Output description

In this chapter

How to navigate output

Names

Indications and example in the relevant algo or tool chapter

Here comprehensive (target) lists

What is displayed or generated ?

Output: 3 types of objects

• series (final and intermediate)

• parameters (automatic, user-defined or resulting from the estimation process)

• diagnostics

displayed

• gui

• R packages

dictionaries user defined functions (deprecated)

generated to files or R objects (lists of lists)

Gathered in 2 data structures

• time series organised in data tables

• individual data (for each series: parameters and diagnostics)

How to access it

Below different lists We flog differences between v2 and v3

Series

fields: concept, name in the software (GUI, cruncher, R), algo used ? or by algo ?

Concept	Name
Original series	$$y$$
Forecasts of the original series	$$y\mathrm{\_}f$$
Standard errors of the forecasts of the original series	$$y\mathrm{\_}ef$$
Interpolated series	$$y\mathrm{\_}c$$
Forecasts of the interpolated series	$$yc\mathrm{\_}f$$
Standard errors of the forecasts of the interpolated series	$$yc\mathrm{\_}ef$$
Linearised series (not transformed)	$$y\mathrm{\_}lin$$
Linearised series (transformed)	$$l$$
Series corrected for calendar effects	$$ycal$$
Forecasts of the series corrected for calendar effects	$$ycal\mathrm{\_}f$$
Forecasts of the linearised series	$$l\mathrm{\_}f$$
Backcasts of the linearised series	$$l\mathrm{\_}b$$
Trend (including deterministic effects)	$$t$$
Forecasts of the trend	$$t\mathrm{\_}f$$
Seasonally adjusted series (including deterministic effects)	$$sa$$
Forecasts of the seasonally adjusted series	$$sa\mathrm{\_}f$$
Seasonal component (including deterministic effects)	$$s$$
Forecasts of the seasonal component	$$s\mathrm{\_}f$$
Irregular component (including deterministic effects)	$$i$$
Forecasts of the irregular component	$$i\mathrm{\_}f$$
All deterministic effects	$$det$$
Forecasts of the deterministic effects	$$det\mathrm{\_}f$$
Calendar effects	$$cal$$
Forecasts of the calendar effects	$$cal\mathrm{\_}f$$
Trading day effect	$$tde$$
Forecasts of the trading day effect	$$tde\mathrm{\_}f$$
Moving holidays effects	$$mhe$$
Forecasts of the moving holidays effects	$$mhe\mathrm{\_}f$$
Easter effect	$$ee$$
Forecasts of the Easter effect	$$ee\mathrm{\_}f$$
Other moving holidays effects	$$omhe$$
Forecasts of the other moving holidays effects	$$omhe\mathrm{\_}f$$
All outliers effects	$$out$$
Forecasts of all outliers effects	$$out\mathrm{\_}f$$
Outliers effects related to irregular (AO, TC)	$$out\mathrm{\_}i$$
Forecasts of outliers effects related to irregular (TC)	$$out\mathrm{\_}i\mathrm{\_}f$$
Outliers effects related to trend (LS)	$$out\mathrm{\_}t$$
Forecasts of outliers effects related to trend (LS)	$$out\mathrm{\_}t\mathrm{\_}f$$
Outliers effects related to seasonal (SO)	$$out\mathrm{\_}s$$
Forecasts of outliers effects related to seasonal (SO)	$$out\mathrm{\_}s\mathrm{\_}f$$
All other regression effects	$$reg$$
Forecasts of all other regression effects	$$reg\mathrm{\_}f$$
Regression effects related to irregular	$$reg\mathrm{\_}i$$
Forecasts of regression effects related to irregular	$$reg\mathrm{\_}i\mathrm{\_}f$$
Regression effects related to trend	$$reg\mathrm{\_}t$$
Forecasts of regression effects related to trend	$$reg\mathrm{\_}t\mathrm{\_}f$$
Regression effects related to seasonal	$$reg\mathrm{\_}s$$
Forecasts of regression effects related to seasonal	$$reg\mathrm{\_}s\mathrm{\_}f$$
Regression effects related to seasonally adjusted series	$$reg\mathrm{\_}sa$$
Forecasts of regression effects related to seasonally adjusted series	$$reg\mathrm{\_}sa\mathrm{\_}f$$
Separate regression effects	$$reg\mathrm{\_}y$$
Forecasts of separate regression effects	$$reg\mathrm{\_}y\mathrm{\_}f$$
Full residuals of the RegARIMA model	$$fullresiduals$$
Linearised series used as input in the decomposition	$$decomposition.y\mathrm{\_}lin$$
Forecast of the linearised series used as input in the decomposition	$$decomposition.y\mathrm{\_}lin\mathrm{\_}f$$
Trend produced by the decomposition	$$decomposition.t\mathrm{\_}lin$$
Forecasts of the trend produced by the decomposition	$$decomposition.t\mathrm{\_}lin\mathrm{\_}f$$
Seasonal component produced by the decomposition	$$decomposition.s\mathrm{\_}lin$$
Forecasts of the Seasonal component produced by the decomposition	$$decomposition.s\mathrm{\_}lin\mathrm{\_}f$$
Irregular produced by the decomposition	$$decomposition.i\mathrm{\_}lin$$
Forecasts of the irregular produced by the decomposition	$$decomposition.i\mathrm{\_}lin\mathrm{\_}f$$
Seasonally adjusted series produced by the decomposition	$$decomposition.sa\mathrm{\_}lin$$
Forecasts of the seasonally adjusted series produced by the decomposition	$$decomposition.sa\mathrm{\_}lin\mathrm{\_}f$$
Seasonal-Irregular produced by the decomposition	$$decomposition.si\mathrm{\_}lin$$
For X-13ARIMA-SEATS only. Series from the X-11 decomposition (x = a, b, c, d, e; y=a1…)	$$decomposition.x-tables.y$$
Benchmarked seasonally adjusted series	$$benchmarking.result$$
Target for the benchmarking	$$benchmarking.target$$

Diagnostics

Parameters

Cruncher Output