Dear Chrisophe, I think you are oversimplifying by saying econometrics tools are for inference. Forecasting and prediction are integral parts of econometric analysis. Econometricians forecast by inferring the right conclusion about the model . I wish to convey to you that I teach both statistics and econometrics, and am now learning ML. There is a fundamental difference among statistics, econometrics and machine learning. Regards,
Samir K Mahajan On Fri, Aug 13, 2021 at 3:39 PM Christophe Pallier <christo...@pallier.org> wrote: > Indeed , this is basically what I told you (you do not be need to copy > textbook stuff: I taught probas/stats) : these are mostly problems for > *inference*. > > On Fri, 13 Aug 2021, 12:03 Samir K Mahajan, <samirkmahajan1...@gmail.com> > wrote: > >> >> Dear Christophe Pallier*,* >> >> When we are doing prediction, we are relying on the values of the >> coefficients of the model created. We are feeding test data on the model >> for prediction. We may be nterested to see if the OLS >> estimators(coefficients) are BLUE or not. In the presence of >> autocorrelation (normally noticed in time series data), residuals are not >> independent, and as such the OLS estimators are not BLUE in the sense that >> they don't have minimum variance, and thus no more efficient estimators. >> Statistical tests (t, F and *χ*2) may not be valid. We may reject the >> model to make predictions in such a situation. . We have to rely upon >> other improved models. There may be issues relating to multicollinearity >> (in case of multivariable regression model) and heteroscedasticity (mostly >> seen in cross-section data) too in a model. Can we discard these tools >> while predicting a model? >> >> Regards, >> >> Samir K Mahajan >> >> >> On Fri, Aug 13, 2021 at 1:07 PM Christophe Pallier < >> christo...@pallier.org> wrote: >> >>> Actually, multicollinearity and autocorrelation are problems for >>> *inference* more than for *prediction*. For example, if there is >>> autocorrelation, the residuals are not independent, and the degrees of >>> freedom are wrong for the tests in an OLS model (but you can use, e.g., an >>> AR1 model). >>> >>> On Thu, 12 Aug 2021, 22:32 Samir K Mahajan, <samirkmahajan1...@gmail.com> >>> wrote: >>> >>>> A note please (to Sebastian Raschka, mrschots). >>>> >>>> >>>> The OLS model that I used ( where the test score gave me a negative >>>> value) was not a good fit. Initial findings showed that t*he >>>> regression coefficients and the model as a whole were significant, *yet >>>> , finally , it failed in two econometrics tests such as VIF (used for >>>> detecting multicollinearity ) and Durbin-Watson test ( used for detecting >>>> auto-correlation). *Presence of multicollinearity and autocorrelation >>>> problems * in the model make it unsuitable for prediction. >>>> Regards, >>>> >>>> Samir K Mahajan. >>>> >>>> On Fri, Aug 13, 2021 at 1:41 AM Samir K Mahajan < >>>> samirkmahajan1...@gmail.com> wrote: >>>> >>>>> Thanks to all of you for your kind response. Indeed, it is a >>>>> great learning experience. Yes, econometrics books too create models for >>>>> prediction, and programming really makes things better in a complex >>>>> world. My understanding is that machine learning does depend on >>>>> econometrics too. >>>>> >>>>> My Regards, >>>>> >>>>> Samir K Mahajan >>>>> >>>>> On Fri, Aug 13, 2021 at 1:21 AM Sebastian Raschka < >>>>> m...@sebastianraschka.com> wrote: >>>>> >>>>>> The R2 function in scikit-learn works fine. A negative means that the >>>>>> regression model fits the data worse than a horizontal line representing >>>>>> the sample mean. E.g. you usually get that if you are overfitting the >>>>>> training set a lot and then apply that model to the test set. The >>>>>> econometrics book probably didn't cover applying a model to an >>>>>> independent >>>>>> data or test set, hence the [0, 1] suggestion. >>>>>> >>>>>> Cheers, >>>>>> Sebastian >>>>>> >>>>>> >>>>>> On Aug 12, 2021, 2:20 PM -0500, Samir K Mahajan < >>>>>> samirkmahajan1...@gmail.com>, wrote: >>>>>> >>>>>> >>>>>> Dear Christophe Pallier, Reshama Saikh and Tromek Drabas, >>>>>> Thank you for your kind response. Fair enough. I go with you R2 is >>>>>> not a square. However, if you open any book of econometrics, it says >>>>>> R2 >>>>>> is a ratio that lies between 0 and 1. *This is the constraint.* >>>>>> It measures the proportion or percentage of the total variation in >>>>>> response variable (Y) explained by the regressors (Xs) in the model . >>>>>> Remaining proportion of variation in Y, if any, is explained by the >>>>>> residual term(u) Now, sklearn.matrics. metrics.r2_score gives me a >>>>>> negative >>>>>> value lying on a linear scale (-5.763335245921777). This negative >>>>>> value breaks the *constraint.* I just want to highlight that. I >>>>>> think it needs to be corrected. Rest is up to you . >>>>>> >>>>>> I find that Reshama Saikh is hurt by my email. I am really sorry >>>>>> for that. Please note I never undermine your capabilities and >>>>>> initiatives. >>>>>> You are great people doing great jobs. I realise that I should have been >>>>>> more sensible. >>>>>> >>>>>> My regards to all of you. >>>>>> >>>>>> Samir K Mahajan >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> On Thu, Aug 12, 2021 at 12:02 PM Christophe Pallier < >>>>>> christo...@pallier.org> wrote: >>>>>> >>>>>>> Simple: despite its name R2 is not a square. Look up its definition. >>>>>>> >>>>>>> On Wed, 11 Aug 2021, 21:17 Samir K Mahajan, < >>>>>>> samirkmahajan1...@gmail.com> wrote: >>>>>>> >>>>>>>> Dear All, >>>>>>>> I am amazed to find negative values of sklearn.metrics.r2_score >>>>>>>> and sklearn.metrics.explained_variance_score in a model ( cross >>>>>>>> validation >>>>>>>> of OLS regression model) >>>>>>>> However, what amuses me more is seeing you justifying negative >>>>>>>> 'sklearn.metrics.r2_score ' in your documentation. This does not >>>>>>>> make sense to me . Please justify to me how squared values are >>>>>>>> negative. >>>>>>>> >>>>>>>> Regards, >>>>>>>> Samir K Mahajan. >>>>>>>> >>>>>>>> _______________________________________________ >>>>>>>> scikit-learn mailing list >>>>>>>> scikit-learn@python.org >>>>>>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>>>>>> >>>>>>> _______________________________________________ >>>>>>> scikit-learn mailing list >>>>>>> scikit-learn@python.org >>>>>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>>>>> >>>>>> _______________________________________________ >>>>>> scikit-learn mailing list >>>>>> scikit-learn@python.org >>>>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>>>> >>>>>> _______________________________________________ >>>>>> scikit-learn mailing list >>>>>> scikit-learn@python.org >>>>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>>>> >>>>> _______________________________________________ >>>> scikit-learn mailing list >>>> scikit-learn@python.org >>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>> >>> _______________________________________________ >>> scikit-learn mailing list >>> scikit-learn@python.org >>> https://mail.python.org/mailman/listinfo/scikit-learn >>> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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