Package 'rjd3sts' reference manual

Title:	State Space Framework and Structural Time Series with 'JDemetra+ 3.x'
Description:	R Interface to 'JDemetra+ 3.x' (<https://github.com/jdemetra>) time series analysis software. It offers access to several functions on state space models and structural time series.
Authors:	Jean Palate [aut, cre], Tanguy Barthelemy [art]
Maintainer:	Jean Palate <[email protected]>
License:	EUPL
Version:	2.1.1.9000
Built:	2024-09-10 05:25:49 UTC
Source:	https://github.com/rjdverse/rjd3sts

Title

Description

Title

Usage

add(model, item)
add(model, item)

Arguments

item

Add a building block to the considered equation

Description

Add a building block to the considered equation

Usage

add_equation(equation, item, coeff = 1, fixed = TRUE, loading = NULL)
add_equation(equation, item, coeff = 1, fixed = TRUE, loading = NULL)

Arguments

`equation`	the equation
`item`	the block of the state array that will be linked to the observation corresponding to this equation through the specified loading and coefficient
`coeff`	the value of the coefficient associated to the block of latent variables defined by `item`.
`fixed`	logical that triggers estimation of coeff (FALSE) or fixes it (TRUE) to a pre-specified value
`loading`	the loading that links the block to the observation

Title

Description

Title

Usage

aggregation(name, components)
aggregation(name, components)

Arguments

components

Autoregressive model

Description

Functions to create an autoregressive model (ar) or a modified autoregressive model (ar2)

Usage

ar(
  name,
  ar,
  fixedar = FALSE,
  variance = 0.01,
  fixedvariance = FALSE,
  nlags = 0,
  zeroinit = FALSE
)

ar2(
  name,
  ar,
  fixedar = FALSE,
  variance = 0.01,
  fixedvariance = FALSE,
  nlags = 0,
  nfcasts = 0
)
ar(
  name,
  ar,
  fixedar = FALSE,
  variance = 0.01,
  fixedvariance = FALSE,
  nlags = 0,
  zeroinit = FALSE
)

ar2(
  name,
  ar,
  fixedar = FALSE,
  variance = 0.01,
  fixedvariance = FALSE,
  nlags = 0,
  nfcasts = 0
)

Arguments

`ar`	vector of the AR coefficients ( $\varphi_1, \dots, \varphi_p$ ).
`fixedar`	boolean that triggers the estimation of the AR coefficients (`FALSE`) or fixed it (`TRUE`) to a pre-specified value set by the parameter `ar`.
`variance`	the variance ( $\sigma^2_{ar}$ ).
`fixedvariance`	boolean that triggers the estimation of the variance (`FALSE`) or fixed it (`TRUE`) to a pre-specified value set by the parameter `variance`.
`nlags`	integer specifying how many lags of the state variable are needed
`zeroinit`	boolean determining the initial condition for the state variable, which is equal to zero if `zeroinit = TRUE`. The default (`zeroinit = FAKSE`) triggers the an initialization based on the unconditional mean and variance of the AR(p) process.
`nfcasts`	integer specifying how many forecasts of the state variable are needed

Details

The AR process is defined by

$\Phi\left(B\right)y_t=\epsilon_t$

where

$\Phi\left(B\right)=1+\varphi_1 B + \cdots + \varphi_p B^p$

is an auto-regressive polynomial.

Autoregressive Integrated Moving Average (ARIMA) Model

Description

Autoregressive Integrated Moving Average (ARIMA) Model

Usage

arima(name, ar, diff, ma, var = 1, fixed = FALSE)
arima(name, ar, diff, ma, var = 1, fixed = FALSE)

Arguments

fixed

Autoregressive Moving Average (ARMA) Model

Description

Autoregressive Moving Average (ARMA) Model

Usage

arma(name, ar, fixedar = FALSE, ma, fixedma = FALSE, var = 1, fixedvar = FALSE)
arma(name, ar, fixedar = FALSE, ma, fixedma = FALSE, var = 1, fixedvar = FALSE)

