Merge pull request #188 from palatej/release-2.1.0

Corrections int SA specifications (help, UI)

nbdemetra-branding/src/main/nbm-branding/modules/eu-europa-ec-joinup-sat-nbdemetra-sa.jar/ec/nbdemetra/tramoseats/descriptors/Bundle.properties

@@ -11,12 +11,21 @@ arimaSpecUI.pcrDesc.desc=[pcr] Level of significance for the Ljung-Box Q-test us
 arimaSpecUI.tsigDesc.desc=[tsig] The threshold value for t-statistics of ARMA coefficients and constant term used for the final test of model parsimony. If the highest order ARMA coefficient has a t-value less than this value in magnitude, the order of the model is reduced. Also if the constant term has a t-value less than ArmaLimit in magnitude, it is removed from the set of regressors.
 arimaSpecUI.ub1Desc.desc=[ub1] The threshold value for the initial unit root test in the automatic differencing procedure. When one of the roots in the estimation of the (2,0,0)(1,0,0) plus mean model, performed in the first step of the automatic model identification procedure, is larger than First unit root limit, in modulus, it is set equal to unity.
 arimaSpecUI.ub2Desc.desc=[ub2] When one of the roots in the estimation of the (1,0,1)(1,0,1) plus mean model, which is performed in the second step of the automatic model identification procedure, is larger than Second unit root limit, in modulus, it is checked if there is a common factor in the corresponding AR and MA polynomials of the ARMA model that can be cancelled (see Cancelation limit)). If there is no cancellation, the AR root is set equal to unity (i.e. the differencing order changes).
+basicSpecUI.pcDesc.desc=When marked, checks the quality of the input series and exclude highly problematic ones: e.g. these with a number of outliers, identical observations and/or missing values above the respective threshold values.  When unmarked, the thresholds are ignored and process is performed, when possible.
+basicSpecUI.spanDesc.desc= Specifies a span (data interval) of the time series to be used for the seasonal adjustment/modelling process. When the user limits the original time series to a given span then only this span will be used in the computations.
+calendarSpecUI.easterDesc.desc=Specifies the Easter effect.
+calendarSpecUI.tdDesc.desc=Specifies the trading days, the working days and the leap year effects.
 easterSpecUI.optionDesc.desc=Options for specification of the presence and length of the Easter effect. Depending the option chosen, the Easter effect is not considered (Unused); influences the period of n days strictly before Easter Sunday (Standard); influences the entire period (n) up to and including Easter Sunday (Include Easter); influences the entire period (n) up to and including Easter Monday (Include Easter Monday);
+easterSpecUI.durationDesc.desc= Duration (length in days, between 1 and 15) of the Easter effect. 
+easterSpecUI.durationDesc.name=Duration
+easterSpecUI.testDesc.desc= A t-test applied for the significance of the Easter effect. The Easter effect is considered as significant if the modulus of t-statistic is greater than 1.96.
+easterSpecUI.testDesc.name=Test
 estimateSpecUI.emlDesc.desc=When marked, an exact maximum likelihood estimation is performed. Alternatively, the Unconditional Least Squares method is used.
+estimateSpecUI.spanDesc.desc= Specifies the span (data interval) of the time series to be used for the estimation of the RegARIMA model coefficients. The RegARIMA model is then applied to the whole series.
 estimateSpecUI.urlimitDesc.desc=[urfinal] The threshold value for the final unit root test for identification of differencing orders. If the magnitude of an AR root for the final model is less than this number, a unit root is assumed, the order of the AR polynomial is reduced by one, and the appropriate order of the differencing (non-seasonal, seasonal) is increased.
+estimateSpecUI.tolDesc.desc=[tol] Convergence tolerance for the nonlinear estimation. The absolute changes in the log-likelihood function are compared to this value to check for the convergence of the estimation iterations.
 outliersSpecUI.aoDesc.desc=[aio-partim] When marked, it enables for an automatic identification of additive outliers.
 outliersSpecUI.autoDesc.desc=[va] The critical value is automatically determined. It depends on the number of observations considered in the outliers detection procedure.
-outliersSpecUI.autoDesc.name=The critical value is automatically determined by the number of observations in the interval specified by the Detection span option. When Use default critical value is disabled, the procedure uses the critical value inputted in the Critical value item.
 outliersSpecUI.emlDesc.desc=[imvx] Controls the method applied for a parameter estimation in the intermediate steps of the automatic detection and correction of outliers. When marked, an exact likelihood estimation method is used, otherwise the fast Hannan-Rissanen method is used.
 outliersSpecUI.enableDesc.desc=[iatip] Enables/disables the automatic detection of outliers in the span determined by the Detection span option.
 outliersSpecUI.lsDesc.desc=[aio-partim] When marked, it enables for an automatic identification of level shifts.
@@ -26,9 +35,30 @@ outliersSpecUI.spanDesc.desc=[int1, int2] A span of the time series to be search
 outliersSpecUI.tcDesc.desc=[aio-partim] When marked, it enables for an automatic identification of transitory changes.
 outliersSpecUI.tcDesc.name=Transitory change
 outliersSpecUI.tcrateDesc.desc=[deltatc] The rate of decay for the transitory change outlier.
