Estimate an equation with autoregressive distributed lags using least squares.
Syntax
ardl(options) linear_regs [@ static_regs] [@asy dual_asymmetric_regs] [@asylr long_run_asymmetric_regs] [@asysr short_run_asymmetric_regs]
The linear_regs specification is required:
• The linear_regs list should be the dependent variable followed by a list of linear distributed-lag regressors.
The remaining specifications are optional
• static_regs should be a list of static regressors, not including a constant or trend term.
• dual_asymmetric_regs are distributed-lag regressors which are asymmetric both in the short-run and long-run.
• long_run_asymmetric_regs regressors are distributed lag-regressors which are asymmetric in the long-run but symmetric in the short-run.
• short_run_asymmetric_regs are asymmetric regressors which are distributed lag-regressors which are asymmetric in the short-run but symmetric in the long-run.
You may specify the lag for an individual distributed-lag variable using the “@fl(variable, lag)” syntax. For instance, if the variable X should use 3 lags, irrespective of the fixed or automatic lag settings, you may specify this by entering “@fl(x, 3)” in the regressor list.
Options
determ=arg (default = “rconst”) | Johansen deterministic trend type: “none” (no deterministics), “rconst” (restricted constant and no trend), “uconst” (unrestricted constant and no trend), “rtrend” (unrestricted constant and restricted trend, “utrend” (unrestricted constant and unrestricted trend). |
trend=arg (deprecated) | Johansen deterministic trend type: Note: this is a deprecated s option which handles a subset of cases covered by the “determ=” option: “none” (no deterministics), “const” (restricted constant and no trend, default), “uconst” (unrestricted constant and no trend), “linear” (unrestricted constant and restricted trend, “ulinear” (unrestricted constant and unrestricted trend). |
fixed | Do not use automatic selection for lag lengths. This option must be used with the “deplags=” and “reglags=” options. |
deplags=int (default = 4) | Set the number of lags for the dependent variable to int. If automatic selection is used, this sets the maximum number of possible lags. If fixed lags are used (the fixed option is set), this fixes the number of lags. |
reglags=int (default = 4) | Set the number of lags for the explanatory variables (dynamic regressors) to int. If automatic selection is used, this sets the maximum number of possible lags. If fixed lags are used (the fixed option is set), this fixes the number of lags for each regressor. |
ic=key (default = “aic”) | Set the method of automatic model selection. key may take values of “aic” (Akaike information criterion, default), “bic” (Schwarz criterion), “hq” (Hannan-Quinn criterion) or “rbar2” (Adjusted R-squared, not applicable in panel workfiles). |
nodf | Do not perform degree of freedom corrections in computing coefficient covariance matrix. The default is to use degree of freedom corrections. |
coef=arg | Specify the name of the coefficient vector (if specified by list); the default behavior is to use the “C” coefficient vector. |
prompt | Force the dialog to appear from within a program. |
p | Print results. |
Examples
wfopen http://www.stern.nyu.edu/~wgreene/Text/Edition7/TableF5-2.txt
ardl(deplags=8, reglags=8) log(realcons) log(realgdp) @ @expand(@quarter, @droplast)
This example uses data from Greene (2008, page 685), containing quarterly US macroeconomic variables between 1950 and 2000. The first line of this example downloads the data set, the second line creates an equation object and estimates an ARDL model with the log of real consumption as the dependent variable, and the log of real GDP as a dynamic regressor. Quarterly dummy variables are included as static regressors. Automatic model selection is used to determine the number of lags of log(realcons) and log(realgdp).
ardl(deplags=3, reglags=3, fixed) log(realcons) log(realgdp) @ @expand(@quarter, @droplast)
This command estimates a second model, replicating Example 20.4 from Greene, estimating a model fixed at 3 lags of the dependent variable and 3 lags of the regressor.
wfopen oecd.wf1
ardl(fixed, deplags=1, reglags=1) log(cons) log(inf) log(inc)
This example estimates a panel ARDL model using the workfile OECD.wf1. This model replicates that given in the original Pesaran, Shin and Smith 1999 paper. Model selection is not used to choose the optimal lag lengths, rather a fixed single lag of both the dependent variable and the regressor are used.
Cross-references
See
“Linear and Nonlinear ARDL” for further discussion.