Object Reference : Object View and Procedure Reference : Equation
Estimate an equation with autoregressive distributed lags using least squares.
equation.ardl(options) dynamic_eqn @ static_regs
The dynamic equation should be the dependent variable followed by a list of dynamic regressors (regressors with lags). The static regressors should be a list of static regressors, not including a constant or trend term.
Do not use automatic selection for lag lengths. This option must be used with the deplags= and reglags= options.
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.
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.
Set the trend specification. key may take values of “const” (include a constant, default), “none” (do not include a trend or constant), or “linear” (include both a constant and a linear trend).
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).
Do not perform degree of freedom corrections in computing
coefficient covariance matrix. The default is to use degree
of freedom corrections.
Specify the name of the coefficient vector (if specified by
list); the default behavior is to use the “C” coefficient vector.
Force the dialog to appear from within a program.
Print results.
wfopen http://www.stern.nyu.edu/~wgreene/Text/Edition7/TableF5-2.txt
equation eq01.ardl(deplags=8, reglags=8) log(realcons) log(realgdp) @ @expand(@quarter, @droplast)
show eq01.icgraph
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).
The final line of code displays a graph showing the Akaike information criteria (the default selection method) for each of the models estimates.
equation eq02.ardl(deplags=3, reglags=3, fixed) log(realcons) log(realgdp) @ @expand(@quarter, @droplast)
show eq02.cointrep
These lines estimate 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, and then viewing the cointegration representation of the estimation, as well as the long-run form of the coefficient estimates.
wfopen oecd.wf1
equation eq03.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.
See “Autoregressive Distributed Lag (ARDL) Models” for further discussion.