Arguments

fixedvar

Title

Description

Title

Usage

cumul(name, core, period, start = 0)
cumul(name, core, period, start = 0)

Arguments

start

Title

Description

Title

Usage

cycle(
  name,
  factor = 0.9,
  period = 60,
  fixed = FALSE,
  variance = 0.01,
  fixedvariance = FALSE
)
cycle(
  name,
  factor = 0.9,
  period = 60,
  fixed = FALSE,
  variance = 0.01,
  fixedvariance = FALSE
)

Arguments

fixedvariance

Create equation

Description

Create equation

Usage

equation(name, variance = 0, fixed = TRUE)
equation(name, variance = 0, fixed = TRUE)

Arguments

fixed

Estimate a SSF Model

Description

Estimate a SSF Model

Usage

estimate(
  model,
  data,
  marginal = FALSE,
  concentrated = TRUE,
  initialization = c("Augmented_Robust", "Diffuse", "SqrtDiffuse", "Augmented",
    "Augmented_NoCollapsing"),
  optimizer = c("LevenbergMarquardt", "MinPack", "BFGS", "LBFGS"),
  precision = 1e-15,
  initialParameters = NULL
)
estimate(
  model,
  data,
  marginal = FALSE,
  concentrated = TRUE,
  initialization = c("Augmented_Robust", "Diffuse", "SqrtDiffuse", "Augmented",
    "Augmented_NoCollapsing"),
  optimizer = c("LevenbergMarquardt", "MinPack", "BFGS", "LBFGS"),
  precision = 1e-15,
  initialParameters = NULL
)

Arguments

`model`	the model
`data`	a matrix containing the data (one time series per column, time series dimension on the rows)
`marginal`	logical value used to specify whether the marginal likelihood definition is used (TRUE) or not (FALSE) during the optimization. The marginal likelihood is recommended when there is at least one variable that loads on a non-stationary latent variable and the loading coefficient needs to be estimated.
`concentrated`	logical value used to specify whether the likelihood is concentrated (TRUE) or not (FALSE) during the optimization
`initialization`	initialization method.
`precision`	indicating the largest likelihood deviations that make the algorithm stop.
`initialParameters`

Title

Description

Title

Usage

filtered_states(model)
filtered_states(model)

Arguments

model

Title

Description

Title

Usage

filtered_states_stdev(model)
filtered_states_stdev(model)

Arguments

model

Title

Description

Title

Usage

filtering_states(model)
filtering_states(model)

Arguments

model

Title

Description

Title

Usage

filtering_states_stdev(model)
filtering_states_stdev(model)

Arguments

model

Title

Description

Title

Usage

loading(pos = NULL, weights = NULL)

loading(pos = NULL, weights = NULL)
loading(pos = NULL, weights = NULL)

loading(pos = NULL, weights = NULL)

Arguments

`pos`	defines the position of each one of the elements of the block of states defined. `NULL` indicates by default the first state included in the block (pos=0)
`weights`	defines the weights associated to each one of the state variables included in the block.
`obs`

Title

Description

Title

Usage

loading_cyclical(period, startpos)
loading_cyclical(period, startpos)

Arguments

startpos

Title

Description

Title

Usage

loading_periodic(period, startpos)
loading_periodic(period, startpos)

Arguments

startpos

Title

Description

Title

Usage

loading_sum(length = 0)
loading_sum(length = 0)

Arguments

length

Local Level

Description

Local Level

Usage

locallevel(name, variance = 0.01, fixed = FALSE, initial = NaN)
locallevel(name, variance = 0.01, fixed = FALSE, initial = NaN)

Arguments

`name`	name of the component.
`variance`	the value of the variance ( $\sigma^2_l$ ).
`fixed`	boolean that triggers estimation of $\sigma^2_l$ (`FALSE`) or fixes it (`TRUE`) to a pre-specified value set by the parameter `variance`.
`initial`	initial value of the level ( $l_0$ ).

