I cant run xtdcce2 command someone help for that

[Eyup TANIL]

I am working on Panel Data , N:25 T:15. ı need to perform CS-ARDL with xtdcce2 code. I run this code '' xtdcce2 dy1 dx1 dx2 dx3 dx4 dly1 dlx1 dlx2 dlx3 dlx4, cr( dly1 dlx1 dlx2 dlx3 dlx4) reportc lr( ly1 lx1 lx2 lx3 lx4) cr_lags(2)'' but the program give me error ;

Units (code) to be removed due to insufficient numbers of observations: 1 2 3 4 5 6 7 8 9 10 11 12
> 13 14 15 16 17 18 19 20 21 22 23 24 25

No observations left.
r(2001);

Whats wrong ?

[JanDitzen]

xtdcce2 points out that you do not have enough data to run the model. In detail, you are trying to run a model with 13 explanatory variables and 15 cross-section averages (3 x 5), thus in total your model would have 28 variables for each regression on a cross-sectional level. However you have only 15 time periods and thus xtdcce2 cannot run the model.

[Eyup TANIL]

ı understood it. Firstly thank you profoser for reply my message. ı would like to know, Can we estimate CS-ARDL-PMG in stata with xtdcce2 ? ıf we cant, is there alternative command to estimate
I am researching how european agriculture subsidies affect total input costs for my thesis. ı cant expand T for same important limitiations. My variables entegrated I(0) and I(1). What you suggest me about that. thank you

[JanDitzen]

You can pool the coefficients with xtdcce2 using the option pooled(). However the problem will remain because the PMG estimator requires unit specific estimates as well. Estimating the unit specific coefficients with that little periods of time is hard. In a sense you run a time series regression for each cross-sectional unit. For those regressions you have only 15 units available - I would not recommend any estimation like this.

My advice would be to pool all coefficients and use one of the variance covariance estimators which do not rely on the difference between the unit and mean group estimates. However you need to discuss this critically in your paper and inference using such a model will be on weak grounds.

[Eyup TANIL]

thank profoser very much for your interest, ı will consult my thesis advisor.

[merve kaya]

Hi everyone,

I'm trying to estimate ardl model with xtdcce2 command.
xtdcce2 y, lr(l.y x1) lr_options(ardl) cr(y x1) cr_lags(K)

I'm not sure of that, K is the maximum lag length or the number of lags? If K is the maximum lag length, how can I define the number of lags should model take?
For example, I want to take 3 lags of dependent and independent variables. How can take 3 lags of them?

[JanDitzen]

cr_lags() defines the number of lags of the cross-section averages, not the lags of the model. The cross-section averages approximate the common factors. Chudik & Pesaran (2015) show proof that k=[T^(1/3)] sufficiently approximates the common factors, where [] is the floor.

[merve kaya]

Dear Ditzen thank you for your quick reply,

But I didn't understand, which command should I use for taking lags of model.

[JanDitzen]

You define the lags using time series operators. For example:

xtdcce2 y, lr(l.y x1) lr_options(ardl) cr(y x1) cr_lags(K)

is an ARDL(1,0) model. For a ARDL(1,1) you would use (additional lag in bold):

xtdcce2 y, lr(l.y x1 L.x1) lr_options(ardl) cr(y x1) cr_lags(K)

[merve kaya]

Dear Ditzen,

Thank you very very much.

[merve kaya]

Dear Ditzen, I have a question about xtdcce2 code. Whatever model I estimate with this code, I take insignificant coefficients. Generally, I use T: 30~35, N: 30. What could be the possible cause of these results?

[JanDitzen]

Hi Merve,
my guess is that there is no effect and thus coefficients are insignificant. If you run a MG estimation, I would suggest to analyse the individual coefficients.
Best,
Jan

[Eyup TANIL]

You define the lags using time series operators. For example:

xtdcce2 y, lr(l.y x1) lr_options(ardl) cr(y x1) cr_lags(K)

is an ARDL(1,0) model. For a ARDL(1,1) you would use (additional lag in bold):

xtdcce2 y, lr(l.y x1 L.x1) lr_options(ardl) cr(y x1) cr_lags(K)

Dear Ditzen,

"What criteria should we use to determine the lag lengths of an ARDL model? In other words, how should we decide between ARDL(1,0) or ARDL(1,1)? I have a series with T=28 and N=4, and three independent variables. I ran the following model: xtdcce2 efc, lr(l.efc fdi gdp gdp2) lr_options(ardl) cr(_all) cr_lags(2), which is ARDL(1,0,0,0). Could I have also estimated an ARDL(1,1,1,1) model? How should we decide between these two models or determine the ideal ARDL model?"

and my another questions,


"In the YouTube videos I watched, some people took the first difference of the variables and ran the model xtdcce2 efc, lr(l.efc fdi gdp gdp2) lr_options(ardl) cr(_all) cr_lags(2). In this case, should we also take the first difference of the variables when calculating CS-ARDL? Or how should we decide between these two approaches?"

[JanDitzen]

Hi Eyup TANIL,
there is - to the best of my knowledge - no guidance on the lag length of ARDL models in panel data models with cross-sectional dependence. My advice would be to determine the lag structure for each country individually and then compare results with different lag lengths.

Given the size of N, I would strongly advice against the use of xtdcce2, respectively the CCE estimator. The estimator and the concept of strong cross-sectional dependence are only valid if N and T are large. N=4 is certainly not large!

I do not know the Youtube videos or people who record them. In general, if you estimate an ECM model, you should take the first difference of the variables.

Hope this helps.

[Eyup TANIL]

Hi Eyup TANIL,
there is - to the best of my knowledge - no guidance on the lag length of ARDL models in panel data models with cross-sectional dependence. My advice would be to determine the lag structure for each country individually and then compare results with different lag lengths.

Given the size of N, I would strongly advice against the use of xtdcce2, respectively the CCE estimator. The estimator and the concept of strong cross-sectional dependence are only valid if N and T are large. N=4 is certainly not large!

I do not know the Youtube videos or people who record them. In general, if you estimate an ECM model, you should take the first difference of the variables.

Hope this helps.

Dear Mr. Ditzen,

I have made updates to my dataset, and currently I have N:15 T:46 panel data. I have 4 variables;1 dependent and 5 independent variables. Among these variables, the dependent variable and 2 independent variables are integrated of order 1 (I(1)), and one variable is integrated of order 0 (I(0)). There is cross-sectional dependence.

When modeling these variables using the (CS-ARDL) xtdcce2 command, I am wondering whether to use the differences or the raw data. What are the differences or advantages between these two methods?

Additionally, you mentioned "My advice would be to determine the lag structure for each country individually and then compare results with different lag lengths." How can I perform this process ?

I run a model as like below; ı run CS-ARDL (1 0 0 0) with difference of all variables. results coeffient good for the model. but what does mean CD-istatistics and prob> F. is this model biased and have problem.