SMC Procedure

FORECAST Statement

FORECAST <options>;

The FORECAST statement specifies options that are related to the forecasting problem, which is to find the probability distribution of the state and the dependent variables. If you specify the LEARN statement, the model parameters in the requested forecasting process are set to the posterior mean of the corresponding model parameters; otherwise, the parameters whose initial values are specified in the PARAMETERS statement are used in the forecasting process. You can specify the following options:

ALGORITHM=algorithm ALG=algorithm

specifies the particle filter algorithm to use for the forecasting problem. You can specify the following algorithms:

APF<(ADP)>: specifies the auxiliary particle filter (APF) algorithm. The ADP suboption specifies the fully adapted auxiliary particle filter algorithm.
BF: specifies the bootstrap filter (BF) algorithm.
SIR<(TH | THRESH | THRESHOLD=value)>: specifies the sequential importance resampling (SIR) algorithm. The optional TH= option specifies the threshold (a value between 0 and 1) of the resampling step in the SIR algorithm. The resampling step occurs when the effective sample size is less than the product of the threshold and the number of particles. By default, TH=0.8.

By default, ALGORITHM=SIR.

LEAD=number

specifies the number of multi-step forecast values to compute, where number must be a nonnegative integer. By default, LEAD=1, which means that a one-step-ahead forecast is performed.

NPARTICLE=number

specifies the number of particles to use in the forecasting process. If you omit this option, the forecasting process uses the value of the NPARTICLE= option in the PROC SMC statement.

PARM=MEAN | MEDIAN

specifies which parameter values from the posterior distribution to use for the forecasting distribution when you also specify the LEARN statement. If you omit the LEARN statement, this option is ignored. You can specify one of the following values:

MEAN: constructs the forecasting distribution by using the estimated posterior means of the parameters.
MEDIAN: constructs the forecasting distribution by using the estimated posterior medians of the parameters.

By default, PARM=MEAN.

OUT<(PERCENTILES=numeric-list)>=libref.data-table OUT<(PERCENTILE=numeric-list)>=libref.data-table OUT<(PERCENT=numeric-list)>=libref.data-table OUT<(PERC=numeric-list)>=libref.data-table OUT<(PCT=numeric-list)>=libref.data-table

writes the summary of the forecasting estimates to the specified output data table. The weighted average, weighted standard deviation, minimum value, first quantile, second quantile, third quantile, maximum value, and percentile points of each state variable at each time step are displayed. The optional PERCENTILES=numeric-list specifies the percentile points in the summary table of the forecasting estimates, where each value in the numeric-list must be between 0 and 100 and values are separated by commas or spaces. By default, PERCENTILES=2.5, 97.5, which yields the 2.5th and 97.5th percentile points, respectively. libref.data-table is a two-level name, where libref refers to the library, and data-table specifies the name of the output data table. For more information about this two-level name, see the DATA= option and the section Using CAS Sessions and CAS Engine Librefs.

OUTSIM=libref.data-table

writes the forecasting estimates to the specified output data table. The particle values with the corresponding normalized weights at each time step are displayed. libref.data-table is a two-level name, where libref refers to the library, and data-table specifies the name of the output data table. For more information about this two-level name, see the DATA= option and the section Using CAS Sessions and CAS Engine Librefs.

RESAMPLE

specifies that particles are to be resampled before the forecasting process.

Last updated: July 09, 2026