4:52 p.m.
GRAPH OF TWO DENSITIES TOGETHER: Thanks for providing the older link.
Although the code there is to plot two densities /consecutively /from
left to right, while what I need to do is to /superimpose/ them - and
this I realize now has the problem of having two different abscissaes
series. Still, I learned something new about handling plots in Gretl.
CONSTANT IN LOG-LIKELIHOOD
The basic code *without the constant in the log-l *is (omitting the
initial part where OLS executes to obtain initial values)
<<
matrix Depv = {LWAGE}
matrix Regrs = {const, EXP, EXP2, WKS, OCC, IND, SOUTH, SMSA,
MS, FEM, UNION, ED, BLK}
matrix cVec = {c0,c1,c2,c3,c4,c5,c6,c7,c8,c9,c10,c11,c12}'
scalar v0 = 1
scalar v1 = 1
scalar v2 = 1
mle logl = check ? -ln(v) - 0.5*(e2hn/v)^2 + ln(cdf(D,l1/sqrt(1+l1^2),
e2hn/omega1, 0) - cdf(D,-l2/sqrt(1+l2^2), e2hn/omega2, 0)):NA
series e2hn = Depv - Regrs*cVec
scalar v = sqrt(v0^2 + v1^2 + v2^2)
scalar l1 = (v2/v1)*(v/v0)
scalar l2 = (v1/v2)*(v/v0)
scalar omega1 = (v*v0/v1)*sqrt(1+ (v2/v0)^2)
scalar omega2 = (v*v0/v2)*sqrt(1+ (v1/v0)^2)
scalar check = (v0>0) && (v1>0) && (v2>0)
params cVec v0 v1 v2
end mle --verbose
>
and gives final results
<<
--- FINAL VALUES:
loglikelihood = -447.517658694 (steplength = 8.38861e-017)
Parameters: 5.6103 0.029306 -0.00048463 0.0036368 -0.16393
0.083254
-0.058693 0.16568 0.093867 -0.32751 0.10612
0.056644
-0.18925 0.21983 0.26368 0.28759
Gradients: 7.1632e-005 -0.00019598 0.0051691 -0.00055942 5.6355e-005
2.3537e-006
4.7828e-005-7.9492e-006 2.2249e-005-2.2649e-006
2.5091e-005 -0.00029823
5.1958e-006 7.7593e-005 5.9452e-006-2.5091e-005 (norm
5.45e-003)
Tolerance = 1.81899e-012
Function evaluations: 397
Evaluations of gradient: 72
Model 3: ML, using observations 1-595
logl = check ? -ln(v) - 0.5*(e2hn/v)^2 + ln(cdf(D,l1/sqrt(1+l1^2),
e2hn/omega1, 0) - cdf(D,-l2/sqrt(1+l2^2), e2hn/omega2, 0)):NA
Standard errors based on Outer Products matrix
estimate std. error z p-value
----------------------------------------------------------
cVec[1] 5.61026 0.189973 29.53 1.12e-191 ***
cVec[2] 0.0293063 0.00650305 4.507 6.59e-06 ***
cVec[3] -0.000484630 0.000127917 -3.789 0.0002 ***
cVec[4] 0.00363680 0.00253677 1.434 0.1517
cVec[5] -0.163931 0.0372662 -4.399 1.09e-05 ***
cVec[6] 0.0832535 0.0305658 2.724 0.0065 ***
cVec[7] -0.0586933 0.0300906 -1.951 0.0511 *
cVec[8] 0.165683 0.0296335 5.591 2.26e-08 ***
cVec[9] 0.0938665 0.0469460 1.999 0.0456 **
cVec[10] -0.327510 0.0678567 -4.826 1.39e-06 ***
cVec[11] 0.106121 0.0335694 3.161 0.0016 ***
cVec[12] 0.0566442 0.00623447 9.086 1.03e-019 ***
cVec[13] -0.189253 0.0551030 -3.435 0.0006 ***
v0 0.219829 0.0951096 2.311 0.0208 **
v1 0.263683 0.111617 2.362 0.0182 **
v2 0.287589 0.103953 2.767 0.0057 ***
Log-likelihood -447.5177 Akaike criterion 927.0353
Schwarz criterion 997.2523 Hannan-Quinn 954.3796
>
