[RsR] robust regression and fixed effects models

[Rand R Wilcox]

You might also look at the Elsevier book  Robust Estimation and Hypothesis Testing. It provides some additional options that might help. 
A couple of quick suggestions. Even when using a robust estimator with a high breakdown point, check on what happens when leverage points (outliers among the independent variables) are eliminated. Second, consider a method that allows a heteroscedastic error term.
Third, take a look at robust smoothers. Illustrations on how to do this are in the book. My experience when working with various research teams is that they can be very important. 

Hope this helps.

Rand

Rand Wilcox
Professor
University of Southern California
3620 McClintock Ave
Los Angeles, CA  90089-1061

For information about Understanding and Applying Basic Statistical Methods Using R, Wiley (in preparation), and other recent books, go to
Dornsife.usc.edu/cf/labs/wilcox/wilcox-faculty-display.cfm
and click on books.
Or go to https://www.amazon.com/author/randwilcox
​

________________________________________
From: R-SIG-Robust <r-sig-robust-bounces using r-project.org> on behalf of Martin Maechler <maechler using stat.math.ethz.ch>
Sent: Friday, June 12, 2015 2:53 AM
To: michael westphal
Cc: r-sig-robust using r-project.org
Subject: Re: [RsR] robust regression and fixed effects models

>>>>> michael westphal via R-SIG-Robust <r-sig-robust using r-project.org>
>>>>>     on Wed, 10 Jun 2015 14:17:57 +0000 writes:

    > Hello:
    > I am using R 3.0.2.

so you really should upgrade {unless you meant 3.2.0}... at
least in a few days when  R 3.2.1 is released.


    > I have panel data on countries' renewable energy net generation (and installed capacity) over time.  I am regressing these dependent variables on various socioeconomic variables, as well as binary policy variables.  I have have done basic OLS, but I wanted to explore both fixed effects models, as there are likely significant country effects and robust regression, as Q-Q plots indicate that there are some strong outliers.  This might be a question of apples and oranges, but how do I compare the goodness of fit of the fixed effects models with the robust regression models?
    > Any help would be appreciated.

Package  robustbase  which has function  lmrob()  with many good
and modern options for robust regression
*also* has a 'Suggests: fit.models' in its own description file,

because the package 'fit.models' with its function fit.models()
tries to take fits of basically the same model and
produce "comparison output" from that.

It's quite useful in situations like yours,
and I plan to add an example of its use to the 'robustbase'
package documentation.



    > [[alternative HTML version deleted]]

  ((Because you used "HTML" aka "rich text" / "formatted text"
    instead of simple plain text, your message ends up looking so
    messy as above ...))



Martin Maechler,
ETH Zurich

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