The output items

The CSV, TXT and XLS outputs of JDemetra+ may contain the items shown in table below.

A list of output items of JDemetra+ CSV, TXT and XLS formats.

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

The CSV matrix of JDemetra+ may contain:

Code	Meaning
${span.start}\$	Start of the series span
${span.end}$	End of the series span
${span.n}$	Length of the series span
${espan.start}$	Start of the estimation span
${espan.end}$	End of the estimation span
${espan.n}$	Length of the estimation span
${likelihood.neffectiveobs}$	Number of effective observations in the likelihood function
${likelihood.np}$	Number of parameters in the likelihood
${likelihood.logvalue}$	Log likelihood
${likelihood.adjustedlogvalue}$	Adjusted log likelihood
${likelihood.ssqerr}$	Sum of the squared errors in the likelihood
${likelihood.aic}$	AIC statistics
${likelihood.aicc}$	Corrected AIC statistics
${likelihood.bic}$	BIC statistics
${likelihood.bicc}$	BIC corrected for length
${residuals.ser}$	Standard error of the residuals (unbiased, TRAMO-like)
$residuals.ser - ml$	Standard error of the residuals (ML, X-13ARIMA-SEATS-like)
${residuals.mean}$	Test on the mean of the residuals
${residuals.skewness}$	Test on the skewness of the residuals
${residuals.kurtos}$	Test on the kurtosis of the residuals
${residuals.dh}$	Test on the normality of the residuals (Doornik-Hansen tests)
${residuals.lb}$	The Ljung-Box test on the residuals
${residuals.lb2}$	The Ljung-Box test on the squared residuals
${residuals.seaslb}$	The Ljung-Box test on the residuals at seasonal lags
${residuals.bp}$	The Box-Pierce test on the residuals
${residuals.bp2}$	The Box-Pierce test on the squared residuals
${residuals.seasbp}$	The Box-Pierce test on the residuals at seasonal lags
${residuals.nruns}$	Test on the number of runs of the residuals
${residuals.lruns}$	Test on the length of runs of the residuals
$mstatistics.m1$	The relative contribution of the irregular over three months span
$mstatistics.m2$	The relative contribution of the irregular component to the stationary portion of the variance
$mstatistics.m3$	The amount of period to period change in the irregular component as compared to the amount of period to period change in the trend-cycle
$mstatistics.m4$	The amount of autocorrelation in the irregular as described by the average duration of run
$mstatistics.m5$	The number of periods it takes the change in the trend-cycle to surpass the amount of change in the irregular
$mstatistics.m6$	The amount of year to year change in the irregular as compared to the amount of year to year change in the seasonal
$mstatistics.m7$	The amount of moving seasonality present relative to the amount of stable seasonality
$mstatistics.m8$	The size of the fluctuations in the seasonal component throughout the whole series
$mstatistics.m9$	The average linear movement in the seasonal component throughout the whole series
$mstatistics.m10$	The size of the fluctuations in the seasonal component in the recent years
$mstatistics.m11$	The average linear movement in the seasonal component in the recent years
${mstatistics.q}$	Summary of the M-Statistics
$mstatistics.q - m2$	Summary of the M-Statistics without M2
${diagnostics.quality}$	Summary of the diagnostics
${diagnostics.basic\ checks.definition:2}$	Definition test
${diagnostics.basic\ checks.annual\ totals:2}$	Annual totals test
${diagnostics.visual\ spectral\ analysis.spectral\ seas\ peaks}$	Test of the presence of the visual seasonal peaks in SA and/or irregular
${diagnostics.visual\ spectral\ analysis.spectral\ td\ peaks}$	Test of the presence of the visual trading day peaks in SA and/or irregular
${diagnostics.regarima\ residuals.normality:2}$	Test of the normality of the residuals
${diagnostics.regarima\ residuals.independence:2}$	Test of the independence of the residuals
${diagnostics.regarima\ residuals.spectral\ td\ peaks:2}$	Test of the presence of trading day peaks in the residuals
${diagnostics.regarima\ residuals.spectral\ seas\ peaks:2}$	Test of the presence of seasonal peaks in the residuals
${diagnostics.residual\ seasonality.on\ sa:2}$	Test of the presence of residual seasonality in the SA series
${diagnostics.residual\ seasonality.on\ sa\ (last\ 3\ years):2}$	Test of the presence of residual seasonality on\ sa\ (last\ 3\ years):2$$
${diagnostics.residual\ seasonality.on\ irregular:2}$	Test of the presence of residual seasonality in the irregular series (last periods)
$diagnostics.seats.seas\ variance:2$	Test on the variance of the seasonal component
$diagnostics.seats.irregular\ variance:2$	Test on the variance of the irregular component
$diagnostics.seats.seas/irr\ cross - correlation:2$	Test on the cross-correlation between the seasonal and the irregular component
${log}$	Log transformation
${adjust}$	Pre-adjustment of the series for leap year
${arima.mean}$	Mean correction
${arima.p}$	The regular autoregressive order of the ARIMA model
${arima.d}$	The regular differencing order of the ARIMA model
${arima.q}$	Regular moving average order of the ARIMA model
${arima.bp}$	The seasonal autoregressive order of the ARIMA model
${arima.bd}$	The seasonal differencing order of the ARIMA model
${arima.bq}$	The seasonal moving average order of the ARIMA model
$arima.phi(i)$	Regular autoregressive parameter (lag=$i$, max $i$=3) of the ARIMA model
$arima.th(i)$	Regular moving average parameter (lag=$i$, max $i$=3) of the ARIMA model
$arima.bphi(i)$	Seasonal autoregressive parameter (lag=$i$, max $i$=1) of the ARIMA model
$arima.bth(i)$	Seasonal moving average parameter (lag=$i$ max $i$=1) of the ARIMA model
$regression.lp:3$	Coefficient and test on the leap year
${regression.ntd}$	Number of trading day variables
${regression.td}\left( i \right):3$	Coefficient and test on the $i^\ $trading day variable
${regression.nmh}$	Number of moving holidays
$regression.easter:3$	Coefficient and test on the Easter variable
${regression.nout}$	Number of outliers
${regression.out}\left( i \right):3$	Coefficient and test on $i^\ $the outlier (max $i$=16)
${decomposition.seasonality}$	Presence of a seasonal component (1 – present, 0 – not present)
${decomposition.trendfilter}$	The order of the trend filter
${decomoposition.seasfilter}$	The order of the seasonal filter