manual: expand information on x13
Johannes Pfeifer
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@ -82,5 +82,5 @@ Bibliography
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* Smets, Frank and Rafael Wouters (2003): “An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area,” *Journal of the European Economic Association*, 1(5), 1123–1175.
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* Stock, James H. and Mark W. Watson (1999). “Forecasting Inflation,”, *Journal of Monetary Economics*, 44(2), 293–335.
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* Uhlig, Harald (2001): “A Toolkit for Analysing Nonlinear Dynamic Stochastic Models Easily,” in *Computational Methods for the Study of Dynamic Economies*, Eds. Ramon Marimon and Andrew Scott, Oxford University Press, 30–61.
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* U.S. Census Bureau (2017): “X-13 ARIMA-SEATS Reference Manual”.
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* U.S. Census Bureau (2020): “X-13 ARIMA-SEATS Reference Manual, Version 1.1”, Center for Statistical Research and Methodology, U.S. Census Bureau, https://www.census.gov/data/software/x13as.html
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* Villemot, Sébastien (2011): “Solving rational expectations models at first order: what Dynare does,” *Dynare Working Papers*, 2, CEPREMAP.
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@ -15,7 +15,7 @@ class and methods for dates. Below, you will first find the class and
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methods used for creating and dealing with dates and then the class
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used for using time series. Dynare also provides an interface to the
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X-13 ARIMA-SEATS seasonal adjustment program produced, distributed, and
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maintained by the US Census Bureau (2017).
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maintained by the U.S. Census Bureau (2020).
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Dates
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@ -2946,8 +2946,8 @@ X-13 ARIMA-SEATS interface
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|br| The x13 class provides a method for each X-13 command as
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documented in the X-13 ARIMA-SEATS reference manual (`x11`,
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`automdl`, `estimate`, ...), options can then be passed by
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key/value pairs. The ``x13`` class has 22 members:
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`automdl`, `estimate`, ...). The respective options (see Chapter 7 of U.S. Census Bureau (2020))
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can then be passed by key/value pairs. The ``x13`` class has 22 members:
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:arg y: ``dseries`` object with a single variable.
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:arg x: ``dseries`` object with an arbitrary number of variables (to be used in the REGRESSION block).
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@ -2991,7 +2991,7 @@ X-13 ARIMA-SEATS interface
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same time span.
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The Following methods allow to set sequence of X-13 commands, write an `.spc` file and run the X-13 binary:
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The following methods allow to set sequence of X-13 commands, write an `.spc` file, and run the X-13 binary:
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.. x13method:: A = arima (A, key, value[, key, value[, [...]]])
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@ -3026,7 +3026,10 @@ X-13 ARIMA-SEATS interface
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Interface to the ``transform`` command, see the X-13
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ARIMA-SEATS reference manual. All the options must be passed
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by key/value pairs.
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by key/value pairs. For example, the key/value pair ``function,log``
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instructs the use of a multiplicative instead of an additive seasonal pattern,
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while ``function,auto`` triggers an automatic selection between the two based
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on their fit.
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.. x13method:: A = outlier (A, key, value[, key, value[, [...]]])
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@ -3134,6 +3137,11 @@ X-13 ARIMA-SEATS interface
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``A.results``. When it makes sense these results are saved in
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``dseries`` objects (*e.g.* for forecasts or filtered variables).
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.. x13method:: clean (A)
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Removes the temporary files created by an x13 run that store the intermediate
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results. This method allows keeping the main folder clean but will also
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delete potentially important debugging information.
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*Example*
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@ -3150,8 +3158,57 @@ X-13 ARIMA-SEATS interface
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>> o.forecast('maxlead',18,'probability',0.95,'save','(fct fvr)');
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>> o.run();
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The above example shows a run of X13 with various commands an options specified.
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*Example*
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::
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% 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960
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y = [112 115 145 171 196 204 242 284 315 340 360 417 ... % Jan
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118 126 150 180 196 188 233 277 301 318 342 391 ... % Feb
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132 141 178 193 236 235 267 317 356 362 406 419 ... % Mar
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129 135 163 181 235 227 269 313 348 348 396 461 ... % Apr
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121 125 172 183 229 234 270 318 355 363 420 472 ... % May
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135 149 178 218 243 264 315 374 422 435 472 535 ... % Jun
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148 170 199 230 264 302 364 413 465 491 548 622 ... % Jul
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148 170 199 242 272 293 347 405 467 505 559 606 ... % Aug
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136 158 184 209 237 259 312 355 404 404 463 508 ... % Sep
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119 133 162 191 211 229 274 306 347 359 407 461 ... % Oct
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104 114 146 172 180 203 237 271 305 310 362 390 ... % Nov
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118 140 166 194 201 229 278 306 336 337 405 432 ]'; % Dec
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ts = dseries(y,'1949M1');
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o = x13(ts);
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o.transform('function','auto');
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o.automdl('savelog','all');
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o.x11('save','(d11 d10)');
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o.run();
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o.clean();
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y_SA=o.results.d11;
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y_seasonal_pattern=o.results.d10;
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figure('Name','Comparison raw data and SAed data');
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plot(ts.dates,log(o.y.data),ts.dates,log(y_SA.data),ts.dates,log(y_seasonal_pattern.data))
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The above example shows how to remove a seasonal pattern from a time series.
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``o.transform('function','auto')`` instructs the subsequent ``o.automdl()`` command
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to check whether an additional or a multiplicative pattern fits the data better (the latter is
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the case in the current example). The ``o.automdl('savelog','all')`` automatically selects a fitting
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ARIMA model and saves all relevant output to the .log-file. The ``o.x11('save','(d11, d10)')`` instructs
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``x11`` to save both the final seasonally adjusted series ``d11`` and the final seasonal factor ``d10``
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into ``dseries`` with the respective names in the output structure ``o.results``. ``o.clean()`` removes the
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temporary files created by ``o.run()``. Among these are the ``.log``-file storing
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summary information, the ``.err``-file storing information on problems encountered,
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the ``.out``-file storing the raw output, and the `.spc`-file storing the specification for the `x11` run.
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There may be further files depending on the output requested. The last part of the example reads out the
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results and plots a comparison of the logged raw data and its log-additive decomposition into a
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seasonal pattern and the seasonally adjusted series.
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Miscellaneous
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=============
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