[Maruf Ahmed]

Dear Adviser,
Thank you for your response.It was really helpful.
I am using 4 variables: dependent variable:lnTB_t and independent variables:lnREX_t, lnRGDP_us_t,lnRGDP_BD_t.First, i want to estimate the following model with Pearsen's(2001) LINEAR ARDL or bounds testing approach:

ΔLnTBi;t =a0 +summation(k=1 to n)b0k ΔLnTBi;t−k +summation(k=0 to n)c0k ΔLnYUS;K+summation(k=0 to n)d0k ΔLnYi;t−k+summation(k=0 to n)e0kΔLnREXi;t−k+ λ1 LnTBi;t−1 + λ2 LnYUS;t−1+λ3 Ln Yi;t−1 + λ4LnREXi;t−1 + μt

I want to impose a maximum of eight lags on each first differenced variable and use Akaike's Information Criteria(AIC) to select the optimum lags. Explicitly, i want to find the short-run coefficient estimates, long-run estimate and diagnostic statistics that include: F, ECM_t-1, LM, RESET, CUSM, CUSM^2, Rbar^2

1))) Please give me the stata codes with which i can carry out the tests and find the results.

Secondly, i want to decompose the real rexchange rate variable lnREX_t as partial sum as: lnREX_t= lnRER_0+ pos+ neg, where pos=summation( J=1 to t) max(ΔLnREXj, 0) ;;; neg=summation( J=1 to t) min(ΔLnREXj, 0)
2))) what are the commands of generating these variables?
Followig Shin et. al (2013) , i replace lnREX_t by pos and neg variables and estimate the following NON-LINEAR ARDL model:

ΔLnTBi;t = a0+summation(k=1 to n1)b0kΔLnTBi;t−k +summation(k=0 to n2)c0kΔLnYUSt−k+summation(k=0 to n3)d0kΔLnYit−k+summation(k=0 to n4)e0kΔPOSt−k+summation(k=0 to n5)f0kNEGt−k+θ0LnTBi;t−1+ θ1LnYUSt−1+ θ2LnYit−1+ θ3POSt−1 +θ4NEGt−1 + ξt

I want to impose a maximum of eight lags on each first differenced variable and use Akaike's Information Criteria(AIC) to select the optimum lags. Explicitly, i want to find the short-run coefficient estimates, long-run estimate and diagnostic statistics that include: F, ECM_t-1, LM, RESET, CUSM, CUSM^2, Rbar^2.
3)))Please give me these stata codes explicitly with which i can carry out the tests and find the results ( I have no idea how to carry out this part)
please help me in this regard especially.I am using time series data spanning 1986-2014. It will be a great help if you provide me with the codes. Grateful to you.

Regards---
Maruf Ahmed

[Sebastian Kripfganz]

Maruf Ahmed:
You can use the ardl option regstore() to store the underlying estimates from regress, e.g.

ardl del_ln_tb_t del_ln_RGDP_US_t del_ln_RGDP_BD_t del_ln_rer_t, regstore(reg_estimates)
estimates restore reg_estimates
estimates replay reg_estimates

The second line allows the subsequent use of all familiar postestimation commands that are available for regress. The last line will display the familiar regress estimation output including F-statistic, R², adjusted R², ...

For CUSUM and CUSUM² results you might want to have a look at the user-written cusum6 command:

ssc describe cusum6

The error message that results with your second model could potentially be avoided by using ardl with the maxcombs(#) option, where # should be a number larger than 500, or by restricting the maximum number of lags with the maxlags() option.

The error-correction representation can be obtained with the ec option, potentially combined with the option minlag1 to obtain the ECM in period t-1.

Please consult the help file of ardl regarding all of these options.

[Maruf Ahmed]

Dear Adviser,
I am using Stata 11 SE, and trying to perform a Non-linear ARDL model with stata.The non linear variables are constructed in the form:
pos= summation(j=1 to t) max( del_lnrer_j, 0) ;neg= summation(j=1 to t) min(del_lnrer_j, 0).
First, I want to estimate the linear ARDL model:

ΔLnTBi;t =a0 +summation(k=1 to n) b0k ΔLnTBi;t−k +summation(k=0 to n) c0k ΔLnYUS;K+summation(k=0 to n) d0k ΔLnYi;t−k+summation(k=0 to n)e0kΔLnREXi;t−k+ λ1 LnTBi;t−1 + λ2 LnYUS;t−1+λ3 Ln Yi;t−1 + λ4LnREXi;t−1 + μt

