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EViews sample workfiles
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EViews sample workfiles
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Save scalars in a vector
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Save scalars in a vector
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Save scalars in a vector
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Save scalars in a vector
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Save scalars in a vector
And if I want to run the loop from 1 to 100 without the step and save the 91 values in a vector? How I could conduct that?
vector(91) a
for !i=10 to 100
for !j=1 to 91
a(!j)=ssrt_!i
next
next
I have run something like this but it gives me the same value (the last ssrt_!i of the loop) in all the rows of the vector.
vector(91) a
for !i=10 to 100
for !j=1 to 91
a(!j)=ssrt_!i
next
next
I have run something like this but it gives me the same value (the last ssrt_!i of the loop) in all the rows of the vector.
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F-Leamer test
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Stationary time series
Hello,
Im new to Eviews and time series analysis. Im a student of Finance and I had to enroll a time series subject. We were told to find and analyse a nonstationary (which was clearly easy) and a stationary time series.
I found like 15 or 20 time series which seemed to be stationary (e.g. here). I use census x-13 to see whether there is seasonality. This particular time series, just like 10 other, is not stationary according to ADF test with NONE model. Its like there is pattern in me doing something wrong or being tremendously unfortunate, because i get these results with every single time series Ive tested.
ADF with C says its stationary, ADF with trend is usually not a good model since the trend is insignificant and ADF with NONE says its nonstationary.
I checked the correlogram of the SA'ed time series at level and there seems to be no order or any other generating process, so I assume that should confirm my theory.
Given the time I have spent trying to find a stationary series it seems like I will never be successful. I need to diagnose it (normality, LM test, ARCH test) and everything has to pass. After that I need to find applicable ARMA model and make simple predictions.
I dont want try SARIMA since we havent learned how so thats why I need to work with SA'ed time series, coz I heard ADF only work on SA'ed TS.
Would anyone here be able to help me, please? Im desperate. This is quite a task.
If either of you happend to have time series that is sure to be stationary, please do share with me.
Im new to Eviews and time series analysis. Im a student of Finance and I had to enroll a time series subject. We were told to find and analyse a nonstationary (which was clearly easy) and a stationary time series.
I found like 15 or 20 time series which seemed to be stationary (e.g. here). I use census x-13 to see whether there is seasonality. This particular time series, just like 10 other, is not stationary according to ADF test with NONE model. Its like there is pattern in me doing something wrong or being tremendously unfortunate, because i get these results with every single time series Ive tested.
ADF with C says its stationary, ADF with trend is usually not a good model since the trend is insignificant and ADF with NONE says its nonstationary.
I checked the correlogram of the SA'ed time series at level and there seems to be no order or any other generating process, so I assume that should confirm my theory.
Given the time I have spent trying to find a stationary series it seems like I will never be successful. I need to diagnose it (normality, LM test, ARCH test) and everything has to pass. After that I need to find applicable ARMA model and make simple predictions.
I dont want try SARIMA since we havent learned how so thats why I need to work with SA'ed time series, coz I heard ADF only work on SA'ed TS.
Would anyone here be able to help me, please? Im desperate. This is quite a task.
If either of you happend to have time series that is sure to be stationary, please do share with me.
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Stationary time series
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Threshold Structural VAR
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Clustered Standard Errors
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Get axis range from graph object
Hey,
I'd like to find out which ranges EViews uses for a certain graph. Is there a way to do that? Setting the range is straightforward, but I'd like to get the ranges EViews uses. If it's not possible to get the ranges from an object, how does EViews calculate min and max? Usually it's some "nice" number and I'd like to know how EViews gets there (simple rounding isn't very flexible if you don't know the scale of the variable apriori).
Thanks in advance!
I'd like to find out which ranges EViews uses for a certain graph. Is there a way to do that? Setting the range is straightforward, but I'd like to get the ranges EViews uses. If it's not possible to get the ranges from an object, how does EViews calculate min and max? Usually it's some "nice" number and I'd like to know how EViews gets there (simple rounding isn't very flexible if you don't know the scale of the variable apriori).
Thanks in advance!
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Get axis range from graph object
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Kalman filter and NAIRU
Hello!
Now I have tried to estimate something but I am not sure if this is right or not.
For example, I have formed these signal and state equations basically for all the European Union countries (this specific example is for Spain):
@signal hicp_es=c(1)*hicp_lag_es + c(2)*(unemp_es-nairu) + c(3)*hicp_foodenergy_es + [var=exp(c(4))]
@state nairu=nairu(-1)+[var=0.04]
param c(1) .0 c(2) .0 c(3) .0 c(4) .0
HICP is taken from Eurostat and is harmonised index of consumer prices (excluding food and energy prices) and foodenergy states for only for food and energy prices. I have converted monthly data to quarterly data by myself since I have quarterly data for unemployment rate.
