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Forecast averaging
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Forecast averaging
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Favar QUESTION
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how to do wilcoxon Test
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Forecast averaging
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How do I do a Box-Cox Transformation?
Hello,
It's possible to estimate the best value of lamda Box-Cox transformation with "use Spitzer(1982) algorithm diriving from L(lamda)".
First, compute the geometric mean of Y(t), with Y(t)<>0.
Second, scale Yt by its geometric mean:
third, the “trick” simplifies L(λ) considerably. Ignoring constant terms, L(lamda)...
my question is: How can estimate the best lamda value with Spitzer(1982) algorithm or via simple program, honestly i have a difficulty to interpret the third step by LogL.
Could someone help me.
Best Cherif.
It's possible to estimate the best value of lamda Box-Cox transformation with "use Spitzer(1982) algorithm diriving from L(lamda)".
First, compute the geometric mean of Y(t), with Y(t)<>0.
Code:
series gm=@gmean(Y)
Second, scale Yt by its geometric mean:
Code:
series z=y/gm
third, the “trick” simplifies L(λ) considerably. Ignoring constant terms, L(lamda)...
my question is: How can estimate the best lamda value with Spitzer(1982) algorithm or via simple program, honestly i have a difficulty to interpret the third step by LogL.
Could someone help me.
Best Cherif.
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Fama-MacBeth regression
Hi everyone
I'm dealing with fama mcbeth regression but i cant realize the procedure.
In fm regression i want to regress monthly returns on operating profitability. I have 100 stocks with monthly return data and annual operating profitability data for ten years. and i also have 3 control variables (b/m , r1,1 , r12,2). So please help me how to fit these data on fm regression because returns data are monthly but operating profitability data are annual. I dont know how can i relate monthly data with annual...
having your assistance would be of great help. thanks.
I'm dealing with fama mcbeth regression but i cant realize the procedure.
In fm regression i want to regress monthly returns on operating profitability. I have 100 stocks with monthly return data and annual operating profitability data for ten years. and i also have 3 control variables (b/m , r1,1 , r12,2). So please help me how to fit these data on fm regression because returns data are monthly but operating profitability data are annual. I dont know how can i relate monthly data with annual...
having your assistance would be of great help. thanks.
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Panel data Coefficient Variance Decomposition Bug?
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error in Dumitrescu Hurlin test in Eviews 9?
This paper (https://www.unine.ch/files/live/sites/i ... P17-03.pdf) claims there is an error in Eviews 8 Dumitrescu Hurlin causality test code. Did you fix that for Eviews 9?
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Engle-Granger test
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Panel data Coefficient Variance Decomposition Bug?
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weights vector
Hello,
I'm doing some basic risk management analysis and have an issue while uploading the vector of portfolio weights. I can upload the weights, but not the name of the Securities in the portfolio, not even as labels or typing in the names. Could anyone kindly help me?
PS: I'm quite new to RM procedures in eviews, so any suggestion you may have is very much welcome!
Best Regards.
Francesca
I'm doing some basic risk management analysis and have an issue while uploading the vector of portfolio weights. I can upload the weights, but not the name of the Securities in the portfolio, not even as labels or typing in the names. Could anyone kindly help me?
PS: I'm quite new to RM procedures in eviews, so any suggestion you may have is very much welcome!
Best Regards.
Francesca
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error in Dumitrescu Hurlin test in Eviews 9?
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Panel data Coefficient Variance Decomposition Bug?
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Fama-MacBeth regression
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Panel data Coefficient Variance Decomposition Bug?
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error in Dumitrescu Hurlin test in Eviews 9?
We'll go into even more detail at a later date, but to prove the results we're getting, here's some EViews code that computes the Wald statistics that they compute on page 8 of their paper.
And here is some Stata code that produces the same things using Stata's built in Wald test and Granger Causality functions:
You can see that EViews produces exactly the same individual Wald statistics as Stata does, and that the EViews produced W statistic is the average of those individual Wald statistics.
Code:
wfopen .\data_demo.dta
pagestruct id @date(t)
'compute granger causality using built in
group g y x
show g.cause(1, dh)
'loop through the cross-sections calculating Wald test for each individual
!n = 10
matrix(!n,2) walds
for !i=1 to !n
smpl if @crossid=!i
freeze(mode=overwrite, temp) g.cause(1) 'built in Granger Causality Test
walds(!i,1) = temp(8,3)
equation eq1.ls y c y(-1) x(-1) 'manually calculate using least squares
freeze(mode=overwrite, temp) eq1.wald c(3)=0
walds(!i,2) = temp(7,2)
next
smpl @all
'display individual walds and their average
show walds
=@cmean(walds)
'means are equal (to a number of decimal places), and match the W-stat given in the built in procedure.
And here is some Stata code that produces the same things using Stata's built in Wald test and Granger Causality functions:
Code:
clear
use "data_demo.dta", clear
xtset id t
xtgcause y x
mat W = J(10,2,0)
forvalues i = 1/10{
regress y L.y L.x if id==`i'
test L.x
matrix W[`i',1]=r(F)
var y x if id==`i', lags(1/1) dfk small
vargranger
matrix a=r(gstats)
matrix W[`i',2]=a[1,1]
}
matlist W
You can see that EViews produces exactly the same individual Wald statistics as Stata does, and that the EViews produced W statistic is the average of those individual Wald statistics.
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I have wonder about exponential smoothing
Hi, I have a request for clarification about this procedure ::
i used exponential smoothing with model specification(MNN) in ETS Smoothing of dowjones returns series(200 obs), then ploted two series (original series and smoothed series) as group and noted the following : there is one lag between these series, so i deleted a first observation from smoothed series and shift above the next observations and identify with original series via garph and find a fairly acceptable result.
then i made two residual series (returns-smoothed series) , (returns-smoothed series with deleted first obs) for estimate garch(1,1) model and found the following: both of estimated coefficients of two models are identity but only AIC ,BIC and Logl are different , where the AIC ,BIC for garch model of series with deleted first obs are better .
my question :: Is my previous method correct? and why?
i attached a workfile for apply
note return : original series,
return_sm_new : smoothed series with deleted first obs
i used exponential smoothing with model specification(MNN) in ETS Smoothing of dowjones returns series(200 obs), then ploted two series (original series and smoothed series) as group and noted the following : there is one lag between these series, so i deleted a first observation from smoothed series and shift above the next observations and identify with original series via garph and find a fairly acceptable result.
then i made two residual series (returns-smoothed series) , (returns-smoothed series with deleted first obs) for estimate garch(1,1) model and found the following: both of estimated coefficients of two models are identity but only AIC ,BIC and Logl are different , where the AIC ,BIC for garch model of series with deleted first obs are better .
my question :: Is my previous method correct? and why?
i attached a workfile for apply
note return : original series,
return_sm_new : smoothed series with deleted first obs
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Average Cross-sectional Regression in Panel Data Structure
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Panel data Coefficient Variance Decomposition Bug?
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