Introduction | Definition of the JVS QR | Version 2 | Version 3 | Description | Creation of the output files | Creation of report JVS | Export | Comparison: v3 / R | X | Method | Period, nobs, Start | End | Adjustment | Tests | Log transformation, ARIMA Model, Leapyear, MovingHoliday, NbTD, Noutliers, Outlier1, Outlier2, Outlier3, Q-stat for X13, Stage 2 Henderson Filter, Final Henderson Filter, Seasonal.Filter | Irregular Standard Deviation | Quality for TS, Max adj, Autocorrelation of order 1 of the SA series | Normal test | Negative and significant autocorrelation

Introduction | Definition of the JVS QR | Version 2 | Version 3 | Dsecription | Création des sorties de JDemetra+ | Creation du rapport JVS | Export | Comparaison : v3 / R | X | Method | Period, nobs, Start | End | Adjustment | Tests | Log transformation, ARIMA Model, Leapyear, MovingHoliday, NbTD, Noutliers, Outlier1, Outlier2, Outlier3, Q-stat for X13, Stage 2 Henderson Filter, Final Henderson Filter, Seasonal.Filter | Irregular Standard Deviation | Quality for TS, Max adj, Autocorrelation of order 1 of the SA series | Normal test | Negative and significant autocorrelation

Selection of calendar regressors | Creating a workspace | Call from the cruncher

Sélection des régresseurs de calendrier | Création d'un workspace | Appel du cruncher

Sélection des régresseurs | Import / Export | Assignation | Sélection avancée

Regressor selection | Import/Export | Assignment | Advanced selection

Introduction | Set-up & Data | Temporal disaggregation and interpolation methods | Chow-Lin, Fernandez and Litterman | Model-based Denton | Autoregressive Distributed Lag (ADL) Models | Reverse regression | Benchmarking methods | Denton | Growth rate preservation (GRP) | Cubic splines | Cholette method | Reconciliation and multivariate temporal disaggregation | Multivariate Cholette | Multivariate Chow-Lin / Fernandez | Calendarization | References

Introduction | Installation settings | Usage | 1. Prepare and import data | 2. Create/Update model | 2.1. Create a new model | 2.2. Update an existing model | 2.3. Composition of the created object | 3. Estimation | 3.1. Different algorithms/functions | 3.2. Prior standardization of the data | 3.3. Fixed parameters | 3.4. Save R object from one time to another | 4. Results | 4.1. Estimates | 4.2. Forecasts | 4.3. News analysis | References

Defining a Basic Structural Model with rjd3sts | Standard definition, noise in the state | Standard definition, noise in the measurement | components with fixed variances, aggregated with diffuse weights (noise in the state) | bsm with long term trend and cycle

Introduction | Definition of the model in rjd3sts | Main results | UCARIMA model | BSM model | Trend estimates

Introduction | Data | Overview of the supported state space forms | Description of the state blocks | ar | State block: | Dynamics | Default loading | Initialization | R code | ar2 | arma | arima | Examples | Basic structural models | Time varying trading days | Regular period cubic splines | Modeling Nile riverflow | Time series with a sampling error

Introduction

Introduction | Definition of the model in rjd3sts | Main results | Estimated parameters | Original series (solid) and signal extraction estimates (dashed) | Signal extraction estimates of the sampling error | Sampling error and signal extraction error | CV improvement due to signal extraction

Introduction | Installation settings | Usage | Input data | Format 1: long view | Format 2: vertical view | Format 3: horizontal view | Processing | Details on the main functions | Vintages & revisions | Revision analysis & reporting | Output | User-defined thresholds | Tests description and interpretation | Relevancy | Theil’s Inequality Coefficient | Bias | T-test and Augmented T-test | Slope and drift | Efficiency | Regression of revisions on previous estimates | Regression of revisions from latter vintages on revisions from the previous vintages | Orthogonality | Regression of latter revisions (Rv) on previous revisions (Rv_1, Rv_2,...Rv_p) | Regression of latter revisions (Rv) on revisions from a specific vintage (Rv_k) | Test of autocorrelations in revisions | Test of seasonality in revisions | Signal vs Noise | References

Use of regular periodic cubic splines | Usual BSM with Harrison-Stevens seasonal component | BSM with full periodic splines seasonal component | BSM with partial periodic splines seasonal component

Introduction | Modelling | SARIMA + TDvar | Available results | BSM + TDvar