HICP lag was calculated in EViews.
Does it seem that there are something missing or extremely wrong?
Because, for example, in this case, I have received final state value 27 something (if I have chosen in the estimation box EViews legacy). If I change it to BFGS, for example, then the value will be -900 something... But the same equations for Germany give me the final state value about 990 (with EViews legacy) which is of course wrong and also, the probabilities of c(2) and c(3) are above 0.05.
These equations were actually taken from one of the examples in this forum.
Please let me know if there is something extremely wrong.
Now I have tried to estimate something but I am not sure if this is right or not.
For example, I have formed these signal and state equations basically for all the European Union countries (this specific example is for Spain):
@signal hicp_es=c(1)*hicp_lag_es + c(2)*(unemp_es-nairu) + c(3)*hicp_foodenergy_es + [var=exp(c(4))]
@state nairu=nairu(-1)+[var=0.04]
param c(1) .0 c(2) .0 c(3) .0 c(4) .0
HICP is taken from Eurostat and is harmonised index of consumer prices (excluding food and energy prices) and foodenergy states for only for food and energy prices. I have converted monthly data to quarterly data by myself since I have quarterly data for unemployment rate.
HICP lag was calculated in EViews.
Does it seem that there are something missing or extremely wrong?
Because, for example, in this case, I have received final state value 27 something (if I have chosen in the estimation box EViews legacy). If I change it to BFGS, for example, then the value will be -900 something... But the same equations for Germany give me the final state value about 990 (with EViews legacy) which is of course wrong and also, the probabilities of c(2) and c(3) are above 0.05.
These equations were actually taken from one of the examples in this forum.
Please let me know if there is something extremely wrong.
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State Space Model Latent Factors with stochastic Volatility
Good morning,
I'm new at Eviews and I'm studying the Diebold Li Nelson Siegel model.
i'm trying to estimate the following state-space model:
y(t) = x*f(t) + e(t) -- measurement equation
f(t) = u(t) + phi*f(t-1) + v(t)*exp(h(t)/2) -- state equation
h(t) = z(t) + delta*h(t-1) + s(t) -- state equation
I using the following code:
And 'm obtaining the following error: invalidade or duplicate specification in covariance assignment
I'm new at Eviews and I'm studying the Diebold Li Nelson Siegel model.
i'm trying to estimate the following state-space model:
y(t) = x*f(t) + e(t) -- measurement equation
f(t) = u(t) + phi*f(t-1) + v(t)*exp(h(t)/2) -- state equation
h(t) = z(t) + delta*h(t-1) + s(t) -- state equation
I using the following code:
@signal y_3 =sv1+( ( 1-exp(-c(28)*3) ) / (c(28)*3) )*sv2+( ( ( 1-exp(-c(28)*3) ) / (c(28)*3) ) - ( exp(-c(28)*3) ) )*sv3+[ename=e_3]
@signal y_12 =sv1+( ( 1-exp(-c(28)*12) ) / (c(28)*12) )*sv2+( ( ( 1-exp(-c(28)*12) ) / (c(28)*12) ) - ( exp(-c(28)*12) ) )*sv3+[ename=e_12]
@signal y_24 =sv1+( ( 1-exp(-c(28)*24) ) / (c(28)*24) )*sv2+( ( ( 1-exp(-c(28)*24) ) / (c(28)*24) ) - ( exp(-c(28)*24) ) )*sv3+[ename=e_24]
@signal y_36 =sv1+( ( 1-exp(-c(28)*36) ) / (c(28)*36) )*sv2+( ( ( 1-exp(-c(28)*36) ) / (c(28)*36) ) - ( exp(-c(28)*36) ) )*sv3+[ename=e_36]
@signal y_60 =sv1+( ( 1-exp(-c(28)*60) ) / (c(28)*60) )*sv2+( ( ( 1-exp(-c(28)*60) ) / (c(28)*60) ) - ( exp(-c(28)*60) ) )*sv3+[ename=e_60]
@signal y_84 =sv1+( ( 1-exp(-c(28)*84) ) / (c(28)*84) )*sv2+( ( ( 1-exp(-c(28)*84) ) / (c(28)*84) ) - ( exp(-c(28)*84) ) )*sv3+[ename=e_84]
@signal y_120=sv1+( ( 1-exp(-c(28)*120) ) / (c(28)*120) )*sv2+( ( ( 1-exp(-c(28)*120) ) / (c(28)*120) ) - ( exp(-c(28)*120) ) )*sv3+[ename=e_120]
