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Conducting the Pearson's Correlation test

February 7, 2016, 5:14 pm
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My model is as follows:

Y=beta_0+beta_1*X_1+beta_2*X_2+beta_3*X_3+beta_4*X_4.

I also have two other variables X_5 and X_6 which I have not included in this model since running the Wald test told me there was no need to include them.

I want to determine if, by omitting X_5 and/or X_6, does my model suffer from omitted variable bias. In order to do this, I have run least squared regressions with Y against X_5 and Y against X_6 to see if there is any correlation (by looking at the R-Squared value). I have also done the same for X_1 against X_5, X_2 against X_5, etc. However, I also want to test whether these R-Squared values are significant or not (H_0: rho=0 and H_1: rho not equal to 0). I am finding it difficult to conduct this test using the Pearson's correlation test or the Spearmans Rank Correlation Coefficient. When I attempt to use the 'pancov' or 'corr' functions I am given an error.

Does anyone have any idea how to conduct these tests with the model I have?

Any information will be appreciated. Thank you.

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I need help in estimating the CCC model

February 7, 2016, 6:52 pm
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EViews Gareth wrote:Software version.


Thank you Gareth.

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Symmetry Plot Output Matrix

February 7, 2016, 8:11 pm
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Hey everyone,

I haven't contributed much to the community, so I want to improve on that.

I have made a simple output matrix for the symmetry plot. As it stands, this isn't built-in in Eviews, but that could change. Until then this program could provide some valuable automation if you are working with web publishing, multiple data sources / platforms.

It works in vector space, so it should be ok for any workfile type. 

Code:

%series ="your_series_name"

vector {%series}_vector = {%series}

vector {%series}_order_vector = @ranks({%series}_vector,"a","i")

matrix(@rows({%series}_vector),2) {%series}_symmetry_matrix

for !i = 1 to @rows({%series}_vector)
    {%series}_symmetry_matrix(!i,1) = @abs({%series}_vector(!i)-@median({%series}))
    {%series}_symmetry_matrix(!i,2) = @abs({%series}_vector(!i)-@median({%series}))
next

{%series}_symmetry_matrix = @capplyranks({%series}_symmetry_matrix,{%series}_order_vector,1)

{%series}_order_vector = @ranks({%series}_vector,"d","i")

{%series}_symmetry_matrix = @capplyranks({%series}_symmetry_matrix,{%series}_order_vector,2)

matrix(@rows({%series}_vector)/2,2) {%series}_symmetry_matrix = @subextract({%series}_symmetry_matrix,1,1,@rows({%series}_vector)/2,2)

show {%series}_symmetry_matrix

d {%series}_vector
d {%series}_order_vector



** forgot to thank Eviews gareth for pointing out the correct use of the @capplyranks function

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Help needed with panel data estimation

February 8, 2016, 3:02 am
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Hi!

I am examing the effect of the demographic transition in emerging Asia on my dependent varibale, the stock of FDI. According to the literature I used several control variables (GDP growth, trade openess, institutional quality, education level, real effective exchange rate (lag), total GDP and I am thinking about adding the lag of the FDI stock in to the independent variables as well.

So I am working with balanced panel data and if I understand correctly the Hausman test tells me to use fixed effects. Somewhere on the internet I found that I need to use cross section weights with the diagonal coëfficiënt covariance method since my t (=18 years) is larger than my N (12 countries). However, I have no idea if this is correct. Furthermore I expect my independent variables to be correlated. How should I deal with this? And with time series data I am used to performing tests for autocorrelation and heteroskedasticity, but how does this work for panel data?

So to sum my question: Am I using the right method of estimation (if not, which should I use and why). And which tests should I perform and how can I perform these tests in eviews 8 (I am thinking about test for autocorrelation, hetereoskedasticity, endogeneity etc.)

Thank you very much!!
PS: for convenience I uploaded the excel file with the data and the eviews file that I am working in. In this eviews file I am experimenting with different methods and different estimations (2 stage least square, EGLS, period fixed etc.)