The command i am writing is:
ardl del_ln_tb_t del_ln_RGDP_US_t del_ln_RGDP_BD_t del_ln_rer_t, this time i get the result,

(1)What should i type as commands to estimate the model. Besides, i want to find"" F, ECM t-1, LM, RESET, CUSUM, CUSUM^2, R bar^2".
to find these what are the commands?
Then i want to estimate the non-linear ARDL model :

ΔLnTBi;t = a0+summation(k=1 to n1)b0kΔLnTBi;t−k +summation(k=0 to n2)c0kΔLnYUSt−k+summation(k=0 to n3)d0kΔLnYit−k+summation(k=0 to n4)e0kΔPOSt−k+summation(k=0 to n5)f0kNEGt−k+θ0LnTBi;t−1+ θ1LnYUSt−1+ θ2LnYit−1+ θ3POSt−1 +θ4NEGt−1 + ξt

The command i am writing is:
ardl del_ln_tb_t del_ln_RGDP_US_t del_ln_RGDP_BD_t pos neg

This time i DO NOT get the result, But after clicking enter""""# of lag permutations (2500) exceeds setting of 'maxcombs' (500)
r(9);""", this line is shown.
How to get rid of this problem? please help me in this regard especially.I am using time series data spanning 1986-2014.
Please, give me some suggestions and commands to get rid of this problem.

Regards---
Maruf Ahmed

[Sebastian Kripfganz]

Elisa:
The -- below the variable name lngdp in the first column of your first output table refers to the "0-th order" lag of that variable. It is equivalent to L0.lngdp.

To obtain the F-statistic from the bounds test, you have to estimate the model in the error-correction representation and then invoke ardl with the btest option:

ardl lnco2 lngdp lngdp2 lnen, aic ec btest

or

ardl lnco2 lngdp lngdp2 lnen, aic ec
ardl, btest

As you can see from your first output table, the AIC has chosen an optimal lag order of zero for the variables lngdp2 and lnen. These variables therefore only enter the long-run relationship but do not appear in the short-run section. Conditional on the long-run relationship, there are indeed no additional short-run effects of these variables. Intuitively, if there is only one coefficient associated with these variables in the ARDL model, you cannot separately identify short-run and long-run effects. You would need additional lags in the ARDL representation of the model.

You can get from your second-last table to the last table by multiplying all the long-run coefficients by the negative of the ADJ coefficient. Please see the ardl help file section about "Long-run coefficients expressed in time t or t-1" for some further remarks about the relation between ARDL and EC coefficients.

[Elisa Nebres]

Hi Sebastian. I am new to econometrics and still learning ARDL.
I found your package and used it to do a cointegration test for the following model:
lnCO2= b0+b1lnGDP+b2lnGDP^2+b3lnEN+e

running ardl lnco2 lngdp lngdp2 lnen, aic automatically runs the regression using the optimal lag form AIC criterion. I get a table with the variables having different lag levels, and for a certain variable there is a "--". What does this mean?
How would I know if the variables are cointegrated? ( I know i must compare the F-stat to the Pesaran table, if it is within the bound then the variables are cointegrated. But I do not see a F-Stat)

once they are found cointegrated, I run the ecm by typing ardl lnco2 lngdp lngdp2 lnen, aic ec regstore(ecreg)
This gives me the SR and LR variable coefficients. Based from the table, the SR only has one independent variable indicated, the other 2 are missing. Does this mean there are no SR effects to the depvar from the other 2? but in the LR, all the variables are indicated in the table even if they are not significant.

lastly, when I regress ecreg, how does this differ from the first table I get?

The results are attached. I hope you could help me.

Thank you.

[James Rossi]

Thanks for the quick response, I'll check out the QML estimator.

[Sebastian Kripfganz]

No, you cannot estimate this model with the ardl command. ardl is for a single time series while you want to estimate a panel model. See my comment #80 above, in particular in the context that your time dimension is small.

If you would like to use my QML estimator for dynamic panel models, keep in mind that it only allows for one lag of the dependent variable (instead of p), but given your small time dimension you need to be restrictive regarding the choice of p in any case.

In general, it is advisable to include time effects and state effects (fixed or random effects). Note that you cannot include both time dummies and other dummy variables for seasonal trends together (if they affect all states jointly) because of perfect collinearity.