@signal y_360=sv1+( ( 1-exp(-c(28)*360) ) / (c(28)*360) )*sv2+( ( ( 1-exp(-c(28)*360) ) / (c(28)*360) ) - ( exp(-c(28)*360) ) )*sv3+[ename=e_360]
@state sv1 = c(10) + c(1)*( sv1(-1) - c(10) ) + c(2)*( sv2(-1) - c(11) ) + c(3)*( sv3(-1) - c(12) ) + [ename=ni_l]
@state sv2 = c(11) + c(4)*( sv1(-1) - c(10) ) + c(5)*( sv2(-1) - c(11) ) + c(6)*( sv3(-1) - c(12) ) + [ename=ni_s]
@state sv3 = c(12) + c(7)*( sv1(-1) - c(10) ) + c(8)*( sv2(-1) - c(11) ) + c(9)*( sv3(-1) - c(12) ) + [ename=ni_c]
@state h1 = c(29) + c(30)*(h1(-1) - c(29)) + c(31)*(h2(-1) - c(33)) + c(32)*(h3(-1) - c(34)) + [ename=ni_h1]
@state h2 = c(33) + c(35)*(h1(-1) - c(29)) + c(37)*(h2(-1) - c(33)) + c(39)*(h3(-1) - c(34)) + [ename=ni_h2]
@state h3 = c(34) + c(36)*(h1(-1) - c(29)) + c(38)*(h2(-1) - c(33)) + c(40)*(h3(-1) - c(34)) + [ename=ni_h3]
@evar var(ni_l)=exp(c(13))*exp(h1/2)
@evar var(ni_s)=exp(c(14))*exp(h2/2)
@evar var(ni_c)=exp(c(15))*exp(h3/2)
@evar var(ni_h1)=exp(c(41))
@evar var(ni_h2)=exp(c(42))
@evar var(ni_h3)=exp(c(43))
@evar var(e_3)=exp(c(19))
@evar var(e_12)=exp(c(21))
@evar var(e_24)=exp(c(22))
@evar var(e_36)=exp(c(23))
@evar var(e_60)=exp(c(24))
@evar var(e_84)=exp(c(25))
@evar var(e_120)=exp(c(26))
@evar var(e_360)=exp(c(27))
And 'm obtaining the following error: invalidade or duplicate specification in covariance assignment
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Kalman filter and NAIRU
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Autocorrelation and heteroskedasticity in Panel Data
Hi,
That is my question too. Can anybody help us?
I use EViews 10, but in this version ,is there any possibility for white test. I want to solve Hetroscedastisty Problem with white, but Cent find it.
Is it enough , if we choose white cross-section In Equastion Estimation, in Panel Option ? or should I choose fix Effect in Effect spesification too ?
Im looking for any answer please!!!
Thank you
That is my question too. Can anybody help us?
I use EViews 10, but in this version ,is there any possibility for white test. I want to solve Hetroscedastisty Problem with white, but Cent find it.
Is it enough , if we choose white cross-section In Equastion Estimation, in Panel Option ? or should I choose fix Effect in Effect spesification too ?
Im looking for any answer please!!!
Thank you
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Autocorrelation and heteroskedasticity in Panel Data
The standard introductory approaches to autocorrelation and heteroskedasticity don't apply in panel data. Thus there is no "White test" or simple "White standard errors". The idea of heteroskedaticity is more complicated in a panel setting - is the covariance between errors within cross-sections only, within period only, or a mixture?
Thus the options for testing and adjusting covariances are different. You have to figure out what you want to correct for, then choose the appropriate covariance option.
Thus the options for testing and adjusting covariances are different. You have to figure out what you want to correct for, then choose the appropriate covariance option.
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Toda and Yamamoto causality test
Good Morning. Hello,
Please, in the Toda-yamamoto method, I proposed the trend as an exogenous variable, with two types (linear and quadratic). Please sir, the choice of the best model between the two, is that between the 2 basic models (Var (1)) OR between the 2 augmented models (Var (1 + 2)) of Toda-yamamoto?
Best wishes.
Please, in the Toda-yamamoto method, I proposed the trend as an exogenous variable, with two types (linear and quadratic). Please sir, the choice of the best model between the two, is that between the 2 basic models (Var (1)) OR between the 2 augmented models (Var (1 + 2)) of Toda-yamamoto?
Best wishes.
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