A Test for Superior Predictive Ability

By P. R. Hansen. 2005

1,248 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/83d10adb98319ab3fcc15dfa44dd3f7ff80e4bda

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

1. Andrew J. Patton
2. Allan G. Timmermann
3. Stavros Degiannakis *
4. P. R. Hansen
5. George Filis
6. Barbara Rossi
7. Fotios Petropoulos
8. David E. Rapach

In Finance

1. Andrew J. Patton
2. Stavros Degiannakis *
3. George Filis
4. S. J. Koopman
5. Allan G. Timmermann
6. Marcelo C. Medeiros
7. Kevin Sheppard
8. Esther Ruiz

In Management Science and Operations Research

1. Fotios Petropoulos
2. Rob J Hyndman
3. Nikolaos Kourentzes
4. Konstantinos Nikolopoulos
5. Vassilios Assimakopoulos
6. Spyros Makridakis
7. Evangelos Spiliotis
8. Christoph Bergmeir

Citations in the corpus

• Volatility forecasting of crude oil market: Can the regime switching GARCH model beat the single-regime GARCH models? (2019, methodology): “However, the satisfactory statistical results are likely to appear by chance rather than due to the inherent superiority of the model (Hansen, 2005; White, 2000).”
• Frontiers in VaR forecasting and backtesting (2016, background): “On the other hand, Bao et al. (2006) and Angelidis and Degiannakis (2007) propose to compare alternative VaR forecasts using the test of superior predictive ability (SPA) of Hansen (2005).”
• A Kolmogorov-Smirnov Based Test for Comparing the Predictive Accuracy of Two Sets of Forecasts (2015, background): “Two sound examples would be Hansen’s [7] Superior Predictive Ability (SPA) test, and Hansen et al.’s [8] Model Confidence Set (MCS) which are superior to the DM test.”
• Bonferroni-Based Size-Correction for Nonstandard Testing Problems (2017, neutral): “The methods of Loh (1985) and Hansen (2005b) are analogous to letting δ → α as n → ∞ in the confidence set Iα−δ(ĥn), but simply using the maximand ch(1−α) in the construction of the CV.”
• Predicting Bid–Ask Spreads Using Long‐Memory Autoregressive Conditional Poisson Models (2011, neutral): “Details of the stationary bootstrap procedure can be found in Hansen (2005).”
• Evaluating Volatility and Correlation Forecasts (2009, background): “When interest lies in comparing many competing forecasts there are two main approaches: the “Reality Check” [62], and a modified version with better power properties [34], or the “Model Confidence Set” [38].”
• Forecasting oil price realized volatility using information channels from other asset classes (2017, methodology): “…the Diebold-Mariano test (Diebold and Mariano, 1995), for a pairwise comparison, the Equal Predictive Accuracy test (Clark and West, 2007) for nested models, or the Reality Check for Data Snooping (White, 2000) and the Superior Predictive Ability test (Hansen, 2005) for multiple comparisons.”
• Forecasting Volatility Using High-Frequency Data (2011, methodology): “The predictive ability of the alternative models is compared using formal testing via the test for superior predictive ability (SPA) by Hansen (2005).”