----------------------------------------------------------
If I specify
mle logl = check ? *ln(4/sqrt(2/$pi))* - ln(v) etc
I get
<<
--- FINAL VALUES:
loglikelihood = 511.673340992 (steplength = 1.6384e-010)
Parameters: 5.6103 0.029306 -0.00048463 0.0036368 -0.16393
0.083254
-0.058694 0.16568 0.093868 -0.32751 0.10612
0.056644
-0.18925 0.21983 0.26368 0.28759
Gradients: -0.00013035 0.0013600 0.052876 -0.0098550 6.4448e-005
-0.00051251
0.00057035-8.1379e-006 -0.00036188 -0.00042025
0.00034582 -0.0013756
0.00053909 -0.0013437 0.00054025 -0.00083513 (norm
1.11e-002)
Tolerance = 1.81899e-012
Function evaluations: 493
Evaluations of gradient: 82
Model 3: ML, using observations 1-595
logl = check ? ln(4/sqrt(2/$pi)) -ln(v) - 0.5*(e2hn/v)^2 +
ln(cdf(D,l1/sqrt(1+l1^2), e2hn/omega1, 0) - cdf(D,-l2/sqrt(1+l2^2),
e2hn/omega2, 0)):NA
Standard errors based on Outer Products matrix
estimate std. error z p-value
----------------------------------------------------------
cVec[1] 5.61027 0.189973 29.53 1.12e-191 ***
cVec[2] 0.0293063 0.00650305 4.507 6.59e-06 ***
cVec[3] -0.000484629 0.000127917 -3.789 0.0002 ***
cVec[4] 0.00363683 0.00253677 1.434 0.1517
cVec[5] -0.163931 0.0372663 -4.399 1.09e-05 ***
cVec[6] 0.0832537 0.0305658 2.724 0.0065 ***
cVec[7] -0.0586936 0.0300906 -1.951 0.0511 *
cVec[8] 0.165683 0.0296335 5.591 2.26e-08 ***
cVec[9] 0.0938678 0.0469461 1.999 0.0456 **
cVec[10] -0.327508 0.0678569 -4.826 1.39e-06 ***
cVec[11] 0.106121 0.0335695 3.161 0.0016 ***
cVec[12] 0.0566442 0.00623448 9.086 1.03e-019 ***
cVec[13] -0.189254 0.0551031 -3.435 0.0006 ***
v0 0.219833 0.0951107 2.311 0.0208 **
v1 0.263678 0.111623 2.362 0.0182 **
v2 0.287587 0.103956 2.766 0.0057 ***
Log-likelihood 511.6733 Akaike criterion -991.3467
Schwarz criterion -921.1297 Hannan-Quinn -964.0024
>
*COMMENT: **all parameter estimates are very close but the value of the
log-likelihood is positive.*
---------------------------------------------
If I specify mle logl = check ? *0.467355827915218* - ln(v) etc I get
<<
--- FINAL VALUES:
loglikelihood = -172.877055337 (steplength = 5.36871e-021)
Parameters: 5.7197 0.029288 -0.00048358 0.0037060 -0.17731
0.065087
-0.062683 0.16589 0.096647 -0.34367
0.098338 0.054146
-0.18382 2.2828e-008 0.33034 0.42766
Gradients: 0.98941 -16.941 65.400 61.632
-0.42174 0.31255
-0.078953 0.32094 -0.099944 -0.028678
1.6085 23.937
0.80591 6.6747e-005 0.045333 0.0018853 (norm
7.16e-001)
Tolerance = 1.81899e-012
Function evaluations: 502
Evaluations of gradient: 79
Model 5: ML, using observations 1-595
logl = check ? 0.467355827915218 -ln(v) - 0.5*(e2hn/v)^2 +
ln(cdf(D,l1/sqrt(1+l1^2), e2hn/omega1, 0) - cdf(D,-l2/sqrt(1+l2^2),
e2hn/omega2, 0)):NA
Standard errors based on Outer Products matrix
estimate std. error z p-value
----------------------------------------------------------------
cVec[1] 5.71974 0.179086 31.94 7.79e-224 ***
