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RE: st: Factor models in panel data (was: Factor models)


From   kokootchke <kokootchke@hotmail.com>
To   statalist <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Factor models in panel data (was: Factor models)
Date   Wed, 14 Oct 2009 18:12:39 -0400

  <5d309276347f2.4a6a72bd@mail.nyu.edu>

   <COL111-W615BFF169F6A53D1717D1FD1100@phx.gbl> 

  <6060c8877a3a.4a748757@mail.nyu.edu>
 

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> From: kokootchke@hotmail.com
> To: statalist@hsphsun2.harvard.edu
> Subject: RE: st: Factor models in panel data   (was: Factor models)
> Date: Mon=2C 3 Aug 2009 10:20:48 -0400
>=20
> Thank you=2C Bob. I think the article you suggested is:
>=20
> Koopman and Lucas (2008)=2C "A Non-Gaussian Panel Time Series Model for
> Estimating and Decomposing Default Risk"
>=20
> I am reading it now and it looks useful! (and even if it's not=2C it seem=
s like I should be aware of it!) Thank you very much.
>=20
> Adrian
>=20
>=20
> ----------------------------------------
>> From: bob.yaffee@nyu.edu
>> To: statalist@hsphsun2.harvard.edu
>> Date: Sat=2C 1 Aug 2009 18:20:07 -0400
>> Subject: Re: st: Factor models in panel data (was: Factor models)
>>
>> Adrian=2C
>> I read an article in the Journal of Business and Economic Statistics a f=
ew months ago
>> that suggests that State Space models can handle panel data.
>> That might be of some help.
>> You're quite welcome=2C
>> Bob
>>
>>
>> Robert A. Yaffee=2C Ph.D.
>> Research Professor
>> Silver School of Social Work
>> New York University
>>
>> Biosketch: http://homepages.nyu.edu/~ray1/Biosketch2009.pdf
>>
>> CV: http://homepages.nyu.edu/~ray1/vita.pdf
>>
>> ----- Original Message -----
>> From: kokootchke=20
>> Date: Friday=2C July 31=2C 2009 2:58 pm
>> Subject: st: Factor models in panel data (was: Factor models)
>> To: statalist=20
>>
>>
>>> First of all=2C thanks to Bob for all the references on dynamic factor =
models.
>>>
>>> I think that some of these are useful for my purposes but not
>>> entirely=2C mainly due to the panel structure of my data.
>>>
>>> As I mentioned earlier=2C I have data on domestic macroeconomic
>>> variables for 40+ countries over time (e.g.=2C GDP growth=2C dollar
>>> reserves=2C inflation=2C etc.) AND data on global financial conditions
>>> that are common to all countries in my data set at any given point in
>>> time (e.g.=2C the level of the U.S. interest rate=2C volatility in the
>>> EMBI index=2C volatility of the VIX index=2C etc.).
>>>
>>> The problems I see with the implementation of a standard factor model
>>> (as implemented by -factor- in Stata) are the following:
>>>
>>> 1. I think that=2C as it is=2C this command doesn't take into account t=
he
>>> panel structure of the database=2C does it? For instance=2C if my data =
has
>>> 80 quarters per country and 40 countries and it's sorted by country
>>> and time period=2C then the change between observation 79 and 80 for an=
y
>>> given domestic macroeconomic variable is going to be very different
>>> than the change between observation 80 and 81=2C because the former two
>>> correspond to periods 79 and 80 of country 1=2C while the latter two
>>> correspond to period 80 of country 1 and period 1 of country 2.
>>>
>>> 2. While the domestic macroeconomic variables change by country and by
>>> time period (e.g.=2C each country has different inflation rates in 1995
>>> Q2)=2C global financial conditions are the same for all countries at a
>>> given time period (e.g.=2C the U.S. interest rate is the same for ALL
>>> countries in my data base in 1995 Q2). So=2C can I still put these
>>> common variables together in the same database where I have my
>>> domestic variables before I do the factor analysis?
>>>
>>> 3. Some of my variables as I use them in my regression model are in
>>> logs (e.g.=2C log of the U.S. interest rate)=2C or "scaled" appropriate=
ly
>>> to compare them across countries (e.g.=2C reserves/imports=2C external
