Efficient parameter-estimating algorithms for symmetry-motivated models: Econometrics and beyond
© 2018, Springer International Publishing AG. It is known that symmetry ideas can explain the empirical success of many non-linear models. This explanation makes these models theoretically justified and thus, more reliable. However, the models remain non-linear and thus, identification or the model’...
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| Main Authors: | , , , |
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| 格式: | Book Series |
| 出版: |
2018
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| 在線閱讀: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85038827524&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/43923 |
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| 機構: | Chiang Mai University |
| 總結: | © 2018, Springer International Publishing AG. It is known that symmetry ideas can explain the empirical success of many non-linear models. This explanation makes these models theoretically justified and thus, more reliable. However, the models remain non-linear and thus, identification or the model’s parameters based on the observations remains a computationally expensive nonlinear optimization problem. In this paper, we show that symmetry ideas can not only help to select and justify a nonlinear model, they can also help us design computationally efficient almost-linear algorithms for identifying the model’s parameters. |
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