The UCM Procedure |
ESTIMATE Statement |
estimate skipfirst=12 back=24;
This statement requests that the initial 12 measurements and the last 24 measurements be excluded during the model-fitting process. The actual observation span used to fit the model is decided as follows: Suppose that and are the observation numbers of the first and the last nonmissing values of the response variable, respectively. As a result of SKIPFIRST=12 and BACK=24, the measurements between observation numbers and form the estimation span. Of course, the model fitting might not take place if there are insufficient data in the resulting span. The model fitting does not take place if there are regressors in the model that have missing values in the estimation span.
indicates that some ending part of the data needs to be ignored during the parameter estimation. This can be useful when you want to study the forecasting performance of a model on the observed data. BACK=10 results in skipping the last 10 measurements of the response series during the parameter estimation. The default is BACK=0.
enables continuation of the diffuse filtering iterations for additional iterations beyond the first instance where the initialization of the diffuse state would have otherwise taken place. If the specified is larger than the sample size, the diffuse iterations continue until the end of the sample. Note that one-step-ahead residuals are produced only after the diffuse state is initialized. Delaying the initialization leads to a reduction in the number of one-step-ahead residuals available for computing the residual diagnostic measures. This option is useful when you want to ignore the first few one-step-ahead residuals that often have large variance.
requests that the usual likelihood be optimized for parameter estimation. For more information, see the section Parameter Estimation by Profile Likelihood Optimization.
specifies an output data set for the estimated parameters.
In the ESTIMATE statement, the PLOT= option is used to obtain different residual diagnostic plots. The different possibilities are as follows:
requests different residual diagnostic plots. The different options are as follows:
produces the residual-autocorrelation plot.
produces the plot of cumulative residuals against time.
produces the plot of cumulative squared residuals against time.
produces the plot of one-step-ahead forecasts in the estimation span.
produces the histogram of residuals.
produces a scatter plot of residuals against time, which has an overlaid loess-fit.
produces the residual-partial-autocorrelation plot.
produces a summary panel of the residual diagnostics consisting of
histogram of residuals
normal quantile plot of residuals
the residual-autocorrelation-plot
the residual-partial-autocorrelation-plot
produces a normal quantile plot of residuals.
produces a needle plot of residuals against time.
produces a plot of p-values, in log-scale, at different lags for the Ljung-Box portmanteau white noise test statistics.
suppresses all the printed output related to the model fitting, such as the parameter estimates, the goodness-of-fit statistics, and so on.
requests that the profile likelihood, obtained by concentrating out one of the disturbance variances from the likelihood, be optimized for parameter estimation. By default, the profile likelihood is not optimized if any of the disturbance variance parameters is held fixed to a nonzero value. For more information see the section Parameter Estimation by Profile Likelihood Optimization.
indicates that some early part of the data needs to be ignored during the parameter estimation. This can be useful if there is a reason to believe that the model being estimated is not appropriate for this portion of the data. SKIPFIRST=10 results in skipping the first 10 measurements of the response series during the parameter estimation. The default is SKIPFIRST=0.
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