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

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

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