On 2020-07-09 07:15 -0700, Bert Gunter wrote:
> On Thu, Jul 9, 2020 at 3:09 AM Valerio Leone Sciabolazza
> wrote:
> >
> > When calculating a panel data
> > regression with multiple fixed
> > effects using the function felm()
> > from the lfe package, no constant
> > term (i.e. intercept) is
While you may get lucky here, your experience on SO indicates that you may
do better by contacting the package maintainer (?maintainer) and asking
him/her to refer you to appropriate references, as this sounds like a
statistical methodology query.
Bert Gunter
"The trouble with having an open mind
Dear list users,
When calculating a panel data regression with multiple fixed effects
using the function felm() from the lfe package, no constant term (i.e.
intercept) is generated in the summary results.
In an old post on stackoverflow [1], someone suggested that it is
possible to retrieve the va
I am using gam from the mgcv package to analyze a dataset with 24 entries :
ran f1 f2 y
1 30005 545
1 300010 1045
1 1 5 536
1 1 10 770
2 30005 842
2 300010 2042
2 1 5 615
2 1 10 1361
3 30005 328
3 300010 1028
li li gmail.com> writes:
>
> Dear all,
> For the data below, I would like to fit a model with common
> random slope and common random intercept as shown below. I am
> interested in obtaining separate fixed effect estimates (intercept
> and slope and corresponding hypothesis test) for each
Dear all,
For the data below, I would like to fit a model with common random
slope and common random intercept as shown below. I am interested in
obtaining
separate fixed effect estimates (intercept and slope and corresponding
hypothesis test)
for each method. Instead of performing the analys
Dear All,
I am trying to implement a regression with state-specific trends in R. I
can implement this in Stata with ease, but I am hoping to preserve my R
workflow. I suspect there is an "R formula trick" that I'm just not
understanding and I would be grateful to anyone who could help me
understan
do on your computer.
John Kane
Kingston ON Canada
> -Original Message-
> From: alexier...@hotmail.com
> Sent: Sun, 1 Nov 2015 18:40:36 +
> To: r-help@r-project.org
> Subject: [R] Fixed effects estimators doesn't drop or omit
>
> The title says it all, really. Fi
You might get better or quicker answers if you ask package-specific
questions
to the maintainer of the package
> maintainer("plm")
[1] "Yves Croissant "
or to the authors listed in
> packageDescription("plm")
Package: plm
Version: 1.4-0
Date: 2013-12-24
Title: Linear Models for Panel
> since the OLS and robust regressions have the same number of DFs, looking
> at the residual standard error is insightful.
Sadly not. The residual scale in a robust model is only partly indicative of
goodness of fit; robust models intentionally downweight outliers. Much of the
difference in
On Tue, May 19, 2015 at 1:23 PM, michael westphal via R-help
wrote:
You can't compare them (statistically -- you can of course draw
pictures). Note, from ?rlm:
" Note that the df.residual component is deliberately set to NA to
avoid inappropriate estimation of the residual scale from the residua
Hello:
I am using R 3.0.2.
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
I have an unbalanced panel of daily, county data that is naturally bounded at
zero so my intention is to use a tobit. I'm using tobit from the AER
package. There is cyclicality in the data for each pattern that I would like
to control for before I add my variables of interest.
I run the regression
rmula(paste(deparse(formula(formula)),"-1",sep=""))
>> X<-model.matrix(formula,data=data)
>> y<-model.response(model.frame(formula,data=data))
>> ## reduce index accordingly
>> names(index)<-row.names(data)
>> ind<-index[which(names(i
,i],index=ind,lambda=1)
> Â Â }
> Â My<-demean(y,index=ind,lambda=1)
>
> Â ## estimate within model
> Â femod<-lm(My~MX-1)
>
> Â return(femod)
> }
> ### end test function
>
>
> Best,
> Giovanni
>
> ### original message #
Dear Andrew,
Thanks for your suggestion.
I will indeed have a look at Allison's booklet...
Best,
On 7 February 2012 23:39, Andrew Miles wrote:
> Based on Paul Allison's booklet "Fixed Effect Regression Models" (2009), the
> FE model can be estimated by person-mean centering all of your variable
Best,
Giovanni
### original message #
------
Message: 28
Date: Tue, 07 Feb 2012 15:35:07 +0100
From: caribou...@gmx.fr
To: r-help@r-project.org
Subject: [R] fixed effects with clustered standard errors
Message-ID: <20120207143507.142...@gmx.com>
C
Based on Paul Allison's booklet "Fixed Effect Regression Models" (2009), the FE
model can be estimated by person-mean centering all of your variables (but not
the outcome), and then including a random intercept for each person. The
centering gives you the FE model estimates, and the random inte
Dear R-helpers,
First of all, sorry for those who have (eventually) already received that
request.
