                                                 30 October 1997

Example 2.  The specs in this example demonstrate two strategies for
            dealing with calendar month heteroskedasticity (i.e., 
            data from some months have more statistical variability
            than data from other months):
            (1)  Use different seasonal filter lengths for different 
                 months.  (Decisions on individual month's filter
                 lengths are usually based on the moving seasonality 
                 ratios in Table D 9.A.)
            (2)  Use calendarsigma=select option.  To see if different
                 calendar months have different variances, set calendarsigma=all
                 in the X11 spec and look at the standard deviations at
                 the bottom of Table C 17.  Months to be treated differently
                 for the purpose of extreme value identification can then 
                 be listed as values of the sigmavec= option used in 
                 conjunction with the calendarsigma=select option.  (See
                 the X-12-ARIMA reference manual for details.)

            It is valuable to look at sliding spans and history diagnostics.


            Midwest Single-Family Housing Starts --

            Sliding spans improve when we use different filter lengths for
            different months.  3x5 seasonal filters are used for December 
            and 3x9s in the rest.  

            The calendarsigma=select option improves the revision histories
            for January and February.  You can see this particularly in 
            percent change and seasonal adjustment value graphs in X-12-Graph.
            (Use the -g execution flag so files are available for X-12-GRAPH.)



# Example 2:  mw1fam0.spc

# Adjustment of Single-Family Housing Starts from the Midwest Region of the US.
# 3x9 seasonal filters only.

series{
  period=12
  title='MIDWEST Single Family Housing Starts'
  file='example2.dat'
  name='MW1FAM'
  format='2L'
  savelog=peaks
  span=(1982.1, ) 
}
transform{function=log}
arima{model=(0 1 1 )(0 1 1)}
estimate{ }
check{print=all savelog=lbq}
x11{
  seasonalma=(s3x9 )
  sigmalim=(1.8, 2.8)
  savelog=(m7 m10 m11 q q2 fd8 msf)
}
slidingspans{
  fixmdl=yes  
  savelog=percent	 
  cutseas=4.5
  cutchng=4.5
# Nondefault thresholds added 10-19-98
}
#history{
#  estimates=(sadj sadjchng)
#  savelog=(asa ach)
#  start=1992.1
#}



# Example 2:  mw1fam1.spc

# Adjustment of Single-Family Housing Starts from the Midwest Region of the US.
# Uses both 3x5 and 3x9 seasonal filters to account for the moving seasonality
# in December.  Notice improvements in the sliding spans.

series{
  period=12
  comptype=add
  title='MW Single Family Starts (with CSD seasonal filters)'
  file='example2.dat'
  name='MW1FAM'
  format='2L'
  savelog=peaks
  span=(1982.1, ) 
}
transform{function=log}
arima{model=(0 1 1 )(0 1 1)}
estimate{ }
check{print=all savelog=lbq}
x11{
  seasonalma=(s3x9 s3x9 s3x9 s3x9 s3x9 s3x9 s3x9 s3x9 s3x9 s3x9 s3x9 s3x5)
  sigmalim=(1.8 2.8)
  savelog=(m7 m10 m11 q2)
}
slidingspans{
  fixmdl=yes  
  savelog=percent	 
  cutseas=4.5
  cutchng=4.5
# Nondefault thresholds added 10-19-98
}
#history{
#  estimates=(sadj sadjchng)
#  savelog=(asa ach)
#  start=1992.1
#}



# Example 2:  mw1fam.spc

# Adjustment of Single-Family Housing Starts from the Midwest Region of the US.
# Uses both 3x5 and 3x9 seasonal filters to account for the large variability in
# the Winter months.  The calendarsigma option reduces revisions, particularly
# in January and February.

series{
  period=12
  title='MW Single Family Starts (with CSD seasonal filters and calendar sigma)'
  file='example2.dat'
  name='MW1FAM'
  format='2L'
  savelog=peaks
  span=(1982.1, ) 
}
transform{function=log}
arima{model=(0 1 1 )(0 1 1)}
estimate{ }
check{print=all savelog=lbq}
x11{
  calendarsigma=select
  sigmavec=(jan feb)
  seasonalma=(s3x9 s3x9 s3x9 s3x9 s3x9 s3x9 s3x9 s3x9 s3x9 s3x9 s3x9 s3x5)
  sigmalim=(1.8 2.8)
  savelog=(m7 m10 m11 q2)
}
slidingspans{
  fixmdl=yes  
  savelog=percent	 
  cutseas=4.5
  cutchng=4.5
# Nondefault thresholds added 10-19-98
}
#history{
#  estimates=(sadj sadjchng)
#  savelog=(asa ach)
#  start=1992.1
#}