-outliersSpecUI.vaDesc.desc=[va] The critical value used in the outliers detection procedure.
+outliersSpecUI.autoDesc.desc=[va] The critical value is automatically determined. It depends on the number of observations considered in the outliers detection procedure. When Use default critical value is disabled, the procedure uses the critical value inputted in the Critical value item.
+outliersSpecUI.autoDesc.name= Use default critical value
+regressionSpecUI.calendarDesc.desc=Determines the manner in which the calendar effects are entered in the TRAMO model.
+regressionSpecUI.interventionDesc.desc= Allows for an estimation of the effects of the special events known a-priori.
+regressionSpecUI.interventionDesc.name=Intervention variables
+regressionSpecUI.prespecDesc.desc= Allows the user to include the pre-specified outliers (i.e. those for which the type and timing is known a-priori) in the RegARIMA model.
+regressionSpecUI.prespecDesc.name=Pre-specified outliers
+regressionSpecUI.rampsDesc.desc= Allows the user to include ramp effects in the RegARIMA model. A ramp effect is a linear increase or decrease in the level of the series over a specified time interval.
+regressionSpecUI.rampsDesc.name=Ramp effects
+regressionSpecUI.userdefinedDesc.desc= Allows the user to include the user-defined variables (external regressors) in the RegARIMA model.
+regressionSpecUI.userdefinedDesc.name=User-defined variables
 seatsSpecUI.epsphiDesc.desc=[epsphi] The tolerance (measured in degrees) to allocate the AR non-real roots to the seasonal component (if the modulus of the inverse complex AR root is greater than Trend boundary and the frequency of this root differs from one of the seasonal frequencies by less than Seasonal tolerance) or the transitory component (otherwise).
 seatsSpecUI.noadmissDesc.desc=[noadmiss] When the ARIMA model estimated by TRAMO does not accept an admissible decomposition, SEATS performs an approximation (None); replaces the model with a decomposable one (Legacy); or estimates a new model by adding a white noise to the non-admissible model estimated by TRAMO (Noisy).
 seatsSpecUI.rmodDesc.desc=[rmod] The boundary from which an AR root is integrated in the trend component. If the modulus of the inverse real root is greater than Trend boundary, the AR root is integrated in the trend component. Below that value the root is integrated in the transitory component.
 seatsSpecUI.wkDesc.desc=The estimation method of the unobserved components. The choice can be made from Burman (default, may result in a significant underestimation of the standard deviations of the components as it may become numerically unstable when some roots of the MA polynomial are near 1); KalmanSmoother (it is not disturbed by the (quasi-) unit roots in MA); McElroyMatrix (has the same stability issues as the Burman's algorithm).
 seatsSpecUI.xlDesc.desc=[xl] When the modulus of an estimated root falls in the range (xl, 1), it is set to 1 if it is a root in the AR polynomial. If a root is in the MA polynomial, it is set to xl.
+tradingDaysSpecUI.automaticDesc.desc=The calendar effects can be added to the model manually (through the Option, tradingDays and LeapYear parameters) or automatically, where the choice of the number of calendar variables is based on the F Test or Wald test (here the model with higher F value is chosen, provided that it is higher than Pftd).
+tradingDaysSpecUI.holidaysDesc.name=Holidays
+tradingDaysSpecUI.holidaysDesc.desc=Enables for using the existing user-defined calendars to create the calendar regression variables. Such calendars should be previously defined, otherwise the list is empty.
+tradingDaysSpecUI.optionDesc.name=Option
+tradingDaysSpecUI.optionDesc.desc=Specifies the type of a calendar being assigned to the series (Default – default calendar without country-specific holidays; Stock – day-of-week effects for inventories and other stock reported for the w-th day of the month; Holidays – the calendar variables based on user-defined calendar possibly with country specific holidays; UserDefined – calendar variables specified by the user) or excludes calendar variables from the regression model (None).
+tradingDaysSpecUI.stdDesc.desc=Estimates day-of-week effects for inventories and other stock reported for the w-th day of the month (to denote the last day of the month enter 31).
+tradingDaysSpecUI.tdDesc.desc=Defines the type of the trading days regression variables (TradingDays – six day-of-the-week regression variables; WorkingDays – one working/non-working day contrast variable).
+tradingDaysSpecUI.lpDesc.name=Leap year
+tradingDaysSpecUI.testDesc.desc=Pre-test of the trading day effects (None – calendar variables are used in the model without pre-testing; Separate_T – a t-test is applied to each trading day variable separately and the trading day variables are included in the TRAMO model if at least one t-statistic is greater than 2.6 or if two t-statistics are greater than 2.0 (in absolute terms); Joint_F – a joint F-test of significance of all the trading day variables. The trading day effect is significant if the F statistic is greater than 0.95).
+tradingDaysSpecUI.userDesc.desc=Enables including the user-defined regression variables in the model. Such variables should be previously defined, otherwise the list is empty.
+transformSpecUI.fctDesc.desc=Control of the bias in the log/level pre-test (it is active when Function is set to Auto); Fct > 1 favours levels, Fct < 1 favours logs.

