362—Appendix B. Command Reference

Examples

ss1.makesignals(t=smooth) sm*

produces smoothed signals in the series with names beginning with “sm”, and ending with the name of the signal dependent variable.

ss2.makesignals(t=pred, n=pred_sigs) sig1 sig2 sig3

creates a group named PRED_SIGS which contains the one-step ahead signal predictions in the series SIG1, SIG2, and SIG3.

Cross-references

See Chapter 25, “State Space Models and the Kalman Filter”, on page 751 of the User’s Guide for details on state space models. For additional discussion of wildcards, see Appendix B, “Wildcards”, on page 943 of the User’s Guide.

See also forecast (p. 300), makefilter (p. 352), and makestates (p. 362).

Generate state series or state standard error series from an estimated sspace object.

Options allow you to generate one-step ahead, filtered, or smoothed values for the states and the state standard errors.

Syntax

Follow the object name with a period and the makestate keyword, options to determine the output type, and a list of names or a wildcard expression identifying the series to hold the output. If a list is used to identify the targets, the number of target series must match the number of names implied by the keyword.

makestats—363

Options

t=output_type Defines output type: one-step ahead state predictions (default=“pred”) (“pred”), RMSE of the one-step ahead state predictions

(“predse”), error in one-step ahead state predictions (“resid”), RMSE of the one-step ahead state prediction (“residse”), filtered states (“filt”), RMSE of the filtered states (“filtse”), standardized one-step ahead prediction residual (“stdresid”), smoothed states (“smooth”), RMSE of the smoothed states (“smoothse”), estimate of the disturbances (“disturb”), RMSE of the estimate of the disturbances (“disturbse”), standardized estimate of the disturbances (“stddisturb”).

n=group_name Name of group to hold newly created series.

Examples

ss1.makestates(t=smooth) sm*

produces smoothed states in the series with names beginning with “sm”, and ending with the name of the state dependent variable.

ss2.makestates(t=pred, n=pred_states) sig1 sig2 sig3

creates a group named PRED_STATES which contains the one-step ahead state predictions in series SIG1, SIG2, and SIG3.

Cross-references

See Chapter 25, “State Space Models and the Kalman Filter”, on page 751 of the User’s Guide for details on state space models. For additional discussion of wildcards, see Appendix B, “Wildcards”, on page 943 of the User’s Guide.

See also forecast (p. 300), makefilter (p. 352) and makesignals (p. 361).

Create and save series of descriptive statistics computed from a pool object.

Syntax

364—Appendix B. Command Reference

You should provide options, a list of series names, an “@” separator, and a list of command names for the statistics you wish to compute. The series will have a name with the cross-section identifier “?” replaced by the statistic command.

Options

Options in parentheses specify the sample to use to compute the statistics

Command names for the statistics to be computed

Examples

pool1.makestats gdp_? edu_? @ mean sd

computes the mean and standard deviation of the GDP_? and EDU_? series in each period (across the cross-section members) using the default common sample. The mean and standard deviation series will be named GDP_MEAN, EDU_MEAN, GDP_SD, and EDU_SD.

pool1.makestats(b) gdp_? @ max min

Computes the maximum and minimum values of the GDP_? series in each period using the balanced sample. The max and min series will be named GDP_MAX and GDP_MIN.