[James Rossi]

I'm trying to examine the effects of various industries (by two digit NAICS classification with a few aggregations measured by GDP) and personal income on several different socioeconomic indicators (e.g. voter turn-out, crime rates, volunteerism, charitable giving, patents, etc.). I have state-level observations for the contiguous U.S. states (N=48) with different time lengths depending on the independent variable (generally T between 8 to 14). Obviously, I plan on running a separate model for each independent variable.

So as a rough estimate of my model, think something like this:
Y_i,t = B Agr_i,t + B Manuf_i,t+ ... + B Inc_i,t + ... B Agr_i,t-p + B Manuf_i,t-p + ... + B Inc_i,t-p +Y_i,t-p + e_i,t
Where i is the i^th observation (i.e. state), t is the t^th year, and p is the maximum lag length. Some specifications may also include dummy variables to account for seasonal trends (e.g. campaign spending would require one).

Can I estimate this model using ARDL? Do I need to include time and/or state dummies (or, more importantly, should I or should I not)? Do I need to address any potential collinearity issues in the explanatory variables before running ARDL?

As a sidenote, am I better using the QML version of the ARDL estimator?

I want to use the outputs of these regressions to help justify the linking of certain industries and the household sector to the production of different forms of capital (e.g. human, social, cultural, political, etc.) in a dynamic social accounting matrix.

[Sebastian Kripfganz]

It seems to me that you are specifying the wrong name with forecast estimates. It should be the name that you have specified with the regstore() option of ardl:

forecast estimates ardl_res

[Jingyang liu]

@ Sebastian,
Hi, I am using ARDL to do forecast vis Stata. I confront some problems. Could you please help me tackle it? Thanks in advance.

Here is my command code

ardl lrcredit lrgdp lt_re net_bank_ro if qtr>tq(2001Q1), ec aic maxlag(2,2,2,2) regstore(ardl_res)
estimates store reg_res
estimates restore ardl_res
forecast creat creditardl, replace
forecast estimates creditardl
forecast solve, prefix(ars_) begin(tq(2002Q2)) static


. forecast estimates creditardl
estimation result creditardl not found
Could not restore estimation result creditardl. Use estimates dir to list results stored in memory. If the
estimation results are saved on disk, use forecast estimates using along with the name of a file containing
saved estimation results.

when I change the estimates to reg_res

. forecast estimates reg_res
option names() required
Because the dependent variable specified with ardl included time-series operators, you must specify an
alternative name in the names() option of forecast estimates. ardl reports 1 dependent variable, so you must
specify 1 name in names().

It's my first time to use ARDL and forecast, maybe it is a stupid question, but I am looking forward to your reply.

JY

[Sebastian Kripfganz]

Dear Iftikhar,

The error message seems to be related to the iterate() option of the nlcom command which is used by ardl to compute the error-correction coefficients and standard errors. With 9 variables and possibly a couple of lags for each, your number of parameters is very large (too large?) relative to your sample size of 35 observations. You might have to reduce the number of variables and/or the number of lags, for example with the maxlags() option of ardl.

Also, I would expect that already the OLS results (ardl without the ec option) are not very meaningful given your large number of coefficients.

[iftikhar yasin]

Sir Sebastian Kripfganz,

I've an issue in running ARDL in STATA , please help me.

As soon as I use " ec " option in ARDL in STATA SE , I receive an error

"Maximum number of iterations exceeded" r(498).

Please help me what to do now ????

I've 9 variables and about 35 observations, and using STATA SE.

Regards

[Georg Neuschaeffer]

Hey all,

regarding the 5 cases presented by Pesaran et al. (2001) I have difficulties to to select the right one. When no constant or time trend is included it is clear, but I could not find a reliable source which case (restricted intercept and/or time trend) should be preferred. I checked the sources given in the ARDL command, but they only help partially. Does anybody have a "rule of thumb" or a source in which it is explained how to distinguish between the restricted and the unrestricted cases with regard to the interpretation of the results? I could not find any empirical paper yet in which this issue is further discussed.

I am thankful for any help.

[Muhammad Bah]

Thanks Sebastian, appreciate your help

[Sebastian Kripfganz]

Yes, it can. Please see the remarks section "Long-run coefficients expressed in time t or t-1" in the ardl help file.