-
Login or Register
- Log in with
- You are not logged in. You can browse but not post. Login or Register by clicking 'Login or Register' at the top-right of this page. For more information on Statalist, see the FAQ.
-
Whether your variables are in logs or not does not matter for this question. It is more a matter of taste and most people seem to prefer the ec1 option. -
Thanks for your answer! Which one would you recommend when we are using log variables?Leave a comment:
-
The long-run coefficients are the same in both specifications. Only the short-run coefficients of the contemporaneous changes of the exogenous regressors differ. In addition to the help file remarks section on "Long-run coefficients expressed in time t or t-1", this difference can also be seen on slide 9 of my presentation at the Stata Conference 2016 in Chicago.Leave a comment:
-
what is the difference between the two different methods here with ec1 and ec? With the help command we get ec = estimate with depvar in first differences and display output in error-correction form and ec1 = like option ec, but parameterizes long-runcoefficients as of time t-1. How would this affect our results? Regards Jonathan Hillgren
ardl lnIMsve lnBNPSve lnPPIse lnSekeur lnVol, exog (FinD) aic ec1 lags(3 0 0 0 2), ardl lnIMsve lnBNPSve lnPPIse lnSekeur lnVol, exog (FinD) aic ec lags(3 0 0 0 2)Leave a comment:
-
The speed-of-adjustment coefficient (ADJ) is expected to fall into the range [-1, 0]. In your case, it is not statistically significantly different from -1. The interpretation would be that any deviation from the long-run relationship is fully corrected instantaneously.Leave a comment:
-
Hi. What is the interpretation for my results? I am very confused by the negative sign!Leave a comment:
-
Dear Sebastian,
My sincere apologies, as the first code is mistakenly reported. I am an idiot.
Here is the corrected version:
and thenCode:ardl mgsv rmp nc itv xgsv if ifscode==941, maxlags(5) aic maxcombs(2000000) fast matrix list e(lags) e(lags)[1,5] mgsv rmp nc itv xgsv r1 1 4 2 5 4
and the boud tests:Code:. ardl mgsv rmp nc itv xgsv if ifscode==941, ec1 lags(1 4 2 5 4) ARDL regression Model: ec Sample: 1996q2 - 2014q4 Number of obs = 75 Log likelihood = 180.45349 R-squared = .87372196 Adj R-squared = .82695232 Root MSE = .02571366 ------------------------------------------------------------------------------ D.mgsv | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- ADJ | mgsv | L1. | -.6387662 .1048283 -6.09 0.000 -.8489344 -.4285981 -------------+---------------------------------------------------------------- LR | rmp | L1. | -.0667753 .2245187 -0.30 0.767 -.5169082 .3833576 | nc | L1. | .9271816 .1467499 6.32 0.000 .6329658 1.221397 | itv | L1. | .0454188 .0377112 1.20 0.234 -.0301876 .1210251 | xgsv | L1. | .4385941 .0484641 9.05 0.000 .3414295 .5357588 -------------+---------------------------------------------------------------- SR | rmp | D1. | -.5076078 .1285887 -3.95 0.000 -.7654127 -.249803 LD. | -.4923306 .1634993 -3.01 0.004 -.8201271 -.1645342 L2D. | .0718866 .1488338 0.48 0.631 -.2265073 .3702804 L3D. | -.2320498 .1198929 -1.94 0.058 -.4724206 .008321 | nc | D1. | .7332384 .1751755 4.19 0.000 .3820326 1.084444 LD. | .4090789 .1885467 2.17 0.034 .0310655 .7870923 | itv | D1. | .2362944 .0427336 5.53 0.000 .1506186 .3219701 LD. | .1047491 .0494219 2.12 0.039 .0056642 .203834 L2D. | .0489504 .0475332 1.03 0.308 -.0463479 .1442487 L3D. | .1018636 .0452727 2.25 0.029 .0110972 .19263 L4D. | .0789433 .0392244 2.01 0.049 .000303 .1575836 | xgsv | D1. | .4191204 .1076309 3.89 0.000 .2033335 .6349074 LD. | .2391912 .113977 2.10 0.041 .010681 .4677015 L2D. | .1915766 .1213281 1.58 0.120 -.0516716 .4348248 L3D. | -.192109 .1150332 -1.67 0.101 -.4227366 .0385186 | _cons | -1.17379 .3393757 -3.46 0.001 -1.854197 -.4933826 ------------------------------------------------------------------------------
