[Click on "Machine Learning" at right for earlier "Machine Learning and Econometrics" posts.]
The predictive modeling perspective needs not only to be respected and embraced in econometrics (as it routinely is, notwithstanding the Angrist-Pischke revisionist agenda), but also to be enhanced by incorporating elements of statistical machine learning (ML). This is particularly true for cross-section econometrics insofar as time-series econometrics is already well ahead in that regard. For example, although flexible non-parametric ML approaches to estimating conditional-mean functions don't add much to time-series econometrics, they may add lots to cross-section econometric regression and classification analyses, where conditional mean functions may be highly nonlinear for a variety of reasons. Of course econometricians are well aware of traditional non-parametric issues/approaches, especially kernel and series methods, and they have made many contributions, but there's still much more to be learned from ML.
The predictive modeling perspective needs not only to be respected and embraced in econometrics (as it routinely is, notwithstanding the Angrist-Pischke revisionist agenda), but also to be enhanced by incorporating elements of statistical machine learning (ML). This is particularly true for cross-section econometrics insofar as time-series econometrics is already well ahead in that regard. For example, although flexible non-parametric ML approaches to estimating conditional-mean functions don't add much to time-series econometrics, they may add lots to cross-section econometric regression and classification analyses, where conditional mean functions may be highly nonlinear for a variety of reasons. Of course econometricians are well aware of traditional non-parametric issues/approaches, especially kernel and series methods, and they have made many contributions, but there's still much more to be learned from ML.
Belum ada tanggapan untuk "ML and Metrics VII: Cross-Section Non-Linearities"
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