Dear Statalisters and users of our ardl command,

We are happy to announce that a major update of the ardl command (version 1.0.0) is available for installation from my website:

If you are already using the command, type the following to upgrade to the latest version:

The most important change concerns the Pesaran, Shin, and Smith (2001) bounds testing procedure. The bounds test is now performed by the

The bounds test for the t-statistic is now also displayed in cases 2 and 4 with restricted deterministic model components. The critical values are identical to those from cases 3 and 5, respectively. (See again the manuscript on my website referenced below for details.)

Here is a basic example. The first regression shows the ARDL model results (level representation), the second regression displays the error correction representation of the same model, and the final output shows the bounds test results with our new finite-sample critical values and approximate p-values:

Please see the Stata help files for details:

The update includes the following additional improvements:

Please use this new Statalist topic for any questions concerning the new version of the command. A Statalist discussion about previous versions of the ardl command can be found here: ARDL in Stata.

We are happy to announce that a major update of the ardl command (version 1.0.0) is available for installation from my website:

Code:

. net install ardl, from(http://www.kripfganz.de/stata/)

Code:

. adoupdate ardl, update

__new postestimation command__estat ectest. Instead of the Pesaran, Shin, and Smith (2001) near-asymptotic critical values and the Narayan finite-sample critical values, the new command now displays our__more precise Kripfganz and Schneider (2018) critical values__. These critical values have been obtained with response surface regressions based on large-scale simulations and they are available__for any sample size and any number of regressors__, and they also properly account for the number of short-run coefficients in the model. In addition, the command now produces__approximate p-values__that allow for an easy test decision given a desired significance level. Both finite-sample and asymptotic critical values / p-values are available. The response surface methodology is explained in detail in a manuscript available on my website (see the references below) that also documents the regression results used to predict the critical values and the p-values. (The previous postestimation command estat btest is now obsolete but previous do-files continue to work!)The bounds test for the t-statistic is now also displayed in cases 2 and 4 with restricted deterministic model components. The critical values are identical to those from cases 3 and 5, respectively. (See again the manuscript on my website referenced below for details.)

Here is a basic example. The first regression shows the ARDL model results (level representation), the second regression displays the error correction representation of the same model, and the final output shows the bounds test results with our new finite-sample critical values and approximate p-values:

Code:

. webuse lutkepohl2 (Quarterly SA West German macro data, Bil DM, from Lutkepohl 1993 Table E.1) . ardl ln_inv ln_inc ln_consump ARDL(1,0,2) regression Sample: 1961q1 - 1982q4 Number of obs = 88 F( 5, 82) = 1990.82 Prob > F = 0.0000 R-squared = 0.9918 Adj R-squared = 0.9913 Log likelihood = 158.83176 Root MSE = 0.0412 ------------------------------------------------------------------------------ ln_inv | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- ln_inv | L1. | .8432219 .0588646 14.32 0.000 .7261214 .9603224 | ln_inc | -.4477328 .3143463 -1.42 0.158 -1.073068 .1776022 | ln_consump | --. | 1.9247 .5487929 3.51 0.001 .8329761 3.016424 L1. | -.3682414 .5622263 -0.65 0.514 -1.486689 .7502058 L2. | -.9598887 .4300221 -2.23 0.028 -1.81534 -.1044377 | _cons | -.0460065 .0706528 -0.65 0.517 -.1865575 .0945445 ------------------------------------------------------------------------------ . ardl ln_inv ln_inc ln_consump, ec ARDL(1,0,2) regression Sample: 1961q1 - 1982q4 Number of obs = 88 R-squared = 0.2228 Adj R-squared = 0.1754 Log likelihood = 158.83176 Root MSE = 0.0412 ------------------------------------------------------------------------------ D.ln_inv | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- ADJ | ln_inv | L1. | -.1567781 .0588646 -2.66 0.009 -.2738786 -.0396776 -------------+---------------------------------------------------------------- LR | ln_inc | -2.855838 2.522611 -1.13 0.261 -7.874116 2.16244 | ln_consump | --. | 3.805188 2.600013 1.46 0.147 -1.367067 8.977442 -------------+---------------------------------------------------------------- SR | ln_consump | D1. | 1.32813 .410621 3.23 0.002 .5112741 2.144986 LD. | .9598887 .4300221 2.23 0.028 .1044377 1.81534 | _cons | -.0460065 .0706528 -0.65 0.517 -.1865575 .0945445 ------------------------------------------------------------------------------ . estat ectest Pesaran, Shin, and Smith (2001) bounds test H0: no level relationship F = 4.069 Case 3 t = -2.663 Finite sample (2 variables, 88 observations, 2 short-run coefficients) Kripfganz and Schneider (2018) critical values and approximate p-values | 10% | 5% | 1% | p-value | I(0) I(1) | I(0) I(1) | I(0) I(1) | I(0) I(1) ---+------------------+------------------+------------------+----------------- F | 3.215 4.193 | 3.887 4.957 | 5.395 6.637 | 0.041 0.111 t | -2.564 -3.224 | -2.875 -3.558 | -3.484 -4.199 | 0.081 0.253 do not reject H0 if both F and t are closer to zero than critical values for I(0) variables (if p-values > desired level for I(0) variables) reject H0 if both F and t are more extreme than critical values for I(1) variables (if p-values < desired level for I(1) variables)

Code:

. help ardl . help ardl postestimation . help ardlbounds

- The Mata-based lag selection algorithm that was introduced in Version 0.7.0 as an option is now the default. This speeds up the ardl command substantially (by more than factor 10).
- Improved display of the header above the estimation table.
- Improved help file.
- Some bug fixes.

- Bounds test:
- Pesaran, M. H., Y. Shin, and R. J. Smith (2001). Bounds testing approaches to the analysis of level relationships.
*Journal of Applied Econometrics*16 (3), 289–326, doi.org/10.1002/jae.616

- Pesaran, M. H., Y. Shin, and R. J. Smith (2001). Bounds testing approaches to the analysis of level relationships.
- New critical value bounds and approximate p-values for the bounds test:
- Kripfganz, S. and D. C. Schneider (2018). Response surface regressions for critical value bounds and approximate p-values in equilibrium correction models.
*Manuscript*, University of Exeter and Max Planck Institute for Demographic Research, www.kripfganz.de/research/Kripfganz_Schneider_ec.html

- Kripfganz, S. and D. C. Schneider (2018). Response surface regressions for critical value bounds and approximate p-values in equilibrium correction models.
- Stata command (some of the slides are now outdated):
- Kripfganz, S. and D. C. Schneider (2016). ardl: Stata module to estimate autoregressive distributed lag models.
*Presented July 29, 2016, at the Stata Conference*, Chicago, fmwww.bc.edu/repec/chic2016/chicago16_kripfganz.pdf

- Kripfganz, S. and D. C. Schneider (2016). ardl: Stata module to estimate autoregressive distributed lag models.
- Mata implementation of the fast lag selection algorithm:
- Kripfganz, S., and D. C. Schneider (2017). Speeding up the ARDL estimation command: A case study in efficient programming in Stata and Mata.
*Presented June 23, 2017, at the German Stata Users Group Meeting*, Berlin, repec.org/dsug2017/Germany17_Schneider.pdf

- Kripfganz, S., and D. C. Schneider (2017). Speeding up the ARDL estimation command: A case study in efficient programming in Stata and Mata.

Please use this new Statalist topic for any questions concerning the new version of the command. A Statalist discussion about previous versions of the ardl command can be found here: ARDL in Stata.

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