The egranger tests is as follows:Code:. estat btest Pesaran/Shin/Smith (2001) ARDL Bounds Test H0: no levels relationship F = 8.370 t = -6.093 Critical Values (0.1-0.01), F-statistic, Case 3 | [I_0] [I_1] | [I_0] [I_1] | [I_0] [I_1] | [I_0] [I_1] | L_1 L_1 | L_05 L_05 | L_025 L_025 | L_01 L_01 ------+----------------+----------------+----------------+--------------- k_4 | 2.45 3.52 | 2.86 4.01 | 3.25 4.49 | 3.74 5.06 accept if F < critical value for I(0) regressors reject if F > critical value for I(1) regressors Critical Values (0.1-0.01), t-statistic, Case 3 | [I_0] [I_1] | [I_0] [I_1] | [I_0] [I_1] | [I_0] [I_1] | L_1 L_1 | L_05 L_05 | L_025 L_025 | L_01 L_01 ------+----------------+----------------+----------------+--------------- k_4 | -2.57 -3.66 | -2.86 -3.99 | -3.13 -4.26 | -3.43 -4.60 accept if t > critical value for I(0) regressors reject if t < critical value for I(1) regressors k: # of non-deterministic regressors in long-run relationship Critical values from Pesaran/Shin/Smith (2001)
I do thank you very much for suggesting the use of the option lags() for egranger. I have to understand how to select the appropriate lag length to be used in the egranger command to check for the presence of serial correlation.Code:. egranger mgsv rmp nc itv xgsv if ifscode==941 Replacing variable _egresid... Engle-Granger test for cointegration N (1st step) = 80 N (test) = 79 ------------------------------------------------------------------------------ Test 1% Critical 5% Critical 10% Critical Statistic Value Value Value ------------------------------------------------------------------------------ Z(t) -4.280 -5.242 -4.595 -4.268 Critical values from MacKinnon (1990, 2010)
Leave a comment:
-
The observation that two different tests do not yield exactly the same results would not puzzle me. In your case, the results are also not too different from each other as both still reject the null hypothesis, say, at the 5% level.
Moreover, the (short-run) specifications differ between the ARDL bounds test and the Engle-Granger test. You might want to use the lags() option of egranger to make sure that there is no serial correlation left in the Engle-Granger residuals.
I am also puzzled how you obtained the lags(1 4 2 5 4) specification for your second ardl estimation. With maxlags(3) in your first specification, you should not obtain such high lag orders.Leave a comment:
-
Dear Sebastian Kripfganz, dear Members
I am using the ARDL command to test for the presence of a long-run relationship between macroeconomic variables. I read the notes provided here: http://www.stata.com/meeting/chicago..._kripfganz.pdf and tried to follow the suggested steps.
I first selected the most appropriate lag length trough the AIC criterion, by making use of the following command:
and on the basis of its result I run the following ardlCode:ardl mgsv rmp iad if ifscode==946, maxlags(3) aic maxcombs(2000000) fast matrix list e(lags)
I then conducted a bound test as follows:Code:. ardl mgsv rmp nc itv xgsv if ifscode==941, ec1 lags(1 4 2 5 4) ARDL regression Model: ec Sample: 1996q2 - 2014q4 Number of obs = 75 Log likelihood = 180.45349 R-squared = .87372196 Adj R-squared = .82695232 Root MSE = .02571366 ------------------------------------------------------------------------------ D.mgsv | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- ADJ | mgsv | L1. | -.6387662 .1048283 -6.09 0.000 -.8489344 -.4285981 -------------+---------------------------------------------------------------- LR | rmp | L1. | -.0667753 .2245187 -0.30 0.767 -.5169082 .3833576 | nc | L1. | .9271816 .1467499 6.32 0.000 .6329658 1.221397 | itv | L1. | .0454188 .0377112 1.20 0.234 -.0301876 .1210251 | xgsv | L1. | .4385941 .0484641 9.05 0.000 .3414295 .5357588 -------------+---------------------------------------------------------------- SR | rmp | D1. | -.5076078 .1285887 -3.95 0.000 -.7654127 -.249803 LD. | -.4923306 .1634993 -3.01 0.004 -.8201271 -.1645342 L2D. | .0718866 .1488338 0.48 0.631 -.2265073 .3702804 L3D. | -.2320498 .1198929 -1.94 0.058 -.4724206 .008321 | nc | D1. | .7332384 .1751755 4.19 0.000 .3820326 1.084444 LD. | .4090789 .1885467 2.17 0.034 .0310655 .7870923 | itv | D1. | .2362944 .0427336 5.53 0.000 .1506186 .3219701 LD. | .1047491 .0494219 2.12 0.039 .0056642 .203834 L2D. | .0489504 .0475332 1.03 0.308 -.0463479 .1442487 L3D. | .1018636 .0452727 2.25 0.029 .0110972 .19263 L4D. | .0789433 .0392244 2.01 0.049 .000303 .1575836 | xgsv | D1. | .4191204 .1076309 3.89 0.000 .2033335 .6349074 LD. | .2391912 .113977 2.10 0.041 .010681 .4677015 L2D. | .1915766 .1213281 1.58 0.120 -.0516716 .4348248 L3D. | -.192109 .1150332 -1.67 0.101 -.4227366 .0385186 | _cons | -1.17379 .3393757 -3.46 0.001 -1.854197 -.4933826 ------------------------------------------------------------------------------
