Title: Census Bureau Seasonal Adjustment Software and Research
1Census Bureau Seasonal Adjustment Software and
Research
- David.F.Findley_at_census.gov
- U S C E N S U S B U R E A
U
2Outline of Talk Software
- X-12-ARIMA and its Evolution to
X-12-ARIMA/SEATS - Windows version ( Jurgen Doornik GiveWin)
- Supporting software
- Genhol (holiday regressors)
- SAS Software
- X-12-Graph (14 types of diagnostic graphs)
- Interface (simplifies analyses sets of series)
- X-12-Write (easy prod./modif. of .spc files)
- X-12-Review (1 page diagnostic summaries)
-
3Outline of Talk Research
- TRAMO/SEATS Evaluation Improvement for
X-12-ARIMA\SEATS (also for short series) - Filters and Filter Diagnostics
- Automatic modeling TRAMO vs. X-12s TRAMO
- Revisions
- State-Space Models using Sampling Error Data
-
- Non-Gaussian Structural State-Space Models
for More Stable Resistance to Outliers
4Statistical Research Division Time Series Group
Research and X-12-ARIMA Programming
- Brian.C.Monsell
- Kellie.C.Wills
- William.R.Bell (honorary)
- David.F.Findley (honorary)
- Donald.E.Martin (Part-time, Howard University)
- Trang.Ta.Nguyen (1-year in-house visitor)
- John.Alexander.Aston (2-year Post-Doc from
Imperial College, London) - S.J.M. Koopman (Fellow, Free Univ. of Amsterdam)
5Economic and Statistical Programming
DivisionTime Series Methods BranchResearch and
SAS, Excel Programming
- Catherine.C.Hood
- Kathleen.M.McDonald.Johnson
- Golam.Farooque
- Roxanne.Feldpausch
6Outline of Talk Software
- X-12-ARIMA and its Evolution to
X-12-ARIMA/SEATS - Windows version ( Jurgen Doornik GiveWin)
- Supporting software
- Genhol (holiday regressors)
- SAS Software
- X-12-Graph (14 types of diagnostic graphs)
- Interface (simplifies analyses sets of series)
- X-12-Write (easy prod./modif. of .spc files)
- X-12-Review (1 page diagnostic summaries)
-
7X-12-ARIMA
- Improvements over StatsCans X-11-ARIMA
- regARIMA models (including outliers, user-defined
regressors, etc.) vs. ARIMA models - Much more extensive automatic options for
modeling, including trading day, holiday est.,
additive vs. multiplicative adjustment - More diagnostics (e.g. spectra, revisions)
- Specialized output files, e.g. log files for
users favorite diagnostics, from many X-12-Graph
(SAS, but for non-SAS-users)
8RegARIMA Models (Forecasts, Backcasts, and
Preadjustments)
Modeling and Model Comparison Diagnostics and
Graphs
X-11 Seasonal Adjustment
Seasonal Adjustment Diagnostics and Graphs
9REGARIMA Model
transformation
ARIMA Process
Regressors for trading day and holiday or
calendar effects, additive outliers, temporary
changes, level shifts, ramps user-defined effects
Leap-year adjustment, or subjective strike
adjustment, etc.
10Types of Regression Variables Available in
X-12-ARIMA
- Outlier and Trend-Change Effects
- Additive (or Point) Outliers
- Temporary Change Outliers
- Level shifts, Ramps
- Seasonal Effects
- Calendar month indicators
- Trigonometric Seasonal (Sines-Cosines)
- Calendar Effects
- Trading Day (Flows or Stocks)
- Leap-year February, Length of Month
- Shifting Holidays (e.g. Easter)
- Constant Term
- User-Defined Effects
- Two-regime option available
- Note Regression coefficients can be fixed
11X-12-ARIMA Releases
- Ver. 0.2.10 July (Statistics Canada options)
- Ver. 0.3 Summer (TRAMO-type automatic ARIMA
model selection) - -based on information gleaned from TRAMO code
provided by Victor Gomez - Ver. 1.0 End of year (Better organized output
and manual, more testing etc.)
12X-12-ARIMA/SEATS
- Offers both x11 and seats commands to
provide X-11 or SEATS type seasonal
adjustments with X-12-ARIMA diagnostics as well
as SEATS diagnostics - Is being updated from SEATS2000 to SEATS20012002
(with support from Agustin Maravall and Gianluca
Caporello) - Schizophenic (duplicate) output, currently
- Distribution for research and testing to
statistical agencies and central banks in 2003 -
-
13Diagnoses from X-12-ARIMA/SEATS
- 1. Spectrum diagnostic reveals source of Invalid
Decomposition problem
14X-12-A/SEATS COMMAND FILE series
file"serie.txt" format"tramo" transformfunct
ionlog outliercritical3.7 arimamodel(0 1
1)(0 1 1) check x11 seats
15Message from seats run
- NOTE Spectral plot for the seasonally adjusted
series cannot be done when SEATS cannot perform a
signal extraction.
