New Features in v 3.x family

In this chapter

This chapter provides an overview of the new features in version 3.x as well as significant modification of display or content for features already available in v 2.2.4. Compared to its predecessor, version 3.x provides:

  • Additional Algorithms for Seasonal Adjustment (SA), Benchmarking and Temporal Disaggregation (TD), Nowcasting, Revision Analysis

  • More stand alone time series tools

  • More “acceptable” frequencies in SA

  • New SA (mass) production possibilities

Seasonal Adjustment and Modelling

Seasonal adjustment algorithms

Improvements on historical algorithms

Improvements X-13-Arima and Tramo-Seats (historical JD+)

  • New acceptable data frequencies for seasonal adjustment and modelling of low frequency data:

    - In v3.x Low frequency data: $p$ in ${2,3,4,6,12}$ is admissible in all
algorithms (historical and new)

- In version 2, only Tramo-Seats supported all these frequencies, whereas
X13-Arima was restricted to $p$ in ${2,4,12}$

  • outlier correction taken into account when selecting decomposition scheme

  • ex-ante leap year correction added to Tramo-Seats (like in X13)

  • automatic trading day regressors selection from pre-defined sets built, according to groups of days

  • specification: split into two distinct concepts, which can be directly manipulated by the user:

    • reference (or domain) specification: a global set of constraints inside of which estimation will be performed

    • point (or estimation) specification: contains all parameter choices resulting from estimation

The user can transform a given “estimation specification” in a user defined specification.

New algorithms in v3.x

Tramo-Seats and X-13-Arima share a very similar and sophisticated pre-adjustment process for the Arima model selection phase.

For new algorithms, the philosophy is to offer

  • a simplified pre-adjustment on the arima modelling side, reduced to airline model

  • several enhanced decomposition options

    • stl+ (“+” stands for airline based pre-adjustment)

    • x12+: airline based pre-adjustment + new trend estimation filters (Local Polynomials)

    • seats+ (to come in the target v3 version): airline based pre-adjustment + AMB decomposition

SA with Basic Structural Models (BSM) available in GUI

In version 3.x, SA with Basic Structural Models is a fully integrated process with outlier detection, calendar correction and options on external regressors.

Fundamentally it is a one-step estimation, performing pre-adjustment and decomposition (with explicit components) in the same run

This makes regression variable selection more complicated:

  • first step: a variable selection is performed with a Tramo like airline model regression

  • second: the entire structural model is estimated

In version 3.x this process is available from the graphical user interface.

BSM output view in GUI

BSM specification box

SA algorithms extended for high-frequency data

All algorithms are available via an R package and will be available in GUI (in target v 3.x version)

  • Extended Airline estimation, reg-Arima like (rjd3highfreq and GUI )

  • Extended Airline Decomposition, Seats like (rjd3highfreq and GUI )

  • MX12+ (rjd3x11plus, GUI upcoming)

  • MSTL+ (rjd3stl and in GUI)

  • MSTS (rjd3sts, GUI upcoming)

Modelling Algorithms

Algorithm Access in GUI Access in R (v2) Access in R (v3)
Reg-Arima ✔️ RJDemetra rjd3x13
Tramo ✔️ RJDemetra rjd3tramoseats
Extended Airline ✔️ (v3 only) ✖ rjd3highfreq
STS ✔️ (v3 only) rjdsts (deprecated) rjd3sts

New SA (mass) production possibilities

New R Tools for wrangling workspaces

With functions for

  • changing raw data path

  • customizing specifications

  • merging workspaces by series names, as you would do with a data table

These functions are in rjd3providers and rjd3workspace packages, (already in a v 2.x stable precursor rjdworkspace)

Production fully in R

without a workspace structure

  • TS objects and full flexibility for customizing specifications

  • new R functions enabling to apply revision policies (rjd3x13::refresh and rjd3tramoseats::refresh), with even more flexibility on data spans

Inherent shortcoming: data no readable by GUI, depriving of more sophisticated and visual feedback (compared to R) for manual fine tuning.

Solution : new R functions to create GUI readable dynamic workspaces on the fly (in aforementioned packages).

In the target 3.x, additional algorithms (X12+, STL+, BSM) will also be usable in production with a workspace and cruncher (on low frequency data)

Time series general purpose tools

Version 3.x offers more stand alone tools (mainly in rjd3toolkit)

  • Tests (seasonality, auto-correlation, normality, randomness…)

  • (Fast) Arima Modelling

  • Flexible Calendar regressors generation

  • Auxiliary variables for pre-adjustment

  • Spectral analysis (in GUI)

  • Detection of multiple seasonal patterns (Canova-Hansen test)

  • State space frame work as a toolbox (rjd3sts)

Canova-Hansen test to identify multiple seasonal patterns

rjd3toolkit::seasonality_canovahansen(data = df_daily$births, 
    p0 = min(ch.sp), p1 = max(ch.sp), np = max(ch.sp) - min(ch.sp) + 1)

Canova Hansen seasonality test

Underway developments

  • Moving Trading Days module integrated in all SA algorithms (for low and high frequency data), with two implementations one based on rolling windows and one on state space modelling

  • Using Cubic Splines for smoother seasonal factors estimation of long periodicities (\(p=365,25\))