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The following flow chart illustrates the procedure.
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Estimate the model in Step 4 using Ordinary Least Squares (OLS).Determine the appropriate lag structure of the model selected in Step 3.Choose DGP $i=1,\ldots,5$ from those outlined in Part 1 and Part2. Specify how deterministics enter the ARDL model.Ensure all variables are integrated of order I$(d)$ with $d
AUTOREGRESSIVE DISTRIBUTED LAG EVIEWS HOW TO
While our two previous posts in this series have been heavily theoretically motivated, here we present a step by step procedure on how to implement Part 1 and Part 2 in practice. Here, we demonstrate just how easily everything can be done in EViews 9 or higher. Again, since the distribution of this test is non-standard, the -value provided in the regression output is not compatible with this distribution and any inference must be conducted using the the -Bounds test critical values provided.In Part 1 and Part 2 of this series, we discussed the theory behind ARDL and the Bounds Test for cointegration. While the the -Bounds Test will not have changed from the Long Run Form and Bounds Test view, the -Bounds Test here reflects the - statistic associated with the CointEq regressor. Here as well we find the the -Bounds Test and the -Bounds Test tables below the regression output. If variables are indeed cointegrated, we typically expect this coefficient to be negative and highly significant. The coefficient associated with this regressor is typically the speed of adjustment to equilibrium in every period. Here, the error correction term derived as the Levels Equation earlier, is included among the regressors and is denoted as CointEq. In this view, an error correction model which estimates the speed of adjustment to equilibrium in a cointegrating relationship. When this is the case, EViews augments the table of regression estimates with a note that such variables should be interpreted as Z = Z(-1) + D(Z).Īnother view that is offered after estimation is View/ Coefficient Diagnostics/Error Correction Form. In particular, least squares estimates of coefficients associated with such variables are simultaneously estimates of the coefficients associated with as well as. Accordingly, such variables should be reinterpreted in the context of the decomposition so that they can be included in the term which arises in each of the CEC regressions. As such, EViews does not include lags and differences of such variables, but estimates them contemporaneously. Moreover, any variables suffixed by a double asterisk indicates a dynamic regressor with an optimal lag of zero. As summarized in notes below the regression output, the single asterisk indicates that the p-value associated with the relevant variable is incompatible with the t-Bounds distribution in Theorem 3.2 in PSS(2001). Note that the lag of the dependent variable in this regression will always be suffixed by a single asterisk while some other variables will be suffixed by a double asterisk. This view displays a table of least squares estimates corresponding to this CEC regression. Every ARDL model is associated with a CEC model. The View/Coefficient Diagnostics menu offers the new item Long Run Form and Bounds Test. Alternatively, if the Fixed radio button is selected, any variables not specified with will have the specified fixed number. In the latter case, if the Automatic Selection radio button is selected, EViews will fix the lags of the variables specified with and automatically select the lags for the variables which were not specified using the function. One can do this for all variables in order to estimate a specific structure, or specify some variables using the command, and others without. For instance, if the variable should possess 3 lags, then one would specify this by writing, 3). The latter can be specified via command in the Dynamic Specification edit box by replacing each variable by the Fixed Lag command LAG).
AUTOREGRESSIVE DISTRIBUTED LAG EVIEWS FULL
You may then select whether you wish EViews to automatically select the number of lags for all variables by selecting the Automatic Selection radio button, fixing the independent variable and the regressors to a uniform fixed length by selecting the Fixed radio buttons, or by taking full control of granularity and specifying a specific lag for each of the independent and regressors variables. To begin, enter the name of the dependent variable, followed by a space delimited list of dynamic regressors ( i.e., variables which will have lag terms in the model) in the Dynamic Specification edit box.