nbdemetra-branding/src/main/nbm-branding/modules/eu-europa-ec-joinup-sat-nbdemetra-sa.jar/ec/nbdemetra/x13/descriptors/Bundle.properties

@@ -22,11 +22,13 @@ arimaSpecUI.qDesc.desc=[q] The order of the non-seasonal moving average polynomi
 arimaSpecUI.reducecvDesc.desc=[reducecv] The percentage by which the outlier\u2019s critical value will be reduced when an identified model is found to have a Ljung-Box statistic with an unacceptable confidence coefficient. The parameter should be between 0 and 1, and will only be active when automatic outlier identification is enabled. The reduced critical value will be set to (1\u2212ReduceCV)\u00d7CV, where CV is the original critical value.
 arimaSpecUI.thetaDesc.desc=[th, jqr] Coefficients of the non-seasonal moving average (MA). To each non-seasonal MA parameter in the model a label that indicates the procedure of its estimation is assigned (Undefined \u2013 no use of a user-defined input; Initial \u2013 the value defined by the user is used as initial condition; Fixed \u2013 holds a parameter fixed at the value defined by the user).
 arimaSpecUI.urlimitDesc.desc=[urfinal] The threshold value for the final unit root test. If the magnitude of an AR root for the final model is less than this number, a unit root is assumed, the order of the AR polynomial is reduced by one, and the appropriate order of the differencing (non-seasonal, seasonal) is increased.
+basicSpecUI.pcDesc.desc=When marked, checks the quality of the input series and exclude highly problematic ones: e.g. these with a number of outliers, identical observations and/or missing values above the respective threshold values.  When unmarked, the thresholds are ignored and process is performed, when possible.
 basicSpecUI.ppDesc.desc=Performs pre-processing of the series using the RegARIMA model.
 basicSpecUI.spanDesc.desc=Specifies a span (data interval) of the time series to be used for the seasonal adjustment/modelling process. When the user limits the original time series to a given span then only this span will be used in the computations.
-calendarSpecUI.easterDesc.desc=Options for an estimation of the Easter effect.
-calendarSpecUI.tdDesc.desc=Options for an estimation of the trading/working day effect and the leap year effect.
-easterSpecUI.durationDesc.desc=Duration (length in days, between 1 and 15) of the Easter effect.
+calendarSpecUI.easterDesc.desc=Specifies the Easter effect.
+calendarSpecUI.tdDesc.desc=Specifies the trading days, the working days and the leap year effects.
+easterSpecUI.durationDesc.desc=[w] Duration (length in days, between 1 and 20) of the Easter effect. 
+easterSpecUI.durationDesc.name=Duration
 easterSpecUI.enabledDesc.desc=When marked it enables the user to consider the Easter effect in the RegARIMA model.
 easterSpecUI.testDesc.desc=A t-test applied for the significance of the Easter effect. The Easter effect is considered as significant if the t-statistic is greater than 1.96.
 estimateSpecUI.spanDesc.desc=Specifies the span (data interval) of the time series to be used for the estimation of the RegARIMA model coefficients. The RegARIMA model is then applied to the whole series.
@@ -49,19 +51,21 @@ regArimaSpecUI.estimateDesc.desc=Controls the span of the time series to be used
 regArimaSpecUI.outlierDesc.desc=Options to perform an automatic detection of additive outliers, temporary change outliers, level shifts, seasonal outliers, or any combination of the four using the specified model.
 regArimaSpecUI.regressionDesc.desc=Options for an estimation of deterministic effects using the pre-defined regression variables.
 regArimaSpecUI.transformDesc.desc=Allows for transformation of the series prior to the estimation of the RegARIMA model.
-regressionSpecUI.calendarDesc.desc=Allows for an estimation of the calendar effects.
+regressionSpecUI.calendarDesc.desc=Determines the manner in which the calendar effects are entered in the RegARIMA model.
 regressionSpecUI.interventionDesc.desc=Allows for an estimation of the effects of the special events known a-priori.
 regressionSpecUI.prespecDesc.desc=Allows the user to include the pre-specified outliers (i.e. those for which the type and timing is known a-priori) in the RegARIMA model.
 regressionSpecUI.rampsDesc.desc=Allows the user to include ramp effects in the RegARIMA model. A ramp effect is a linear increase or decrease in the level of the series over a specified time interval.
 regressionSpecUI.rampsDesc.name=Ramp effects