16Parameter Estimate
Errors ------------------------------------------
----------- Nonseasonal MA
Lag 1
0.3846 0.12087 Seasonal MA
Lag 12
-0.3665 0.12612
17 10LOG(SPECTRUM) of the regARIMA model
residuals Spectrum estimated from 1990.Jan
to 1995.Oct. I
-22.11I I
I I
-23.34I
I
I
T
I T
-24.57I
T
I
T I
T
I
T -25.81I
T
I
T
18series file"serie.txt" format"tramo" transfor
mfunctionlog outliercritical3.7 arimamodel
(0 1 1)(0 1 1) check x11 seats
19X-12-ARIMA/SEATS Seasonal Adjustment
Program Version Number 0.3s Build 24
WARNING At least one visually significant
trading day peak has been found in one or more of
the estimated spectra.
20G.1 10LOG(SPECTRUM) of the differenced,
transformed seasonally adjusted data. Spectrum
estimated from 1990.Jan to 1995.Oct.
I
I
T I
T
I
T -20.10I
T
I
T I
T
I
T -22.01I
T
I
T I
T
21series file"serie.txt" format"tramo" transfor
mfunctionlog outliercritical3.7 arimamodel
(0 1 1)(0 1 1) regressionvariablestd check s
eats
22X-12-ARIMA/SEATS Seasonal Adjustment
Program Version Number 0.3s Build 24 Reading
input spec file from metalss.spc Storing any
program output into metalss.out Storing any
program error messages into metalss.err
WARNING At least one visually significant
seasonal peak has been found in one or more of
the estimated spectra.
23 Standard Parameter
Estimate Errors ------------------------
----------------------------- Nonseasonal MA
Lag 1
0.1995 0.12871 Seasonal
MA Lag
12 0.3843 0.15795
24X-12-ARIMA Diagnoses for SEATS
- 2. T/S Practice of adding outliers to improve
kurtosis, etc. can substantially increase the
size of revisions of the initial seasonal
adjustments - Example (from Catherine Hood) US Exports of
Passenger Cars History diagnostic shows cost to
revisions of adding outlier regressors to reduce
kurtosis
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27Outline of Talk Software
- X-12-ARIMA and its Evolution to
X-12-ARIMA/SEATS - Windows version ( Jurgen Doornik GiveWin)
- Supporting software
- Genhol (holiday regressors)
- SAS Utilities
- X-12-Graph (14 types of diagnostic graphs)
- Interface (simplifies analyses of many series)
- X-12-Write (easy prod./modif. of .spc files)
- X-12-Review (1 page diagnostic summaries)
-
28Genhol
- From holiday date file Generates regressor
matrices and associated command files to enable
X-12-ARIMA estimation of complex moving holiday
effects (e.g. for Easter, Ramadan, etc.). - Regressors for up to three intervals
- before-the-holiday interval
- surrounding-the-holiday interval
- past-the-holiday interval (recovery interval)
-
29Proportionality Regressors An Example
- Assume
- An effect interval is 10 days long, and this year
2 of its days fall in January and 8 in February. - The interval regressors values for this year
will be - 0.2 in January
- 0.8 in February
- 0.0 for the rest of the year
30Interface Program (SAS) for seasonal adjustment
of sets of series
- Example Seasonally Adjusted Total U.S. Imports
sum of 140 component series, c. 80 of which
are seasonally adjusted. - What is the effect on the month-to-month changes
and quality diagnostics of the S. A. Total
Imports if the seasonal adjustment options are
changed for 5 of the component series?
31Outline of Talk Research
- TRAMO/SEATS Evaluation Improvement for
X-12-ARIMA\SEATS (also for short series) - Filter Diagnostics
- Automatic modeling TRAMO vs. X-12s TRAMO
- Revisions
-
-
32Filters and Filter Diagnostics
- Filter (spectral) diagnostics needed
- To understand limitations/issues with short
series (finite filter diagnostics, also for
concurrent adjustments, trends) - To decide between closely competitive models
- Paper by David Findley and Donald Martin.
