EViews.com

Hello,

You can proceed to cointegration even if there is a mix of I(0) and I(1) variables! Each stationary variable will reveal itself as a cointegrating vector.

Regads

Hello ermutuxia,

I don’t know if you are familiar with cointegration analysis within the Johansen framework but how do we proceed in the case where there is a mix of I(0) and I(1) variables?

Regards

In a previous post I wrote the following: For those who might be interested in estimating standard errors for the associated marginal effects take a look at the two following papers! Anderson, S. and Newell, R. (2003). Simplified marginal effects in discrete choice models. Economics Letters, 81. 321...

My question has to do with the Johansen cointegration technique and with the associated error-correction model. The question is how can I define a list of exogenous I(1) variables whose effects are assumed to be confined to the cointegrating relationships (restricted actually to the cointegrating sp...

Hi, I would like to examine if 5 variables are cointegrated. The problem is that 4 of them are I(1) (3 independent variables plus the dependent variable) and one is I(0)(independent variable). I know that a cointegration can be established (without worrying about the integration order of the variabl...

Hi Trubador,

Please forgive me for the indiscreet question but is there any intension to complete the program by incorporating Monte Carlo simulations?

Kind Regards

hi

Try to see more carefully the forum. There are exact answers to your questions.

Regards

hi

You can continue to the estimation without dropping the variable

Dear Trubador,

It would be of great pleasure if a Monte Carlo simulation was also applied to calculate the exact critical values according to the sample size used each time. Is it hard to program something like that? Once again thank you for your contribution!

Regards

From the estimated equation window select: view representations. Now select and copy the formula for the substituted coefficients. It should look like, depending on the variables you have used, something like this: GRADE = 1-@CNORM(-(-7.45231964735 + 1.62581003927*GPA + 0.0517289454976*TUCE + 1.4263...

hi,

In step 4 you estimate l_xb which is a scalar ( a single value – not a series ).

In stap 5 multiply l_xb (scalar) to each single coefficient to get the marginal effect (l_xb*coefficient)

regards

Hi,

Another thing that you may do, if the purpose is to detect those observations which are highly correlated within your sample, is to estimate rolling correlations for a sample window of lets say 20 observations. I don’t know if my suggestion is of any help!

Regards

Hi, I have compared the results delivered from the above E-views code with those delivered from the corresponding RATS code. They are identical. This is a perfect piece of work. We are grateful Trubador!! For users convenience the asymptotic critical values are provided below: model Α model Β model ...

Hello, Usually we estimate marginal effects for a “typical” individual. Therefore, given that all your independent variables are continuous, calculate the mean value for the sufferingF series. scalar xb= @mean(sufferingF) then estimate the following scalar scalar l_xb = @dlogistic(-xb) And you are d...

For those who might be interested in estimating standard errors for the associated marginal effects take a look at the two following papers! Anderson, S. and Newell, R. (2003). Simplified marginal effects in discrete choice models. Economics Letters, 81. 321-326. Carlevaro, F. and Senegas, M. (2006)...