>>> debt/GDP=2C etc.)? Should I leave them like this in my factor analysis=
=2C
>>> or should I just use the actual variables I'm interested in (e.g.=2C
>>> U.S. interest rates in levels=2C reserves and external debt without
>>> scaling by imports or GDP=2C respectively=2C etc.)?
>>>
>>> Thank you very much once again.
>>>
>>> Adrian
>>>
>>>
>>> ----------------------------------------
>>>> From: bob.yaffee@nyu.edu
>>>> To: statalist@hsphsun2.harvard.edu
>>>> Date: Sat=2C 25 Jul 2009 02:49:33 -0400
>>>> Subject: Re: st: Factor models
>>>>
>>>> Adrian=2C
>>>> There is a substantial literature on dynamic factor models. Mario
>>> Forni and Lucrezia Richlin have written on this in 1996 and 1998 . In
>>> September 2003=2C Mario Forni=2C Marc Hallan=2C Marco Lippi=2C and
>>>> Lucrezia Richline published "The Generalized Dynamic Factor Model: One=
-sided
>>>> Estimation and Forecasting=2C" as an LEM working paper (2003/13).
>>> Marco Lippi and Daniel Thornton
>>>> have written a working paper for the St. Louis Federal Reserve bank
>>> (WP2004-013a) in
>>>> 2004=2C entitled "A dynamic factor analysis of the response of U.S.
>>> interest to News."
>>>> Stock and Watson in 1989=2C 1991=2C and later have worked on this
>>> subject. In 2005 they
>>>> published=2C "Implications of Dynamic Factor models for VAR analysis=
=2C"
>>> in which they have dealt with dynamic factors and model identification
>>> with long and short run restrictions in VAR form. Tao Chen=2C Elaine
>>> Martin=2C and Gary Montague have written an article in Computational
>>> Statistics and Data Analysis (2009) v. 59 entitled=2C "Robust
>>> probabalistic PCA with missing data and contribution for outlier
>>> detection." Rangan Gupta and Alain Kabundi in 2008 (Journal of
>>> Economic Literature) have written "A Dynamic Factor Analysis for
>>> Forecasting Macroeconomic Variables in South Africa." Some
>>>> time ago=2C Allessandro Federici and Andrea Mazzitelli in 2005 at an
>>> Italian Stata User's Group presented "Dynamic Factor Analysis with
>>> Stata=2C" based on the Coppi and Zannella(1978) and later work.
>>>> In 2004 Ben Bernanke has written on factor analysis with BVAR
>>> models. Siem Jan Koopman has also incorporated PCA in his state space
>>> models. James Hamilton=2C in his 1994 classic=2C Time Series Analysis=
=2C has
>>> shown how state space models lend themselves to dynamic factor analysis=
.
>>>> And these are small sample of the articles that have emerged on this
>>> subject. Andrew Harvey in 1989 in "Forecasting=2C Structural time serie=
s
>>> models=2C and the Kalman Filter" has also shown on these factors are th=
e
>>> unobserved signal components extracted from the data generating process=
.
>>>> These are some of the articles on the subject.
>>>> Regards=2C
>>>> Bob Yaffee
>>>>
>>>>
>>>> Robert A. Yaffee=2C Ph.D.
>>>> Research Professor
>>>> Silver School of Social Work
>>>> New York University
>>>>
>>>> Biosketch: http://homepages.nyu.edu/~ray1/Biosketch2009.pdf
>>>>
>>>> CV: http://homepages.nyu.edu/~ray1/vita.pdf
>>>>
>>>> ----- Original Message -----
>>>> From: kokootchke
>>>> Date: Friday=2C July 24=2C 2009 7:49 pm
>>>> Subject: st: Factor models
>>>> To: statalist
>>>>
>>>>
>>>>> Hi everyone.
>>>>>
>>>>> I would like to know if anyone could provide some references to
>>>>> factor models (latent factor models=2C dynamic factor models=2C etc.)=
.
>>>>>
>>>>> I am studying the effects of domestic macroeconomic variables vs.
>>>>> global economic conditions on bond spreads and I believe that I could
>>>>> use factor models to improve my analyses.
>>>>>
>>>>> I have never used these models before and have found some papers by
>>>>> Stock and Watson=2C Lippi et al.=2C etc... which are useful... but I =
would
>>>>> like to read something a bit easier to get my feet wet=2C and then st=
art
>>>>> building up from there.
>>>>>
>>>>> Thank you very much!
>>>>>
>>>>> Best regards=2C
>>>>> Adrian
>>>>>
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