33(No Transcript)
34Outline of Talk Research
- TRAMO/SEATS Evaluation Improvement for
X-12-ARIMA\SEATS (also for short series) - Automatic modeling TRAMO vs. X-12s TRAMO
- Accuracy
- Results from simulated series
- Revisions
- Results from Census Bureau series
35ESMPDs Automatic Modeling Study
- First presented at the International Forecasters
Symposium, June 2001 - Continuation of this work to appear at the ASA
meetings, August 2002, in a paper by Kathleen
McDonald-Johnson, et al.
36Series
- 306 time series from the US Census Bureaus
Import/Export series and Retail Sales
37Results
- 88 series (29) with same regARIMA model
- 27 series (9) with same differencing and same
regressors but different ARMA choices - 123 series (40) with same differencing, but
different regressors - 32 series (10) with different nonseasonal
differencing order (but sometimes offset by a
constant) - 36 series (12) with different seasonal
differencing order
38Conclusions
- TRAMOs weakness is the procedure for deciding
about trading day modeling - TRAMO developers are aware of our results
- X-12-ARIMA has a problem with choosing
nonparsimonious models - Monsell has already implemented some changes,
including a unit root test.
39Why Are Different Models Chosen?
- Model estimation method is different
- TRAMO Hannan-Rissanen and m.l.e conditional on
AR part of model - X-12-ARIMA Exact MLE
- Model residuals are different, which can lead to
different choices of outliers - Outlier procedure itself is different
- TRAMO removes insignificant outliers after each
iteration - TRAMO uses approximate BIC
40Accuracy X-12-ARIMA vs T/S (ESMPD)
- Results from 54 simulated series were first
presented at the ASA meetings, August 2000 - Continuation of the first SEATS studies,
beginning in 1997
41The Simulated Series
- Fifty-four series
- Six different trends three from SEATS and three
from X-12 - Six different seasonal factors three from SEATS
and three from X-12 - Irregular sampled from three sets of irregular
factors combined from SEATS and X-12
42Results of Accuracy Study
- SEATS performed better on the majority of series
with large irregulars if the series are 9 years
long, but most adjustments were not acceptable. - Both programs did better than expected on the
short series, but X-12-ARIMA adjustments were
usually better than SEATS adjustments on series
4-7 years long
43Revisions X-12-ARIMA vs T\S
- New ESMPD study using X-12-SEATS on Census
series. Final results will be presented at
the ASA meetings, August 2002. - Can we identify characteristics in the series
that will indicate if its linearized series
will be a better candidate for a model-based
adjustment than for an X-11 filter adjustment or
vice versa?
44Methods
- Use X-12-SEATS to get revision diagnostics from
both an X-11/X-12-type adjustment and a SEATS
adjustment - Used TRAMO to get the ARIMA model, and then used
either an x11 or a seats spec
45Very Preliminary Results
- 260 US Import/Export series
- Only a very small subset (18 series) where we can
see definite differences in the revision
diagnostics for the seasonal adjustment
46An Observation Series with
- Large revisions in X-12 and smaller revisions in
SEATS had generally large values for ?12 (most
greater than 0.95) and values for X-12s I/S
ratio lt 5. - Large revisions in SEATS and smaller revisions in
X-12 had generally 0.4 lt ?12 lt 0.6 and values
for I/S gt 6. - In both cases, smaller revisions are associated
with more constant seasonal factor estimates
47Next Steps
- Look at more series
- Look at more diagnostics/characteristics of the
series to try to find patterns, not just revisions
48Outline of Talk Research
- Projects almost ready to yield results
-
- State-Space Models using Sampling Error Data
-
- Non-Gaussian Structural State-Space Models
for More Stable Resistance to Outliers
49State-Space Models with Sampling Error
Statistics Bell and Nguyen
- 100 Disaggregate Construction series with high
sampling error variances - Consider model-based adjustment with
- regARIMAobservation error
- models that incorporate sampling error variance
and autocovariance estimates to achieve
acceptable or better seasonal adjs. - (Need state-space for model est. seas adj.)
50Non-Gaussian Structural State-Space Models for
More Stable Resistance to Outliers Koopman
and Aston
- X-12-ARIMA and T\S use outlier regressors
identified by t-statistics and critical values.
Identifications can change as new data arrive,
causing seasonal adjustment revisions. - Use heavy tailed non-Gaussian models instead of
critical values. (Hard to estimate such models,
simplest for Harveys structural models)
51More Information
- WWW site for X-12-ARIMA (papers and software)
- www.census.gov/srd/www/x12a
-
52- Thanks to Catherine Hood for supplying some of
these slides.