The mail has been bumped several times, so I am not sure the list has received
it... and I need help (if you have time)! ;-)
I have a very simple question and I really hope that someone could help
Dear R-helpers,
I have a very simple question and I really hope that someone could help me
I would like to estimate a simple fixed effect regression model with clustered
standard errors by individuals.
For those using Stata, the counterpart would be xtreg with the "fe" option, or
areg with the
On 2011-07-28 06:21, Luca Deckert wrote:
Dear R community,
I am trying to do my own fixed effects regression using the Within function
in PLM. I apply the Within function to all my pseries and then run OLS on
the transformed vectors using lm().
When I compare the results to those obtained via p
Dear R community,
I am trying to do my own fixed effects regression using the Within function
in PLM. I apply the Within function to all my pseries and then run OLS on
the transformed vectors using lm().
When I compare the results to those obtained via plm ("within"), the
estimates are not always
Hi all,
I am trying to do my own fixed effects regression using the Within function
in PLM. I apply the Within function to all my pseries and then run OLS on
the transformed vectors using lm().
When I compare the results to those obtained via plm ("within"), the
estimates are not always the same.
A cleaner and slightly more tested version is at
http://davidhughjones.blogspot.com/2011/07/honore-style-fixed-effects-estimators.html
David Hugh-Jones
Research Associate
CAGE, Department of Economics
University of Warwick
http://davidhughjones.googlepages.com
On 13 July 2011 15:33, David Hugh
True! Here's my attempt -- use at your own risk.
honore <- function (b, dataset, x1, x2) {
dxb <- (x2 - x1) %*% b
y1 <- # insert your y variable here
y2 <- # insert your y variable here
sum(
(pmax(y1, dxb) - pmax(y2, dxb) - dxb)^2 +
2*(y1 < dxb)*(dxb-y1)*y2 +
2*(y2
Not that I know of, but the paper says that they are easy to compute. If you
did, you could contribute the code.
Best,
Daniel
David Hugh-Jones-3 wrote:
>
> Hi all,
>
> Is there any code to run fixed effects Tobit models in the style of Honore
> (1992) in R?
> (The original Honore article is he
Hi all,
Is there any code to run fixed effects Tobit models in the style of Honore
(1992) in R?
(The original Honore article is here:
http://www.jstor.org/sici?sici=0012-9682%28199205%2960%3A3%3C533%3ATLALSE%3E2.0.CO%3B2-2)
Cheers
David
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_
---
Message: 91
Date: Mon, 24 May 2010 18:24:00 -0400
From: ivo welch
To: r-help
Subject: [R] Fixed Effects Estimations (in Panel Data)
Message-ID:
Content-Type: text/plain; charset=ISO-8859-1
dear readers---I struggled with how to do nice fixed-effects
regressions in
dear readers---I struggled with how to do nice fixed-effects
regressions in large economic samples for a while. Eventually, I
realized that nlme is not really what I needed (too complex), and all
I really wanted is the plm package. so, I thought I would share a
quick example.
sa
the regression.
-Original Message-
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of Roy Lowrance
Sent: Sunday, March 21, 2010 8:01 PM
To: r-help@r-project.org
Subject: [R] fixed effects regression
Hi All:
I am trying to move a model from Stata to R.
It
The 90,000 indicators are the interactions between 300 zip codes (= postal
codes) and 300 month indices.
- Roy
On Mon, Mar 22, 2010 at 5:56 AM, Petr PIKAL wrote:
> Hi
>
> r-help-boun...@r-project.org napsal dne 22.03.2010 01:01:00:
>
> > Hi All:
> >
> > I am trying to move a model from Stata to
Hi All:
I am trying to move a model from Stata to R.
It is a linear regression model with about 90,000 indicator variables.
What is the best approach to follow in R?
- Roy
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__
R-help@r-project.org mailing
: untested code.
Bill Venables.
Bill Venables
http://www.cmis.csiro.au/bill.venables/
-Original Message-
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of parkbomee
Sent: Saturday, 7 February 2009 1:57 PM
To: r-help@r-project.org
Subject: [R] fixed effec
Hi everyone,
I am running this fixed effects model in R.
The thing is, my own code and the canned routine keep returning different
estimates for the fixed effects factors and intercept.
(The other variables' estimates and R^2 are the same!!! weird!!)
I attach my own code, just in case.
I am pre
help.search('fixed effect') creates these matches. Does one of the do
what you want?
Help files with alias or concept or title matching 'fixed effect'
using fuzzy matching:
fixef(lme4)Extract Fixed Effects
lmer(lme4) Fit (Generalized) Linear Mixed-Effect
Hello everyone!
I would really appreciate it if someone knew where could I find the command
in R in order to run a fixed effects regression.
What format should my data have?
I have looked through the manual and I could not find anything
Thank you in advance,
Petros
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