enable the model information to be specified by BY groups. BY variables can be included in this data set that match the BY variables used to process the series. If no BY variables are included, then the models specified by _NAME_ apply to all BY groups.

should match the variable name of the time series to which the model is to be applied. Omit the _NAME_ variable if you are specifying the same model for all series in the BY group.

specifies whether the observation contains regression or ARIMA information. For PROC X12, _MODELTYPE_ should either be REG to supply regression information or ARIMA to supply model information. If valid regression information exists in the MDLINFOIN= data set for the BY group and series being processed, then the REGRESSION, INPUT, and EVENT statements are ignored for that BY group and series. Likewise, if valid model information exists in the data set, then the AUTOMDL, ARIMA, and TRANSFORM statements are ignored. Valid values for the other variables in the data set depend on the value of the _MODELTYPE_ variable. While other values of _MODELTYPE_ might be permitted in other SAS procedures, PROC X12 recognizes only REG and ARIMA.

further qualifies the regression or ARIMA information in the observation. For _MODELTYPE_= REG, valid values of _MODELPART_ are INPUT, EVENT, and PREDEFINED. A value of INPUT indicates that this observation refers to the user-defined variable whose name is given in _DSVAR_. Likewise, a value of EVENT indicates that the observation refers to the SAS or user-defined EVENT whose name is given in _DSVAR_. PREDEFINED indicates that the name given in _DSVAR_ is a predefined U.S. Census Bureau variable. If only model information is included in the data set (that is, all observations have _MODELTYPE_ = ARIMA) then the _MODELPART_ variable can be omitted. However, valid values for model information are FORECAST, ".", or blank.

further qualifies the regression or ARIMA information in the observation. For _MODELTYPE_= REG, the only valid value of _COMPONENT_ is SCALE. Other SAS procedures might allow other values. For _MODELTYPE_= ARIMA, the valid values of _COMPONENT_ are TRANSFORM, CONSTANT, NONSEASONAL, and SEASONAL. TRANSFORM indicates that the observation contains the information that would be supplied in the TRANSFORM statement. CONSTANT is specified to control the constant term in the model. NONSEASONAL and SEASONAL refer to the AR, MA, and differencing terms in the ARIMA model.

further qualifies the regression or ARIMA information in the observation. For regression information, the value of _PARMTYPE_ is the same as the value of the REGRESSION USERTYPE= option. Since the USERTYPE= option applies only to user-defined events and variables, the value of _PARMTYPE_ does not alter processing in observations where _MODELPART_ = PREDEFINED. However, it is consistent to use a value for _PARMTYPE_ that matches the Census predefined variable. For the constant term in model information, _PARMTYPE_ should be SCALE. For transformation information, the value of _PARMTYPE_ should be NONE, LOG, LOGIT, SQRT, or BOXCOX. For ARIMA model information, _PARMTYPE_ should be AR, MA, or DIF.

specifies the variable name associated with this observation. For regression information, the value of _DSVAR_ is the name of the user-defined variable, the EVENT, or the Census predefined variable. For model information, _DSVAR_ should match the name of the series being processed. If the model information applies to more than one series, then _DSVAR_ can be blank or ".".

contains a numerical value that is used as a parameter for certain types of information. For certain Census predefined variables, _VALUE_ is the associated parameter value. For example, the REGESSION statement option PREDEFINED=EASTER(6) would be implemented using _DSVAR_=EASTER and _VALUE_=6. For a BOXCOX transformation, _VALUE_ would be the associated parameter value. For _COMPONENT_=SEASONAL, if _VALUE_ is nonmissing, then _VALUE_ is used as the seasonal period. If _VALUE_ is missing for _COMPONENT_=SEASONAL, then the seasonal period is determined by the interval of the series.

applies only to AR and MA ARIMA model information. The actual value of _FACTOR_ should be the same for all observations related to lags within the same ARIMA factor. So the value of _FACTOR_ identifies lags that belong in the same factor.

