Econometrics in Panel data?

[DeepBlue]

Hi all,

I am new to Python. Just wondering can Python able to do econometric
regression in either Time-series or pooled (panel) data? As well as test
for hetero, autocorrelation, or endogeneity?
Thank you!

[beliavsky]

DeepBlue wrote:

Hi all,

I am new to Python. Just wondering can Python able to do econometric
regression in either Time-series or pooled (panel) data? As well as test
for hetero, autocorrelation, or endogeneity?
Thank you!

NumPy can do linear regression, and one can certainly program any
statistical algorithm in it. For the fairly advanced statistical
methods you are interested in, however, it may be easiest to call R
from Python using the methods described at
http://www.omegahat.org/RSPython/index.html .

[DeepBlue]

so are you saying that Python is not an appropriate language for doing
econometrics stuff?
Dennis Lee Bieber wrote:

On Tue, 09 May 2006 05:58:10 +0800, DeepBlue <12*@321.com> declaimed the
following in comp.lang.python:

Hi all,

I am new to Python. Just wondering can Python able to do econometric
regression in either Time-series or pooled (panel) data? As well as test
for hetero, autocorrelation, or endogeneity?

Can you do such in FORTRAN, COBOL, SNOBOL, APL, C, Matlab, Maple,
Excel, Turing Machine? Most likely...

Is there a pre-built library to compute such? No idea...

[Cameron Laird]

In article <44**********************@per-qv1-newsreader-01.iinet.net.au>,
DeepBlue <12*@321.com> wrote:

so are you saying that Python is not an appropriate language for doing
econometrics stuff?
Dennis Lee Bieber wrote:

On Tue, 09 May 2006 05:58:10 +0800, DeepBlue <12*@321.com> declaimed the
following in comp.lang.python:

Hi all,

I am new to Python. Just wondering can Python able to do econometric
regression in either Time-series or pooled (panel) data? As well as test
for hetero, autocorrelation, or endogeneity?

Can you do such in FORTRAN, COBOL, SNOBOL, APL, C, Matlab, Maple,
Excel, Turing Machine? Most likely...

Is there a pre-built library to compute such? No idea...

[Robert Kern]

DeepBlue wrote:

so are you saying that Python is not an appropriate language for doing
econometrics stuff?

Alan Isaac certainly thinks it is appropriate and lists many Python resources
for econometrics:

http://www.american.edu/econ/pytrix/pytrix.htm

You may also want to look at QuantLib, which has a Python interface via SWIG.

http://www.quantlib.org

--
Robert Kern

"I have come to believe that the whole world is an enigma, a harmless enigma
that is made terrible by our own mad attempt to interpret it as though it had
an underlying truth."
-- Umberto Eco

[Cameron Laird]

In article <mu************@lairds.us>, I counseled:

In article <44**********************@per-qv1-newsreader-01.iinet.net.au>,
DeepBlue <12*@321.com> wrote:

so are you saying that Python is not an appropriate language for doing
econometrics stuff?
Dennis Lee Bieber wrote:

On Tue, 09 May 2006 05:58:10 +0800, DeepBlue <12*@321.com> declaimed the
following in comp.lang.python:

Hi all,

I am new to Python. Just wondering can Python able to do econometric
regression in either Time-series or pooled (panel) data? As well as test
for hetero, autocorrelation, or endogeneity?

[beliavsky]

Cameron Laird wrote:

In article <mu************@lairds.us>, I counseled:

In article <44**********************@per-qv1-newsreader-01.iinet.net.au>,
DeepBlue <12*@321.com> wrote:

so are you saying that Python is not an appropriate language for doing
econometrics stuff?
Dennis Lee Bieber wrote:
On Tue, 09 May 2006 05:58:10 +0800, DeepBlue <12*@321.com> declaimed the
following in comp.lang.python:

> Hi all,
>
> I am new to Python. Just wondering can Python able to do econometric
> regression in either Time-series or pooled (panel) data? As well as test
> for hetero, autocorrelation, or endogeneity?