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Panel Data - Identical Cross Section Values-UnitRoot test

February 8, 2016, 6:18 am
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Hello, my first post here.

Using FE panel data, I am regressing economic growth on a set of variables for 149 countries, from 1996 to 2011. One of the regressors is the log of initial GDP per capita (1996), so for each cross section/country, there are 16 identical values.

Random numerical example:

Country Year GDP
Afghanistan 1996 400
Afghanistan 1997 400
...
Afghanistan 2011 400

and so on so forth.

When I try any unit root test for this variable, i get the ''insufficient number of observations encountered in Uroot ́ ́error.

Could this be a simple case of literally not enough observations (hard to believe, 2384 obs should be ok) or could it be a general specification problem?

Thank you in advance!

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Sign Restricted VAR

February 8, 2016, 6:45 am
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Hi!

Thank you for this really interesting add-in. Have you considered extending it with different priors and/or more than one structural shock?

Best regards.

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Scenarios giving the same output

February 8, 2016, 7:13 am
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I have a model with several equations and two scenarios. When I initially solved the model, I got different output for the two scenarios, as one would suspect. However, I then later changed one of the equations, and then clicked on Proc - Links - Update all links - Recompile model. However, now when I solve the model I get the same output for both scenarios (just for the equation that changed - the other equations are still giving different results per scenario).

I am using Eviews 9.

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Scenarios giving the same output

February 8, 2016, 7:31 am
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Importing data description bug

February 8, 2016, 7:33 am
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It isn't a bug, it is by design. Importing will never change/overwrite attributes.

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The forecasting process

February 8, 2016, 9:41 am
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Hi, for all
how to convert the differenced forecast series to the original series !!

Thanks for your help.
Regards,,

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The forecasting process

February 8, 2016, 9:46 am
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The forecasting process

February 8, 2016, 9:59 am
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Thanks a lot for your help.
When I started with my series, I found my series is non-stationary.
I converted my series to be stationary by using:: diff.
And, I found the fit model in this case and I did the forecasting process for the differenced series.
Now, I want to find the forecasting for the original series.

Thanks for your time.

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The forecasting process

February 8, 2016, 10:00 am
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By default if you estimate an equation where the dependent variable is a difference, it will forecast the undifference values.

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The forecasting process

February 8, 2016, 10:02 am
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February 8, 2016, 9:57 pm
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The forecasting process

February 9, 2016, 1:26 am
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Thanks a lot for your cooperation.
But here,
If the model includes a lagged dependent variable. It matters for forecasting purposes whether the dependent variable is an auto series or not.
My series not Auto series, and the differenced series here as ordinary series.

Regards,

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Structuring bilateral data on excel

February 9, 2016, 2:30 am
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Hello,

I have bilateral FDI inflows data varying across time and also bilateral data on distance between countries, and I want to import it to eViews 8. Other variables that I will use in the equation include host country GDP, source country GDP, host country wage and a number of dummies on EMU and EU.

I'm choosing from two ways of doing it, I have attached Excel file with short examples of them. Could you please advice me which one is more suitable or perhaps if there's some other way to do it?

Thank you!

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The forecasting process

February 9, 2016, 3:42 am
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So, in this case, we can write the first difference in the estimate equation as d(1) or not?? in order to work directly with the original series.
What is your opinion??

For example:


Thanks for your help.

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Sign Restricted VAR

February 9, 2016, 3:47 am
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Hi kkotarac,

Thanks for your comment. Actually it is good point. To ensure orthogonality of structural shocks, some people suggest to use QR decomposition for a rejection method.
However I could find the difference. So for identification of multiple shocks, just go it again for other shocks. For a penalty function method, it is bit tricky.
I will revise it with different priors and may extend penalty function approach with multiple shocks.

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Using the last observation in a series as a scalar

February 9, 2016, 7:30 am
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Hi all,

I have a series and I'd like to take the last observation of that series, which will change over time, and use it in a simple multiplication. Eg series1 is 1,2,3,4. I want to program an equation so it'll find the number 4 and multiply it by other things. How might I do that?

Thanks!

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