cVec[2] 0.0292883 0.00593066 4.938 7.87e-07 ***
cVec[3] -0.000483577 0.000116778 -4.141 3.46e-05 ***
cVec[4] 0.00370595 0.00245961 1.507 0.1319
cVec[5] -0.177311 0.0358279 -4.949 7.46e-07 ***
cVec[6] 0.0650868 0.0284437 2.288 0.0221 **
cVec[7] -0.0626826 0.0289442 -2.166 0.0303 **
cVec[8] 0.165888 0.0279917 5.926 3.10e-09 ***
cVec[9] 0.0966475 0.0452880 2.134 0.0328 **
cVec[10] -0.343670 0.0618104 -5.560 2.70e-08 ***
cVec[11] 0.0983375 0.0315998 3.112 0.0019 ***
cVec[12] 0.0541457 0.00606580 8.926 4.40e-019 ***
cVec[13] -0.183823 0.0524279 -3.506 0.0005 ***
v0 2.28282e-08 405491 0.0000 1.0000
v1 0.330336 0.0328825 10.05 9.57e-024 ***
v2 0.427663 0.0335927 12.73 3.98e-037 ***
Log-likelihood -172.8771 Akaike criterion 377.7541
Schwarz criterion 447.9711 Hannan-Quinn 405.0984
>
*COMMENT: **slope coefficients are again comparable and the value of the
likelihood is close to what it should have been if its constant term was
added afterwards. But the estimates of the three variance terms v0 v1 v2
are totally different, the one reaching the specified boundary of the
parameter space (zero). *
Alecos Papadopoulos
Athens University of Economics and Business, Greece
Department of Economics
cell:+30-6945-378680
fax: +30-210-8259763
skype:alecos.papadopoulos
On 9/7/2013 16:00, gretl-users-request(a)lists.wfu.edu wrote:
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Today's Topics:
1. retrieving F-stat and p-value from a VAR system (cociuba mihai)
2. Re: Constant in log-likelihood and graph of two densities
together (Allin Cottrell)
3. Re: retrieving F-stat and p-value from a VAR system
(Allin Cottrell)
4. Re: retrieving F-stat and p-value from a VAR system
(Allin Cottrell)
5. Implement new criterion for var lag selection
(Gian Lorenzo Spisso)
6. Re: Implement new criterion for var lag selection
(Riccardo (Jack) Lucchetti)
7. Re: Implement new criterion for var lag selection
(Gian Lorenzo Spisso)
----------------------------------------------------------------------
Message: 1
Date: Tue, 9 Jul 2013 01:44:11 +0300
From: cociuba mihai <cociuba(a)gmail.com>
Subject: [Gretl-users] retrieving F-stat and p-value from a VAR system
To: gretl-users(a)lists.wfu.edu
Message-ID:
<CADSiGnWsNfdNat0ZNGzib+Qg6TsANuRGS3NTPO0id=qY36zcXQ(a)mail.gmail.com>
Content-Type: text/plain; charset="iso-8859-1"
Dear GRETL users,
I'm testing Granger causality between inflation and inflation uncertainty
for 15 countries and I would like to retrieve the result of the Wald test
in a matrix, the script that I try to run gets stuck at the last step. Any
suggestion are welcome.
###hansl###
open Table_17.3.gdt
var 10 M1 R --lagselect
a=2
b=3
c=6
d=8
#number of rows 4, but the number of F statistics reported in the VAR
output for #every equations is 3 so maybe I need more?
scalar T = 4
#generate the matrix with 4 rows and 2 colums
matrix F_stat = zeros(T,2)
