The Model of Confidence

By P. Cofta. 2007

285 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b729f325a2d9a8ee3f08f91a9b668bcb7ab38f10

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In Artificial Intelligence

Citations in the corpus

Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold–Mariano Tests (2015, unknown): “Hansen, Lunde, and Nason (2011) went even farther by developing methods for controlling the family- wise error rate when performing many Vuong-type tests, allowing them to obtain a set of models containing the KLIC-optimal approximating model with controlled error rate, the so-called "model…”
Does Anything Beat 5-Minute RV? A Comparison of Realized Measures Across Multiple Asset Classes (2015, unknown): “…on the estimated differences in accuracy, such as the pair-wise comparisons of Diebold and Mariano (2002) and Giacomini and White (2006), the “reality check” of White (2000) as well as the multiple testing procedure of Romano and Wolf (2005), and the “model confidence set” of Hansen et al. (2011).”
Decoupling the Short- and Long-Term Behavior of Stochastic Volatility (2016, unknown): “We calculate MSE, QLIKE, and also the model confidence set (MCS) of Hansen et al. (2011), which is a procedure to construct a “set of best models” with a certain probability, as measured by the specific loss function in question, that avoids the problems that arise from doing multiple comparisons…”
Improved central limit theorem and bootstrap approximations in high dimensions (2019, methodology): “For example, it appears in selecting the regularization parameters for the Lasso estimator and the Dantzig selector ([11]), in carrying out reality checks for data snooping and testing superior predictive ability ([35, 21]), in constructing model confidence sets ([22]), in testing conditional and/or”
Forecast combinations for value at risk and expected shortfall (2020, methodology): “In each MCS test, we used the equivalence test based on the Diebold-Mariano test and the one-sided elimination rule described as Tmax,M by Hansen et al. (2011). We followed Hansen et al. (2011) by considering 75% and 90% confidence levels, and we report the”
Combining density forecasts using focused scoring rules (2017, unknown): “The optimized csl weights are namely always in the MCS, irrespective of 𝜅.”
Forecasting oil price realized volatility using information channels from other asset classes (2017, methodology): “We further employ the newly established Model Confidence Set (MCS) procedure by Hansen et al. (2011), which identifies the set of the best models, as these are defined in terms of a specific loss function, without an a priori choice of a benchmark model 11 .”
Econometric Analysis of Vast Covariance Matrices Using Composite Realized Kernels and Their Application to Portfolio Choice (2016, methodology): “To assess whether the differences in the selected minimum variance portfolios are statistically significant we apply the model confidence set (MCS) methodology suggested by Hansen, Lunde, and Nason (2011).”
Forecasting of density functions with an application to cross-sectional and intraday returns (2019, methodology): “The model confidence set procedure proposed by Hansen, Lunde, and Nason (2011) consists of a sequence of tests that permit the construction of a set of ‘‘superior" models for which the null hypothesis of equal predictive ability (EPA) is not rejected at a specified confidence level.”
Cross-Validation With Confidence (2017, methodology): “[16] consider this problem for low-dimensional linear regression problems using a hierarchical testing procedure based on the F -test.”
Forecasting the realized range-based volatility using dynamic model averaging approach (2017, methodology): “It means that those “surviving” models have better performance than the models removed by MCS test (Hansen et al., 2011).”
Harnessing jump component for crude oil volatility forecasting in the presence of extreme shocks (2019, methodology): “Next, we used an advanced statistical test, the Model Confidence Set (MCS) test proposed by Hansen et al. (2011), to choose a subset of models containing all superior models from the initial model set.”
Forecasting Accuracy Evaluation of Tourist Arrivals: Evidence from Parametric and Non-Parametric Techniques (2015, unknown): “The detailed results from Hansen et al.’s (2011) MCS test are available upon request.”
Multi-Horizon Forecast Comparison (2019, methodology): “In such a setting, taking into account the heterogeneity, the variance can be estimated using a HAC-type estimator, as in for instance Giacomini and White (2006) or, following Hansen et al. (2011), it may be obtained using bootstrap methods.”
INFERENCE AFTER MODEL AVERAGING IN LINEAR REGRESSION MODELS (2018, methodology): “There are also many alternative approaches to model averaging, for example, bagging (Breiman, 1996; Inoue and Kilian, 2008), LASSO (Tibshirani, 1996), adaptive LASSO (Zou, 2006), and the model confidence set (Hansen et al., 2011), among others.”
Forecasting global stock market implied volatility indices (2018, methodology): “In addition, we employ the Model Confidence Set (MCS) procedure of Hansen et al. (2011).”