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From |
Austin Nichols <[email protected]> |

To |
[email protected] |

Subject |
Re: st: Inverse hyperbolic sine transformation |

Date |
Fri, 29 Jan 2010 14:08:22 -0500 |

Stephane Mahuteau <[email protected]>: If you are willing to assume homoskedastic errors, you can get consistent estimates of the predicted outcomes y conditional on X using the "smearing" estimator proposed by Duan (http://www.jstor.org/stable/2288126). If you are doing this because you have a nonnegative skewed dependent variable, you are better off with -poisson- or another model in that family, which will give you much better estimates of the predicted outcomes conditional on X, directly, with no additional work associated with back-transformation. You can also get estimates of y conditional on X and y>0. What does your model look like? What are the X and y variables? On Fri, Jan 29, 2010 at 12:06 PM, Maarten buis <[email protected]> wrote: > > --- On Fri, 29/1/10, Stephane Mahuteau <[email protected]> wrote: >> I estimated a double hurdle model using the Inverse >> Hyperbolic sine transformation to my dependent variable in >> the second hurdle. From these results I'd like to compute >> the expected values of this dependent variable y (given >> y>0) rather than using the expected values of the >> transformed variable. From what I read in the Cameron & >> Trivedi "Microeconometrics using stata" applied to some >> other transformations on non linear models, it looks like >> getting the expected values of the variable y from the >> estimated values of the transformed y would be more complex >> than just inversing the transformation. Would anybody have >> any idea on how I can perform this? Is it ok to just invert >> the transformation? > > The problem is that that is a non-linear transformation. You > can easily see what happens in a simple model: predicting > the variable by its mean: > > *-------- begin example --------- > sysuse nlsw88, clear > gen asinh_w = asinh(wage) > sum asinh_w > di sinh(r(mean)) > sum wage > *-------- end example ------------ > > You can see here that first transforming the variable and > than backtransforming the mean, will not result in the mean > of the original variable. > > Probably the easiest solution to implement, but hard to > explain to your audience, is that if you log transform > the dependent variable you could interpret the > backtransformed predicted values as Geometric means. > Alternatively, you can include the transformation as a > link function in the likelihood, as happens in -glm-like > models. Then you are modeling the mean directly, so the > predicted values do represent the conditional means. > > Hope this helps, > Maarten * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Inverse hyperbolic sine transformation***From:*Stephane Mahuteau <[email protected]>

**Re: st: Inverse hyperbolic sine transformation***From:*Maarten buis <[email protected]>

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