.
.
.

There is not, however, a readily-accessible library targeted
for this sort of work. If I had the opportunity to work in
econometrics now, I'd think seriously about R, Lisp, and
Mathematica, and see what's available among the functional
languages, along with Python.

Smalltalk, too; I'd throw it in the mix. Much serious econometrics
has been done with Fortran, but I have no enthusiasm for pursuing
that direction, mostly because I think too much of the computing
world is going in a different one.

There are many statistics packages and programming languages used in
econometrics and in general, so most of the computing world is going in
a different
"direction", no matter which package or language you choose.

Enough programmers still use Fortran that major hardware vendors such
as Intel, IBM, and Sun are actively maintaining their Fortran 95
compilers and adding features from Fortran 2003. G95 is free, available
almost everywhere that gcc is, and good enough for production use IMO.

The recent book
http://methcenter.psu.edu/newbooks/f...k/thebook.html
Developing Statistical Software in Fortran 95
by David R. Lemmon and Joseph L. Schafer
Spriger (2005)

discusses how to build statistical software components in Fortran that
can be used in statistical packages.

The IMSL and NAG software libraries have extensive statistical
functionality and are available in Fortran, among other languages.

It is important for a programming language used for econometrics to
conveniently handle multidimensional arrays, and here Fortran outshines
C, C++, and Java (NumPy is good, though).

I am a quantitative financial analyst who implements econometrics
algorithms. Data sets are getting bigger -- use of intraday data is now
common -- and the CPU time for many algorithms scales as the N^2 or
N^3, where N is the number of observations. Speed still matters.

[N/A]

Oh! I think I should stop wasting time to learn Python to do my
econometric algorithms. >_<
be*******@aol.com wrote:

Cameron Laird wrote:

In article <mu************@lairds.us>, I counseled:

In article <44**********************@per-qv1-newsreader-01.iinet.net.au>,
DeepBlue <12*@321.com> wrote:
so are you saying that Python is not an appropriate language for doing
econometrics stuff?
Dennis Lee Bieber wrote:
> On Tue, 09 May 2006 05:58:10 +0800, DeepBlue <12*@321.com> declaimed the
> following in comp.lang.python:
>
>> Hi all,
>>
>> I am new to Python. Just wondering can Python able to do econometric
>> regression in either Time-series or pooled (panel) data? As well as test
>> for hetero, autocorrelation, or endogeneity?

.
.
.

There is not, however, a readily-accessible library targeted
for this sort of work. If I had the opportunity to work in
econometrics now, I'd think seriously about R, Lisp, and
Mathematica, and see what's available among the functional
languages, along with Python.

Smalltalk, too; I'd throw it in the mix. Much serious econometrics
has been done with Fortran, but I have no enthusiasm for pursuing
that direction, mostly because I think too much of the computing
world is going in a different one.

There are many statistics packages and programming languages used in
econometrics and in general, so most of the computing world is going in
a different
"direction", no matter which package or language you choose.

Enough programmers still use Fortran that major hardware vendors such
as Intel, IBM, and Sun are actively maintaining their Fortran 95
compilers and adding features from Fortran 2003. G95 is free, available
almost everywhere that gcc is, and good enough for production use IMO.

The recent book
http://methcenter.psu.edu/newbooks/f...k/thebook.html
Developing Statistical Software in Fortran 95
by David R. Lemmon and Joseph L. Schafer
Spriger (2005)

discusses how to build statistical software components in Fortran that
can be used in statistical packages.

The IMSL and NAG software libraries have extensive statistical
functionality and are available in Fortran, among other languages.

It is important for a programming language used for econometrics to
conveniently handle multidimensional arrays, and here Fortran outshines
C, C++, and Java (NumPy is good, though).

I am a quantitative financial analyst who implements econometrics
algorithms. Data sets are getting bigger -- use of intraday data is now
common -- and the CPU time for many algorithms scales as the N^2 or
N^3, where N is the number of observations. Speed still matters.