebold-Mariano test (see Giacomini and White 2006).”
Deja Vol: Predictive Regressions for Aggregate Stock Market Volatility Using Macroeconomic Variables (2012, neutral): “14The asymptotic results in Giacomini and White (2006) require that the estimation sample remain bounded.”
MGARCH models: Trade-off between feasibility and flexibility (2018, methodology): “The out-of-sample predictions are evaluated using a the rolling window procedure as proposed by Giacomini and White (2006).”
Comparing forecast accuracy: A Monte Carlo investigation (2013, methodology): “A different approach is taken by Giacomini and White (2006) that focus on comparing 1We wish to thank Joerg Breitung, Graham Elliott, Raffaella Giacomini, Lutz Kilian, Michael McCracken and Ken West for useful suggestions on an earlier draft.”
Copula-based multivariate GARCH model with uncorrelated dependent errors (2009, methodology): “In addition, treating the test conditional on the estimated parameter values θ̂ jR in line with Giacomini and White (2006), the OOS reality check inference may handle the nested cases as well.”
Bayesian VARs with large panels (2007, methodology): “A comparison of forecasts based, alternatively, on Bayesian regression and principal components regression has recently been performed by De Mol, Giannone, and Reichlin (2006) and Giacomini and White (2006).”
Short‐Term Wind Speed Forecasting for Power System Operations (2012, background): “Typically, it is between 3 h and 10 h, but for quick resources, it can be under 3 h (Genton & Hering, 2007).”
Elusive Return Predictability (2008, methodology): “To get an indication of the relative forecasting performance of the approaches under consideration, we next computed the test statistic proposed by Giacomini and White (2006).”
Risk-Adjusted Forecasts of Oil Prices (2009, methodology): “In table 10 we present results of the unconditional test of predictive ability proposed by Giacomini and White (2006).12 It asks which forecast 12Viewing the di¤erence in forecast performance (e.g., squared prediction error) as the dependent variable in a regression containing only a constant, it…”
Copula Methods for Forecasting Multivariate Time Series (2013, methodology): “Giacomini and White (2006) consider OOS forecasting models that are based on an estimation window of nite length (i.e., a xed or rolling estimation scheme), and consider the forecast performance of two competing models conditional on their estimated parameters: H0 : E h log c1 Ût; ̂1t log c2 Ût;…”
Forecasting exchange rates with a large Bayesian VAR (2009, methodology): “Finally, the stars in the table denote rejection of the null of equal forecast accuracy of the models at 1%, 5%, and 10%, according to the Giacomini and White (2006) statistic.”
Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods (2019, neutral): “Table 4 reports the p -values of the unconditional Giacomini and White (2006) test for superior predictive ability for squared and absolute errors and the multi-horizon superior predictive ability test of Quaedvlieg (2017).”
Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection (2020, methodology): “and Giacomini and White (2006) test are used in the out-of-sample forecasting evaluations.”
Properties of Foreign Exchange Risk Premiums (2012, methodology): “The results for the Clark and West (2007) test and the Giacomini and White (2006) test for conditional predictive ability further support that the model predictions are more accurate than those of the benchmarks.”