The results of the boun test confirm the presence of a long-run relationship, but I am puzzled by the result of the cointegration test performed as follows:Code:. estat btest Pesaran/Shin/Smith (2001) ARDL Bounds Test H0: no levels relationship F = 8.370 t = -6.093 Critical Values (0.1-0.01), F-statistic, Case 3 | [I_0] [I_1] | [I_0] [I_1] | [I_0] [I_1] | [I_0] [I_1] | L_1 L_1 | L_05 L_05 | L_025 L_025 | L_01 L_01 ------+----------------+----------------+----------------+--------------- k_4 | 2.45 3.52 | 2.86 4.01 | 3.25 4.49 | 3.74 5.06 accept if F < critical value for I(0) regressors reject if F > critical value for I(1) regressors Critical Values (0.1-0.01), t-statistic, Case 3 | [I_0] [I_1] | [I_0] [I_1] | [I_0] [I_1] | [I_0] [I_1] | L_1 L_1 | L_05 L_05 | L_025 L_025 | L_01 L_01 ------+----------------+----------------+----------------+--------------- k_4 | -2.57 -3.66 | -2.86 -3.99 | -3.13 -4.26 | -3.43 -4.60 accept if t > critical value for I(0) regressors reject if t < critical value for I(1) regressors k: # of non-deterministic regressors in long-run relationship Critical values from Pesaran/Shin/Smith (2001)
which seems to point for the presence of an extremely weak cointegration (very close to the 10% bound)Code:. egranger mgsv rmp nc itv xgsv if ifscode==941 Replacing variable _egresid... Engle-Granger test for cointegration N (1st step) = 80 N (test) = 79 ------------------------------------------------------------------------------ Test 1% Critical 5% Critical 10% Critical Statistic Value Value Value ------------------------------------------------------------------------------ Z(t) -4.280 -5.242 -4.595 -4.268 Critical values from MacKinnon (1990, 2010)
I am aware that the ardl command tests for the presence of a long-run relationship between variables, whereas the egranger to test for the cointegration. Yet, I was surprises that the two procedures lead to very different results and I was wondering what could be the explanation.
I do apologise if the question is silly.
Many many thanks for your kind attention.
Leave a comment:
-
Try to restrict the maximum number of lags for lnBNPeuro to 2 with the maxlags() or lags() option, e.g.
Code:ardl lnXsve lnBNPeuro lnPPIes lnEursek lnVol , exog(FinD) aic maxcombs(2500) maxlags(. 2 . . .)
Leave a comment:
-
Hi again, I found that the regression didn´t work and decided to only test ln of the values like in this regression "ardl lnXsve lnBNPeuro lnPPIes lnEursek lnVol , exog (FinD) aic maxcombs(2500)" and recieved this message note: L3.lnBNPeuro omitted because of collinearity
Collinear variables detected.
I believe that this could be so because BNPeuro is a interpolated value, (quarterly values to monthly). Is there any way to deal with this problem? best regards and happy easterLeave a comment:
-
Sebastian Kripfganz thanks again, with the following regression ardl rXsve rBNPeuro rEursek rVol rPPIse, exog (FinD) aic maxcombs(2500)
I get these resultsLeave a comment:
-
You can use the exog() option to attach a dummy variable to the regression.Leave a comment:
-
Sebastian Kripfganz It helped and thank you very much. I cant see any details in help ardl how to process a dummy variable in our regression, Do you know how to implement this effect?Leave a comment:
-
You need to use the maxcombs() option top allow for a larger number of lag permutations, e.g.
Please see help ardl for details.Code:ardl rXsve rBNPeuro rEursek rVol rPPIes, aic maxcombs(2500)
Leave a comment:
Leave a comment: