I have been lurking for a while, but I think this is my first post. I have 
tried to address your question from the perspective of an undergraduate 
student in Economics who has gone one to do some economic forecasting, but 
has made no contributions to theoretical economics. 
I have no experience with Matlab or Octave -- so I can't compare.

> 3. Which language do you think is better for students' further
> development?  We would like to equip them with something they can use
> later on in their career even if they don't become theoretical
> economists (very few undergraduate students do that).

Since you mention "undergraduate students" I assume you mean a course in 
content similar to 
Alpha Chiang's classic "Mathematical Methods in Economics."

http://www.mcgraw-hill.co.uk/html/0070662193.html

I believe you will find all you need in R, but I doubt you will find it in 
one place!

The relevant techniques (see link to Alpha Chiang's textbook above) may be 
spread across many packages. 

For example, just recently  there was a post to this list about how to 
estimate a nonlinear production function, both Cobb-Douglas and CES. 
Klein's Model I is also implemented in another R package. Numerous 
techniques for estimation, optimization and simulation, including Monte 
Carlo and resampling methods are all available in various R packages.

Fortunately, your earlier post indicates that you are well acquainted with 
the R Help system. 

As far as "something they can use later in their careers." R has at least 
two advantages:

1. As mentioned R is open source which is beneficial to their student 
budget constraints. Institutions also have budget
constraints and new graduates starting out on their careers can download R 
in any institution almost anywhere in the world 
where there is broadband Internet access.

2. The R mailing list is cross-disciplinary. Students can see how 
mathematics and statistics are applied in the cutting edge of a
variety of fields. I have personally found the posts on geostatistics and 
biostatistics fascinating. If I were an undergraduate starting
out -- I would probably want to try biostatistics (or bioinformatics or 
whatever its called).

Are you interested in mathematical methods applied to classical 
macroeconomic models or newer financial techniques (portfolio & options)?

If you want to demonstrate advanced econometric techniques applied to 
macroeconomics  -- a very sadistic suggestion - for graduate students -- 
would be to port Professor Roy Fair's US, MC or MCU "Fairmodels" to R. 
This would cover numerical methods involved in both estimation and 
simulation -- grounded in a very applied context. I have contemplated 
doing that myself (on my own time) but sanity prevailed (I'll stick with 
OLS) -- but it would be a great learning experience... (so would climbing 
the Himalayas).

see:
http://fairmodel.econ.yale.edu/main2.htm
and
http://fairmodel.econ.yale.edu/fp/fp.htm

If you want to demonstrate newer financial techniques (portfolios & 
options) you might look at the MITPress book, by Professor Simon Benninga 
"Financial Modeling" (an earlier book by this author was titled "Numerical 
Techniques in Finance") -- which incidentally uses MS Excel. The author 
uses matrix techniques in Excel which are rarely, if ever, covered in 
general purpose Excel books (but are very relevant to your course).

see:
http://mitpress.mit.edu/catalog/item/default.asp?sid=4296516E-E57D-4EDD-9094-17E4E86330AB&ttype=2&tid=3433
and
http://finance.wharton.upenn.edu/~benninga/

of course these methods can (and have) been implemented in R as well 
(thanks Dirk!).

If you were teaching only accounting or business students, "Business 
Analysis with Microsoft Excel" by Conrad Carlberg might be useful -- it 
has downloadable Excel add-ins for Arima and statistical process control. 
The choice and presentation of topics is very appropriate for a business 
setting.

For an undergraduate or an MBA student, the learning curve for anything 
beyond MS Excel (or Gnumeric) is steep. For this reason, you might want to 
look at John Fox's GUI frontend to R -- to ease the shock.  Over twenty 
years later I still recall estimating exactly one equation in SPSS -- and 
then only after careful spoon-feeding. I accepted the JCL on the first few 
punch cards on blind faith. Simulating, the Penn-FRB-Wharton model (again 
on punch cards) as an undergraduate was an exciting, but baffling 
experience. But, then my first job out of college I got to regress and 
simulate using XSIM (a wonderful command line econometric language).

>From a strictly economic perspective, I would look (Google) at  Gretl, G 
or the Fair-Parkes program (again Fairmodel), but since your objective 
seems to be to go under the hood and look at the underlying mathematical 
techniques -- R is a good, though challenging choice. Gretl is 
open-source. G and Fair-Parkes are freely downloadable, but not, strictly 
speaking, GPL.

As for R documentation, I keep PDFs of "An Introduction to R" and Julian 
Faraway's "Practical Regression and Anova using R" on my Windows desktop 
-- hint the appendices are the best place to start!  I also keep paper 
copies of John Fox's "Companion to Applied Regression" and Peter 
Dalgaard's "Introductory Statistics with R" handy.

One disadvantage of R is that, although Professor Ripley has done heroic 
work in debugging R dates; R dates are a pain and are often skipped as in 
John Fox's examples.  Coming from a background in XSIM/Troll where dates 
were intrinsic to the times series data structure we used -- I find R 
dates very painful and not very useful. But, then we only used OLS in XSIM 
-- not n-stage least squares or full-information maximum likelihood 
(FIML). And we would never, ever attempt analysis of genomes in XSIM!

The greatest advantage of R is the cross-pollenation provided by the 
mailing list. It is easy to guess that in the near future, numerous 
mathematical and statistical techniques will be developed to deal with the 
difficult challenges of the study of genomics (genes) and protonomics 
(proteins). The R mailing list will be a great window on those 
developments.

Jim Callahan
Management, Budget & Accounting
City of Orlando
(407) 246-3039 office
(407) 234-3744 cell phone


> Message: 24
> Date: Sun, 25 Apr 2004 11:08:09 +0200
> From: Tamas Papp <tpapp@axelero.hu>
> Subject: [R] R vs Matlab: which is more "programmer friendly"?
> To: R-help mailing list <r-help@stat.math.ethz.ch>
> Message-ID: <20040425090809.GA704@localhost>
> Content-Type: text/plain; charset=iso-8859-1
> 
> Hi,
> 
> The department of economics at our university (Budapest) is planning a
> course on numerical methods in economics.  They are trying to decide
> which software to use for that, and I would like to advocate R.  The
> other alternative is Matlab.
> 
> I have found comparisons in terms of computational time for matrix
> algebra, but I don't think that is relevant: the bottleneck for
> economists is usually the programmer's time: if it takes a couple of
> hours to write something that is run only a few times, one should not
> care whether it runs in 2 or 2.1 minutes...
> 
> I am an economist, and I have used Octave, but only until I found R.
> So I am not in a position to evaluate Matlab vs R.  I would be
> grateful if somebody could compare R to Matlab, especially regarding
> the following:
> 
> 1. How "smart" the language is.  R appears to be a nice functional
> programming language, is Matlab comparable?  Last time I used Octave,
> it seemed to be little more than syntactic sugar on some C/Fortran
> libraries.  It appears to me that using R gradually pushes people
> towards better programming habits, but I may be biased (I am a Scheme
> lover).
> 
> 2. Learning curve.  If somebody could share his/her experience on
> using R or Matlab or both in the classrom, how students take to it.
> 
> 3. Which language do you think is better for students' further
> development?  We would like to equip them with something they can use
> later on in their career even if they don't become theoretical
> economists (very few undergraduate students do that).
> 
> 4. How flexible are these languages when developing new
> applications/functions?  Very few of the problems I encounter have a
> ready-made solution in a toolbox/library.
> 
> Thanks,
> 
> Tamas
> 
> -- 
> Tamás K. Papp
> E-mail: tpapp@axelero.hu
> Please try to send only (latin-2) plain text, not HTML or other garbage.
> 
> 
> 
> ------------------------------
> 
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> End of R-help Digest, Vol 14, Issue 25
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