#rename the colums
# is it possible to have also the name of the F test?
colnames(F_stat, "t-stat p-value")
loop foreach i a b c d
var $i M1 R --nc
F_stat[$i,] = $test ~ $pvalue
endloop
print F_stat
###end###
Thanks, Mihai
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Message: 2
Date: Mon, 8 Jul 2013 21:31:05 -0400 (EDT)
From: Allin Cottrell <cottrell(a)wfu.edu>
Subject: Re: [Gretl-users] Constant in log-likelihood and graph of two
densities together
To: Gretl list <gretl-users(a)lists.wfu.edu>
Message-ID: <alpine.LFD.2.10.1307082117570.23324@myrtle>
Content-Type: TEXT/PLAIN; charset=US-ASCII; format=flowed
On Mon, 8 Jul 2013, Alecos Papadopoulos wrote:
> Good evening everybody. I am rather new to Gretl and my questions
> are probably kindergarten-level, but I could not figure out the
> answers myself or using Help. So here they are
>
> 1) I run maximum likelihood from the script window. I am trying
> two different and non-nested stochastic specifications. I have to
> compare and evaluate them by using the value of the maximized
> log-likelihood. But since they are non-nested, their
> log-likelihood functions are totally different. So, suddenly, the
> constants of each log-likelihood, although they play no role in
> the estimation of the parameters, influence the value of the
> maximized logl - and they are different constants.
>
> If I don't include them in the logl function, then the values of
> the maximized logl (and the AIC and BIC and HQ criteria) will be
> misleading for comparison purposes of the two competing stochastic
> specifications, and currently I am doing the corrections by hand
> (which I can live with). But it would be nice not to have output
> that needs such corrections. I tried to include them in the
> specification of the logl after the "mle logl = " command. But
> when I tried to include them as, say, "ln(4/sqrt(2/pi))" or
> "ln(4/sqrt(2/%pi)) I get "syntax error on the command line".
The recommended way of accessing pi = 3.14... in current gretl
(version 1.9.12) is "$pi", though plain "pi" (deprecated since May
2012) will still work; "%pi" will definitely not work. The
expression
ln(4/sqrt(2/$pi))
is correctly evaluated as 1.612... in current gretl.
> When I calculate them explicitly, say 0.45678 and enter this
> constant instead, Gretl runs, but the estimation goes astray, and
> produces different results than when the constant is not included.
> I suspect that this may have something to do with the fact that I
> do not specify analytical derivatives, but I really don't know.
> What am I doing wrong?
The issue of analytical versus numerical derivatives wouldn't seem
to be relevant to the inclusion or non-inclusion of a constant term
(which obviously doesn't have a derivative) in the log-likelihood.
I suppose something else must be wrong here. I think you'll have to
show us your full script to get useful help.
> 2) Again for comparison purposes, I would want to have in one graph the
> estimated densities of two series. But when I select two series the
> "Variable" menu becomes disabled, while in the "View" menu there
are
> various graph options, but not the option to graph the estimated
> densities of the two series together. Is there a way around this?
This question has come up before. Please see
http://lists.wfu.edu/pipermail/gretl-users/2013-April/008745.html
Allin Cottrell
------------------------------
Message: 3
Date: Mon, 8 Jul 2013 21:59:16 -0400 (EDT)
From: Allin Cottrell <cottrell(a)wfu.edu>
Subject: Re: [Gretl-users] retrieving F-stat and p-value from a VAR
system
To: Gretl list <gretl-users(a)lists.wfu.edu>
Message-ID: <alpine.LFD.2.10.1307082142420.23324@myrtle>
Content-Type: TEXT/PLAIN; charset=US-ASCII; format=flowed
On Tue, 9 Jul 2013, cociuba mihai wrote:
> I'm testing Granger causality between inflation and inflation
> uncertainty for 15 countries and I would like to retrieve the
> result of the Wald test...
What Wald test? (That is, for what null hypothesis?)
> in a matrix, the script that I try to run gets stuck at the last
> step. Any suggestion are welcome.
[last step]
> loop foreach i a b c d
> var $i M1 R --nc
> F_stat[$i,] = $test ~ $pvalue
> endloop
The "var" command in gretl does not supply a $test accessor. In fact
no model estimation command in gretl does that: the label "test" is
much too general, given that many sorts of tests might be
contemplated after estimating a given model (either single-equation
or multi-equation).
Since a VAR is just a collection of equations related in a certain
way (identical right-hand sides, specific relation between left-hand
side variables and right-hand sides), estimated in practice via OLS,
you can get whatever Wald statistics you want by estimating the
equations singly via the "ols" command, and using either "omit" or
"restrict" (which do produce $test and $pvalue).
(I suppose we could generalize the current scalar $Fstat accessor
for single equation models to a matrix for VARs, but that would
require some decisions on which F-stats to include and in what
configuration.)