 regressionSpecUI.userdefinedDesc.desc=Allows the user to include the user-defined variables (external regressors) in the RegARIMA model.
 tradingDaysSpecUI.autoDesc.desc=Option for an automatic correction for the leap year effect. It is available when the transformation function is set to Auto.
 tradingDaysSpecUI.holidaysDesc.desc=Enables for using the existing user-defined calendars to create the calendar regression variables.
-tradingDaysSpecUI.lpDesc.desc=Option for including the leap-year effect in the model when transformation has been set to none or log. The leap year effect can be modelled by a contrast variable (LeapYear); or by a length-of-month (or length-of-quarter) regression variable (LengthofPeriod) or not included in the model (None).
+tradingDaysSpecUI.lpDesc.desc=Option for including the leap-year effect in the model when transformation has been set to log. The leap year effect can be modelled by a contrast variable (LeapYear); or by a length-of-month (or length-of-quarter) regression variable (LengthofPeriod) or not included in the model (None).
+ tradingDaysSpecUI.lpDesc.name=Leap year
 tradingDaysSpecUI.optionDesc.desc=Specifies the type of a calendar being assigned to the series (Default \u2013 default calendar without country-specific holidays; Stock \u2013 day-of-week effects for inventories and other stock reported for the w-th day of the month; Holidays \u2013 the calendar variables based on user-defined calendar possibly with country specific holidays; UserDefined \u2013 calendar variables specified by the user) or excludes calendar variables from the regression model (None).
 tradingDaysSpecUI.stdDesc.desc=The parameter indicates day of the month when inventories and other stock are reported (to denote the last day of the month enter 31).
-tradingDaysSpecUI.tdDesc.desc=Assigns a type of model-estimated regression effect to pre-specified regression variables (TradingDays \u2013 six day-of-the-week regression variables; WorkingDays \u2013 one working/non-working day contrast variable).
-tradingDaysSpecUI.testDesc.desc=Pre-tests the significance of the trading day regression variables using the AICC statistics. The trading day variables are not included in the initial regression model but that they can be added to the RegARIMA model after the test (Add); or belong to the initial regression model but they can be removed from the RegARIMA model after the test (Remove); or are not pre-tested (None).
+tradingDaysSpecUI.tdDesc.desc=Defines the type of the trading days regression variables (TradingDays \u2013 six day-of-the-week regression variables; WorkingDays \u2013 one working/non-working day contrast variable).
+tradingDaysSpecUI.testDesc.desc=Pre-tests the significance of the trading day regression variables using the AICC statistics. The trading day variables: are not included in the initial regression model but can be added to the RegARIMA model after the test (Add); belong to the initial regression model but can be removed from the RegARIMA model after the test (Remove); or are not pre-tested (None). 
+tradingDaysSpecUI.testDesc.name=Test
 transformSpecUI.adjustDesc.desc=[adjust] Pre-adjustment of the series for length of period or leap year effects. The option is available when Function is set to Log.
 transformSpecUI.fnDesc.desc=[lam] Transformation of data (None - no transformation of data; Log - takes logs of data; Auto - the program tests for the log-level specification).
 x11SpecUI.autotrendmaDesc.desc=[trendma] When marked, it enables for an automatic selection of the length of the Henderson filter used for the estimation of the trend.

nbdemetra-sa/src/main/java/ec/nbdemetra/tramoseats/descriptors/RegressionSpecUI.java

@@ -171,7 +171,7 @@ private EnhancedPropertyDescriptor userdefinedDesc() {
 
     @Messages({
         "regressionSpecUI.calendarDesc.name=Calendar",
-        "regressionSpecUI.calendarDesc.desc=Calendar"
+        "regressionSpecUI.calendarDesc.desc=Calendar effects"
     })
     private EnhancedPropertyDescriptor calendarDesc() {
         try {

nbdemetra-sa/src/main/java/ec/nbdemetra/tramoseats/descriptors/TradingDaysSpecUI.java

@@ -247,7 +247,7 @@ private EnhancedPropertyDescriptor autoDesc() {
     }
 
     @Messages({"tradingDaysSpecUI.holidaysDesc.name=holidays",
-        "tradingDaysSpecUI.holidaysDesc.desc="
+        "tradingDaysSpecUI.holidaysDesc.desc=holidays"
     })
     private EnhancedPropertyDescriptor holidaysDesc() {
         if (inner().getHolidays() == null) {