scholarly journals Forecast Evaluation of Small Nested Model Sets

2009 ◽  
Author(s):  
Kirstin Hubrich ◽  
Kenneth D. West
Keyword(s):  
Forecast Evaluation ◽  
Nested Model ◽  
Model Sets

scholarly journals Forecast evaluation of small nested model sets

2010 ◽  
Vol 25 (4) ◽  
pp. 574-594 ◽  
Author(s):  
Kirstin Hubrich ◽  
Kenneth D. West
Keyword(s):  
Forecast Evaluation ◽  
Nested Model ◽  
Model Sets

scholarly journals “A Bias Recognized Is a Bias Sterilized”: The Effects of a Bias in Forecast Evaluation

Mathematics ◽  
2022 ◽  
Vol 10 (2) ◽  
pp. 171
Author(s):  
Nicolas Hardy
Keyword(s):  
Asymptotic Distribution ◽  
Null Model ◽  
Critical Values ◽  
Forecast Evaluation ◽  
Nuisance Parameters ◽  
Nested Model ◽  
Nominal Size ◽  
Out Of Sample ◽  
Size Distortions ◽  
Encompassing Test

Are traditional tests of forecast evaluation well behaved when the competing (nested) model is biased? No, they are not. In this paper, we show analytically and via simulations that, under the null hypothesis of no encompassing, a bias in the nested model may severely distort the size properties of traditional out-of-sample tests in economic forecasting. Not surprisingly, these size distortions depend on the magnitude of the bias and the persistency of the additional predictors. We consider two different cases: (i) There is both in-sample and out-of-sample bias in the nested model. (ii) The bias is present exclusively out-of-sample. To address the former case, we propose a modified encompassing test (MENC-NEW) robust to a bias in the null model. Akin to the ENC-NEW statistic, the asymptotic distribution of our test is a functional of stochastic integrals of quadratic Brownian motions. While this distribution is not pivotal, we can easily estimate the nuisance parameters. To address the second case, we derive the new asymptotic distribution of the ENC-NEW, showing that critical values may differ remarkably. Our Monte Carlo simulations reveal that the MENC-NEW (and the ENC-NEW with adjusted critical values) is reasonably well-sized even when the ENC-NEW (with standard critical values) exhibits rejections rates three times higher than the nominal size.

Model sets with positive entropy in Euclidean cut and project schemes

2019 ◽  
Vol 52 (5) ◽  
pp. 1073-1106 ◽  
Author(s):  
Tobias JÄGER ◽  
Daniel LENZ ◽  
Christian OERTEL
Keyword(s):  
Positive Entropy ◽  
Model Sets

Forecast Evaluation

2019 ◽  
Author(s):  
Mingmian Cheng ◽  
Norman Rasmus Swanson ◽  
Chun Yao
Keyword(s):  
Forecast Evaluation

scholarly journals Design of a structure model set for inorganic compounds based on ping-pong balls linked with snap buttons

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ryo Horikoshi ◽  
Hiroyuki Higashino ◽  
Yoji Kobayashi ◽  
Hiroshi Kageyama
Keyword(s):  
High School Students ◽  
Coordination Compounds ◽  
Periodic Structures ◽  
Inorganic Compounds ◽  
Structure Model ◽  
School Students ◽  
Model Sets ◽  
Ping Pong ◽  
Hands On ◽  
Model Set

Abstract Structure model sets for inorganic compounds are generally expensive; their distribution to all students in a class is therefore usually impractical. We have therefore developed a structure model set to illustrate inorganic compounds. The set is constructed with inexpensive materials: ping-pong balls, and snap buttons. The structure model set can be used to illustrate isomerism in coordination compounds and periodic structures of ceramic perovskites. A hands-on activity using the structure model set was developed for high school students and was well-received by them. Despite the concepts being slightly advanced for them, the students’ retention of the knowledge gained through the activity was tested a week after they completed the activity and was found to be relatively high, demonstrating the usefulness of the activity based on the structure model set.

Efficient Dynamics Estimation With Adaptive Model Sets

2021 ◽  
Vol 6 (2) ◽  
pp. 2373-2380
Author(s):  
Ellis Ratner ◽  
Andrea Bajcsy ◽  
Terrence Fong ◽  
Claire J. Tomlin ◽  
Anca D. Dragan
Keyword(s):  
Adaptive Model ◽  
Model Sets

Robust control from data via uncertainty model sets identification

2004 ◽  
Vol 14 (11) ◽  
pp. 945-957 ◽  
Author(s):  
S. Malan ◽  
M. Milanese ◽  
D. Regruto ◽  
M. Taragna
Keyword(s):  
Robust Control ◽  
Model Sets ◽  
Uncertainty Model
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