Allin Cottrell
------------------------------
Message: 4
Date: Mon, 8 Jul 2013 22:15:21 -0400 (EDT)
From: Allin Cottrell <cottrell(a)wfu.edu>
Subject: Re: [Gretl-users] retrieving F-stat and p-value from a VAR
system
To: Gretl list <gretl-users(a)lists.wfu.edu>
Message-ID: <alpine.LFD.2.10.1307082212280.23324@myrtle>
Content-Type: TEXT/PLAIN; charset=US-ASCII; format=flowed
On Mon, 8 Jul 2013, Allin Cottrell wrote:
> On Tue, 9 Jul 2013, cociuba mihai wrote:
>
>> I'm testing Granger causality between inflation and inflation uncertainty
>> for 15 countries and I would like to retrieve the result of the Wald
>> test...
> What Wald test? (That is, for what null hypothesis?)
OK, in fact clear enough from context. Trivial example of what I
described in my previous posting:
<hansl>
open data9-7
scalar p = 4
var p PRIME UNEMP
list RHS = const PRIME(-1 to -p) UNEMP(-1 to -p)
# first equation: does UNEMP Granger-cause PRIME?
ols PRIME RHS --quiet
omit UNEMP(-1 to -p) --quiet --test-only
eval $test
eval $pvalue
# second equation: does PRIME Granger-cause UNEMP?
ols UNEMP RHS --quiet
omit PRIME(-1 to -p) --quiet --test-only
eval $test
eval $pvalue
</hansl>
Allin Cottrell
------------------------------
Message: 5
Date: Tue, 9 Jul 2013 13:15:43 +0200
From: Gian Lorenzo Spisso <glspisso(a)gmail.com>
Subject: [Gretl-users] Implement new criterion for var lag selection
To: gretl-users(a)lists.wfu.edu
Message-ID:
<CAJ_wB9=gLShM2DdET7uk_f0CDBTBRgdcsqj_G_mvhkHE4jcE_w(a)mail.gmail.com>
Content-Type: text/plain; charset="iso-8859-1"
Hi all,
I would like to implement in GRETL the procedure for lag selection of a VAR
as specified here:
http://www.tandfonline.com/doi/pdf/10.1080/1350485022000041050 which
essentialy replace BIC and HQC with a weighted average of the two.
Is there any easy to install package that I could use?
Otherwise could it be possible to simply reprogram AIC column to show this
criterion instead? In case can anybody provide a little guidance for the
process? I am not familiar with gretl programming.
Thank you,
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Message: 6
Date: Tue, 9 Jul 2013 14:45:28 +0200 (CEST)
From: "Riccardo (Jack) Lucchetti" <r.lucchetti(a)univpm.it>
Subject: Re: [Gretl-users] Implement new criterion for var lag
selection
To: Gretl list <gretl-users(a)lists.wfu.edu>
Message-ID: <alpine.DEB.2.10.1307091444170.13798(a)ec-4.econ.univpm.it>
Content-Type: text/plain; charset="iso-8859-1"
On Tue, 9 Jul 2013, Gian Lorenzo Spisso wrote:
> Hi all,
> I would like to implement in GRETL the procedure for lag selection of a VAR
> as specified here:
> http://www.tandfonline.com/doi/pdf/10.1080/1350485022000041050 which
> essentialy replace BIC and HQC with a weighted average of the two.
>
> Is there any easy to install package that I could use?
> Otherwise could it be possible to simply reprogram AIC column to show this
> criterion instead? In case can anybody provide a little guidance for the
> process? I am not familiar with gretl programming.
I don't have a subscription to "Applied Economics Journal". Could you
describe me the proposed method?
-------------------------------------------------------
Riccardo (Jack) Lucchetti
Dipartimento di Scienze Economiche e Sociali (DiSES)
Universit? Politecnica delle Marche
(formerly known as Universit? di Ancona)
r.lucchetti(a)univpm.it
http://www2.econ.univpm.it/servizi/hpp/lucchetti
-------------------------------------------------------
------------------------------
Message: 7
Date: Tue, 9 Jul 2013 14:58:42 +0200
From: Gian Lorenzo Spisso <glspisso(a)gmail.com>
Subject: Re: [Gretl-users] Implement new criterion for var lag
selection
To: r.lucchetti(a)univpm.it, Gretl list <gretl-users(a)lists.wfu.edu>
Message-ID:
<CAJ_wB9ndVjdNygwUYYDQEc9Ad7=+Px9q4wDW+orXLza6f28rjw(a)mail.gmail.com>
Content-Type: text/plain; charset="iso-8859-1"
Dear Riccardo,
I attach a screenshot of the relevant part.
