I don't want to talk too much, but I have read that there are some
goals/tests for which a which a normal data distribution is not
important, and others for which it very definitely is. And my
suggestion would be, if a book did exist: put that and similar
information on page 1 of every chapter, in a small heads-up box. TMN

On Fri, May 2, 2014 at 11:58 AM, Narandra Dashora <narandrad@gmail.com> wrote:
> If it is not undue intervention as an humble student of Econometrics ( I am
> 70 years old ) I feel The various assumptions about  Normal Distribution are
> hardly met in real  life data. If one has sound theory yest there is no co
> integration of data or unit root is present then there are two golden ways.
> A. Increase the sample size
> B. Reexamine the theory
> Incidentally I may   comment that the concept of spurious regression has
> been oversold
>
>
> On Fri, May 2, 2014 at 8:54 AM, Tim Nall <tnall.ling@gmail.com> wrote:
>>
>> All,
>>
>> Again please refer to previous disclaimers about the fact that I
>> certainly and truly have no idea what I am talking about w. respect to
>> statistics, and perhaps with respect to other issues as well.
>>
>> In an earlier post, someone or other expressed a concern that gretl
>> might be  perceived as a toy program that is purely for educational
>> purposes, and not for serious research. I have no idea whether or not
>> that's the case, but I would suggest that focusing on pushing gretl as
>> a serious tool is not the only path toward your ultimate goal. Me
>> personally, I would recast a perceived shortcoming as a serious
>> strength: push it as an educational tool. The way to do so, as I
>> mentioned earlier, is by writing a book. The book should be
>> data-centric and outcome-centric. That is, the traditional teaching
>> approach would be to think, "I have x number of statistical topics to
>> cover, and they can be ranked in terms of difficulty and inheritance
>> of concepts from one another, so I will present them according to that
>> rank. I will discuss the math and theory first, and perhaps (or
>> perhaps not) tack on a skimpy example in the end." This is a
>> forest-for-the-trees approach IMHO. Me personally, i would approach
>> the book as "You have (this) type of data, and you want (this) type of
>> outcome, so you should use (this) type of model, unless your data has
>> (these) conditions, and the way to deal with (these) conditions is
>> (chapter)."
>>
>> I can truly embarrass myself here by offering my own recent exp. as an
>> example: in my very modest research, a set of 10 OLS regressions (on
>> time series data) that were truly beautiful and perfect in every way
>> (they conformed very, very precisely to my initial set of hypotheses)
>> sat on the pages of my document for months before I realized that the
>> results were spurious due to non-stationary data. [Econometricians and
>> statisticians can politely refrain from giggling.] So ch. 1 of your
>> book, rather than immediately presenting the nuts and bolts of OLS,
>> could first present the same sort of case (very quickly). And so on.
>> Make top-level ideas (such as which problems to check for before
>> considering any given approach) *very* easy to find at a glance. Got
>> math? It goes in appendices.
>>
>> And here's the point: you would want your book to help gretl catch on
>> as an educational tool, but sprinkled throughout the book you could
>> also mention (w. brief details) its ability to do serious research. No
>> harm done. Then if it catches on in the former context, eventually
>> (lag a couple years) people will pick up on the latter idea -- also
>> realizing, hey, you know, it's free.
>>
>> So that's all I have to say. Thank you for your patience. TMN
>> _______________________________________________
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>
>
>
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--
Best regards,
Timothy M. Nall
Assistant Professor
National Quemoy University
Kinmen, Taiwan

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