PcGets offers a wide range of options that can be set by the user to implement their desired selection strategy. Section 15.2 describes those available in the model settings dialog box, which determines which selection and evaluation options are implemented during the search. Then section 15.4 discusses all the significance levels that the expert user can select for each of the search and evaluation statistics. Model Settings are accessed from the Model menu, or by clicking on the second icon from the left.
This dialog is for specifying the PcGets algorithm implemented in the estimation methods GETS and GETSIVE.
If `outlier correction' is on, outliers are detected using the size of the residuals in the GUM, and dummy variables are added to the model. The user determines the magnitude of departure, in terms of residual standard deviations, that defines an `outlier'.
If `lag order pre-selection' is on, an F-test checks the longest-lag blocks till the null is rejected at the chosen level. Thus, if the GUM is:
|
|
where m= max ( n0...nk) and x0,t-j=yt-j (for convenience of notation) then all the included {xi,t-m,i=0,...,k} are first tested, then {xi,t-m+1,i=0,...,k} and so on.
If `top-down' is on, the t2-statistics are ordered from the smallest up, and a cumulative F-test checks increasing block sizes till the null is rejected at the chosen level. This test is analyzed in Chapter 10.4.
If `bottom-up' is on, an F-test checks decreasing block sizes from the largest t2-statistics down till the null is not rejected at the chosen level.
If `sample-split analysis' is on, the significance of every variable in the final model is tested in two overlapping sub-samples. The variables are penalized accordingly and reliability statistics are recorded. The percentage overlap is set by the user, as are the `penalties' for unreliability. For example, the default settings entail:
There is an additional penalty if the variable is insignificant in one or both sub-samples at four time the relevant significance level.
Naturally, the user must decide how to react to the information offered, noting that, even in case 6., PcGets has chosen to retain the variable in question, possibly because its deletion induces non-congruence. This option reflects a concern about recursive estimation, which so far eludes full automation.
If `sample-size adjustment' is on, the significance levels change with the sample size. This issue is analyzed in §11.8. Note that the sample-size adjustment option is only provided for the built-in strategies. The present settings are as follows.
Liberal strategy T < 60 60£T < 120 120£T < 200 200£T < 1000 1000£T a F 0.125 0.075 0.075 0.050 0.025 a t 0.100 0.050 0.050 0.025 0.010 pre-F1 0.900 0.750 0.625 0.500 0.250 pre-F2 0.750 0.500 0.375 0.250 0.100 pre-t1 0.125 0.075 0.075 0.050 0.025 pre-t2 0.100 0.050 0.050 0.025 0.010 Conservative strategy a F 0.050 0.020 0.015 0.010 0.002 a t 0.025 0.010 0.010 0.005 0.001 pre-F1 0.750 0.500 0.250 0.100 0.050 pre-F2 0.500 0.250 0.100 0.050 0.025 pre-t1 0.050 0.020 0.015 0.010 0.002 pre-t2 0.025 0.010 0.010 0.005 0.001
This is the `built-in' PcGets strategy focusing on minimizing the non-selection probability of relevant variables. In that sense, a `liberal strategy' should have a higher probability of retaining relevant variables at the risk of also retaining irrelevant.
This is the `built-in PcGets strategy focusing on minimizing the non-deletion probability of nuisance variables. In that sense, a `conservative strategy' should have a higher probability of eliminating irrelevant variables at the risk of also eliminating relevant ones.
This strategy as defined by the Options set in section 15.4.
Prints the GUM and the selected model.
Prints only the major steps of the model reduction.
| symbol | Reduction path information |
| . | single reduction step: a variable or group of variables has been removed; |
| * | reduction failed, path returns to the previous specification; |
| f | reduction failed, path is not continued. |
| c | reduction path converged to a previously found reduction; |
| t | terminal specification found. |
Prints every step with detailed information. Note that this setting is only relevant for GETS/GETSIVE.
Options allows an expert user to specify their desired strategy. It refers to settings which are likely to be changed infrequently, and the choices are persistent between runs of PcGets.
Sets the significance level of t-tests.
Sets the significance level of F-tests.
Sets the significance level of the F-test of the GUM.
Sets the significance level of the encompassing tests.
Sets the loosest significance level of diagnostic tests.
Sets the most stringent significance level of diagnostic tests (implemented if the relevant test rejects at the looser level in the GUM).
Sets the significance level of the lag pre-selection.
Sets the significance level of the top-down reduction pre-search (Step 1).
Sets the significance level of the top-down reduction pre-search (Step 2).
Sets the significance level of the bottom-up reduction pre-search.
Sets the marginal t-probability of the top-down reduction pre-search: the reduction ceases when the smallest remaining t-value is smaller than this probability (Step 1).
Sets the marginal t-probability of the top-down reduction pre-search (Step 2).
Sets the marginal t-probability of the bottom-up reduction pre-search.
If checked, the top-down reduction pre-search runs through two steps.
If checked, a reduction path starts by removing a group of variables with t-probability > 0.90.
If checked, a reduction path starts by removing a group of variables with t-probability > 0.70.
If checked, a reduction path starts by removing a group of variables with t-probability > 0.50.
If checked, a reduction path starts by removing a group of variables with t-probability > 0.25.
If checked, a reduction path starts by removing a group of variables with t-probability > 0.10.
If checked, a reduction path starts by removing a group of variables with t-probability > 0.05.
If checked, a reduction path starts by removing a group of variables with t-probability > 0.01.
If checked, a reduction path starts by removing a group of variables with t-probability > 0.001.
Four information criteria are calculated and reported, one of which can be set to select the final choice from mutually encompassing congruent terminal models.
Sets significance level for t-tests in sub-samples.
Sets size of the sub-sample as fraction of the full sample.
Sets penalty for failed t-test in full sample.
Sets penalty for failed t-test in sub-sample 1.
Sets penalty for failed t-test in sub-sample 2.
Determines the size of a marginal outlier (as a multiple of s^).
If checked, the first Chow test is included in the test battery.
If checked, the second Chow test is included in the test battery.
If checked, the portmanteau statistic is included in the test battery.
If checked, the normality test is included in the test battery.
If checked, the LM test for residual autocorrelation is included in the test battery.
If checked, the test for ARCH in the residuals is included in the test battery.
If checked, the LM test for heterosckedasticity is included in the test battery.
Sets first break-point as a fraction of the sample.
Sets second break-point as a fraction of the sample.
Sets number of lags used in calculating the portmanteau statistic.
Sets the minimal lag of the LM test for residual autocorrelation.
Sets the maximal lag of the LM test for residual autocorrelation.
Sets the minimal lag of the test for ARCH effects in the residuals.
Sets the maximal lag of the test for ARCH effects in the residuals.
Leaves the expert settings unchanged when selected.
Resets the expert settings to the liberal strategy.
Resets the expert settings to the conservative strategy.
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