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The version 2.0 of the spectral analysis add-in is now available. It contains tests for detecting periodic components, white noise and Gaussian white noise, also it performs an automatic procedure for extracting cycles of a time series, named significant pass filter (SPF). The add-in could also be u...

Hi, The code below shows a way to perform the Augmented Dickey-Fuller test and to detect deterministic components in a series. It is based on Enders, W. (2008). Applied econometric time series. John Wiley & Sons. As the author mention no procedure can be expected to work well if it is used in a ...

Hi, You have two typos, @ename e1 @ename e2 @evar var(e1)=exp(c(4)) @evar var(e2)=exp(c(5)) @signal ipg_norm = c(6)*sv1+e1 ' you wrote the state variable as a lead of orden one,this can not be done @state sv1 = c(1)*sv1(-1) + c(2)*sv2(-1) + c(3)*sv3(-1)+e2 @state sv2 = sv1(-1) @state sv3 = sv2(-1) '...

Good afternoon,

Is there a way to make three-dimensional graphs in Eviews?

Thanks!

You can use the GroupX12 add-in.

The Hodrick Prescott filter is a low pass filter, that is, it preserves the low frequency components in the spectrum (long run). Therefore, you can use the estimated spectrum of your series to see if there is any significant peak at the low frequency components, if there is not, the smoothed series ...

Hi, You should use VECM if 1) your variables are nonstationary and 2) you find a common trend between the variables (cointegration). If your variables are nonstationary and you transform them to became stationary (diferences or subtracting trends) without doing a cointegration test you might be omit...

Hi, I belive you should introduce starting values for the state variables, you can do it like this for the Clark model: @signal gdp = trend + cycle @state trend = c(1) + trend(-1) + [var = exp(c(2))] @state cycle = c(3)*cycle(-1) + c(4)*lcycle(-1) + [var = exp(c(5))] @state lcycle = cycle(-1) param ...

1) In units of the response variable. 2) Cholesky is a matrix decomposition that ensures that your residual covariance matrix could be transformed into a diagonal matrix. The purpose of this Cholesky decomposition is that the impulse to one variable (or their innovations) must be unrelated to the im...

Hi, When you give an impulse to a system of equations or an equation, you do it through the residuals (also called innovations) which are the non explained part of the depent variable, so they are in units of the impulse variable. Usually the constant of the model is not taken into account in the im...

Did you tried different starting values for the coefficients? Maybe the coefficients c(1) to c(5) have a value where the algorithm can not start its iteration.

You can change them in the c vector coeficient.

I did not know of the existence of the formula object, thanks Glenn.

Hi, you miss some brackets or put some more, I estimate it like this: ls log(cs/(cs+ls)) - log(cs(-1)/(cs(-1) + ls(-1)))=c(3)*(c(4) - 1) + (c(4) - 1)/(1-c(2))*(log(cs+ls)/1)- (log(cs(-1)+ls(-1)))/1+ (1- c (4))*(log(pk) - log(pk(-1)))- log(py)+log(py(-1)) I think that a simpler way to estimate the mo...

It uses the 400 past values in the sense that you estimate the model with this data, but the forecast are out of sample dynamic.

No, you don´t have to change the value, you can keep it in seven, I said it in the case you wanted more forecasting ahead steps than five. I have a question,are you sure that the cointegration relationship remains across the time window?