identifies the degree for differencing and AR and MA lags. If _COMPONENT_=SEASONAL, then the value in _LAG_ is multiplied by the seasonal period indicated by the value of _VALUE_.

contains the shift value for transfer functions. This value is not processed by PROC X12.

indicates whether a parameter associated with the observation is to be estimated. For example, the NOINT option would be indicated by constant information with _NOEST_=1 and _EST_=0. _NOEST_=1 indicates that the value in _EST_ is a fixed value. _NOEST_ pertains to the constant term, to AR and MA lags, and to regression information.

contains an initial or fixed value for a parameter associated with the observation that is to be estimated. _NOEST_=1 indicates the value in _EST_ is a fixed value. _EST_ pertains to the constant term, to AR and MA lags, and to regression information.

contains output information about estimated parameters.

contains output information about estimated parameters.

contains output information about estimated parameters.

EVENT variable name. _NAME_ is displayed with the case preserved. Since _NAME_ is a SAS variable name, the event can be referenced by using any case. The _NAME_ variable is required; there is no default.

class of EVENT: SIMPLE, COMBINATION, PREDEFINED. The default for _CLASS_ is SIMPLE.

contains either a date keyword (SIMPLE EVENT), a predefined EVENT variable name (PREDEFINED EVENT), or an EVENT name (COMBINATION event). All _KEYNAME_ values are displayed in upper case. However, if the _KEYNAME_ value refers to an EVENT name, then the actual name can be of mixed case. The default for _KEYNAME_ is no keyname, designated by ".".

contains either the date timing value or the first date timing value to use in a do-list. The default for _STARTDATE_ is no date, designated by a missing value.

contains the last date timing value to use in a do-list. The default for _ENDDATE_ is no date, designated by a missing value.

contains the interval for the date do-list. The default for _DATEINTRVL_ is no interval, designated by ".".

contains either the datetime timing value or the first datetime timing value to use in a do-list. The default for _STARTDT_ is no datetime, designated by a missing value.

contains the last datetime timing value to use in a do-list. The default for _ENDDT_ is no datetime, designated by a missing value.

contains the interval for the datetime do-list. The default for _DTINTRVL_ is no interval, designated by ".".

contains either the observation number timing value or the first observation number timing value to use in a do-list. The default for _STARTOBS_ is no observation number, designated by a missing value.

contains the last observation number timing value to use in a do-list. The default for _ENDOBS_ is no observation number, designated by a missing value.

contains the interval length of the observation number do-list. The default for _OBSINTRVL_ is no interval, designated by ".".

type of EVENT. The default for _TYPE_ is POINT.

value for nonzero observation. The default for _VALUE_ is .

INTERVAL that defines the units for the DURATION values. The default for _PULSE_ is no interval, designated by ".".

number of durations before the timing value. The default for _DUR_BEFORE_ is 0.

number of durations after the timing value. The default for _DUR_AFTER_ is 0.

determines whether the curve is GROWTH or DECAY before the timing value for _TYPE_=RAMP, _TYPE_=RAMPP, and _TYPE_=TC. The default for _SLOPE_BEFORE_ is GROWTH.

determines whether the curve is GROWTH or DECAY after the timing value for _TYPE_=RAMP, _TYPE_=RAMPP, and _TYPE_=TC. The default for _SLOPE_AFTER_ is GROWTH unless _TYPE_=TC; then the default is DECAY.

number of _PULSE_= intervals to shift the timing value. The shift can be positive (forward in time) or negative (backward in time). If _PULSE_= is not specified, then the shift is in observations. The default for _SHIFT_ is .

parameter for EVENT of TYPE=TC. The default for _TCPARM_ is .

rule to use when combining events or when timing values of an event overlap. The default for _RULE_ is ADD.

frequency interval at which the event should be repeated. If this value is missing, then the event is not periodic. The default for _PERIOD_ is no interval, designated by ".".

label or description for the event. If you do not specify a label, then the default label value is displayed as ".". For events that produce dummy variables, either the user-supplied label or the default label is used. For COMPLEX events, the _LABEL_ value is merely a description of the group of events.