You can see the formulas for the two criterion, and the new criterion
proposed by Hatemi which simply averages the two. He then goes on and uses
a Montecarlo simulation to show that this mixed criterion as higher
probability in picking the right lag.
On Tue, Jul 9, 2013 at 2:45 PM, Riccardo (Jack) Lucchetti <
r.lucchetti(a)univpm.it> wrote:
> On Tue, 9 Jul 2013, Gian Lorenzo Spisso wrote:
>
> Hi all,
>> I would like to implement in GRETL the procedure for lag selection of a
>> VAR
>> as specified here:
>>
http://www.tandfonline.com/**doi/pdf/10.1080/**1350485022000041050<htt...
>> essentialy replace BIC and HQC with a weighted average of the two.
>>
>> Is there any easy to install package that I could use?
>> Otherwise could it be possible to simply reprogram AIC column to show this
>> criterion instead? In case can anybody provide a little guidance for the
>> process? I am not familiar with gretl programming.
>>
> I don't have a subscription to "Applied Economics Journal". Could you
> describe me the proposed method?
>
> ------------------------------**-------------------------
> Riccardo (Jack) Lucchetti
> Dipartimento di Scienze Economiche e Sociali (DiSES)
>
> Universit? Politecnica delle Marche
> (formerly known as Universit? di Ancona)
>
> r.lucchetti(a)univpm.it
>
http://www2.econ.univpm.it/**servizi/hpp/lucchetti<http://www2.econ.un...
> ------------------------------**-------------------------
> _______________________________________________
> Gretl-users mailing list
> Gretl-users(a)lists.wfu.edu
> http://lists.wfu.edu/mailman/listinfo/gretl-users
>
5:45 p.m.
New subject: Constant in log-likelihood and graph of two densities together (Allin Cottrell)
On Tue, 9 Jul 2013, Alecos Papadopoulos wrote:
GRAPH OF TWO DENSITIES TOGETHER: Thanks for providing the older link.
Although the code there is to plot two densities /consecutively /from left to
right, while what I need to do is to /superimpose/ them - and this I realize
now has the problem of having two different abscissaes series. Still, I
learned something new about handling plots in Gretl.
Really? Have you seen this?
http://lists.wfu.edu/pipermail/gretl-users/2013-April/008747.html
CONSTANT IN LOG-LIKELIHOOD
The basic code *without the constant in the log-l *is (omitting the initial
part where OLS executes to obtain initial values)
[...]
*COMMENT: **slope coefficients are again comparable and the value of
the
likelihood is close to what it should have been if its constant term was
added afterwards. But the estimates of the three variance terms v0 v1 v2 are
totally different, the one reaching the specified boundary of the parameter
space (zero). *
This is very strange indeed. It *may* have something to do with the
machine epsilon of your computer, but still it's very strange. Basically,
models 1 and 2 converge to the same maximum (with negligible differences);
model 3 really doesn't converge at all: BFGS gives you a spurious
convergence message, but you're not on the maximum. Weird.
Here's a couple of things you may try just to see what happens:
* try using "set bfgs_richardson on"; this uses a different algorithm for
computing numerical derivatives. Slower, but much more accurate.
* re-parametrise your model so to avoid estimating quantities, such as
variances, which have a lower bound. Try logarithms instead, for example.
-------------------------------------------------------
Riccardo (Jack) Lucchetti
Dipartimento di Scienze Economiche e Sociali (DiSES)
Università Politecnica delle Marche
(formerly known as Università di Ancona)
r.lucchetti(a)univpm.it
http://www2.econ.univpm.it/servizi/hpp/lucchetti
-------------------------------------------------------
3937
days inactive
3937
days old
1 comments
2 participants
participants (2)
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Riccardo (Jack) Lucchetti