Details

$\begin{cases}l_{t+1} = l_t + \mu_t \\ \mu_t \sim N(0, \sigma^2 \sigma^2_l) \end{cases}$

Local Linear Trend

Description

Local Linear Trend

Usage

locallineartrend(
  name,
  levelVariance = 0.01,
  slopevariance = 0.01,
  fixedLevelVariance = FALSE,
  fixedSlopeVariance = FALSE
)
locallineartrend(
  name,
  levelVariance = 0.01,
  slopevariance = 0.01,
  fixedLevelVariance = FALSE,
  fixedSlopeVariance = FALSE
)

Arguments

`name`	name of the component.
`levelVariance`	variance of the level ( $\sigma^2_l$ )
`fixedLevelVariance`, `fixedSlopeVariance`	boolean that triggers the estimation of the variances $\sigma^2_l$ and $\sigma^2_n$ (`FALSE`) or fixes it (`TRUE`) to a pre-specified value set by the parameters `levelVariance` and `slopevariance`.

Details

$\begin{cases}l_{t+1} = l_t + n_t + \xi_t \\ n_{t+1} = n_t + \mu_t \\ \xi_t \sim N(0, \sigma^2\sigma^2_l)\\ \mu_t \sim N(0, \sigma^2\sigma^2_n) \end{cases}$

Create Composite Model

Description

Create Composite Model

Usage

model()
model()

Modeling errors in surveys with overlapping panels

Description

Modeling errors in surveys with overlapping panels

Usage

msae(name, nwaves, ar, fixedar = TRUE, lag = 1)

msae2(name, vars, fixedvars = FALSE, ar, fixedar = TRUE, lag = 1)

msae3(name, vars, fixedvars = FALSE, ar, fixedar = TRUE, k, lag = 1)
msae(name, nwaves, ar, fixedar = TRUE, lag = 1)

msae2(name, vars, fixedvars = FALSE, ar, fixedar = TRUE, lag = 1)

msae3(name, vars, fixedvars = FALSE, ar, fixedar = TRUE, k, lag = 1)

Arguments

`name`	name of the component.
`nwaves`	integer representing the number of waves
`ar`	matrix representing the covariance structure of the wave specific survey error.
`fixedar`	logical that triggers the estimation of the correlation patterns (`TRUE`) or fixes them to the values given by the entries `ar` (`FALSE`)
`lag`	integer specifying the number of time periods (in the base frequency) that compose the survey period. This coincides with the number of time periods an individual has to wait between two different waves. Note that if the survey period is one quarter, all of them have already responded in the previous wave exactly 3 months ago (because individuals are always interviewed at the same stint during each survey period).

Title

Description

Title

Usage

msignal(object, m, pos = NULL, stdev = FALSE)
msignal(object, m, pos = NULL, stdev = FALSE)

Arguments

stdev

Noise component

Description

Noise component

Usage

noise(name, variance = 0.01, fixed = FALSE)
noise(name, variance = 0.01, fixed = FALSE)

Arguments

fixed

Get Parameters of SSF Model

Description

Get Parameters of SSF Model

Usage

parameters(model)
parameters(model)

Arguments

model

Title

Description

Title

Usage

periodic(name, period, harmonics, variance = 0.01, fixedvariance = FALSE)
periodic(name, period, harmonics, variance = 0.01, fixedvariance = FALSE)

Arguments

fixedvariance

Title

Description

Title

Usage

## S3 method for class 'JD3STS'
print(x, ...)
## S3 method for class 'JD3STS'
print(x, ...)

Arguments

`x`
`...`

Time Varying Regressors

Description

Time Varying Regressors

Usage

reg(name, x, var = NULL, fixed = FALSE)
reg(name, x, var = NULL, fixed = FALSE)

Arguments

`x`	matrix containing the regressors
`fixed`

Title

Description

Title

Usage

reg_td(
  name,
  period,
  start,
  length,
  groups = c(1, 2, 3, 4, 5, 6, 0),
  contrast = TRUE,
  variance = 1,
  fixed = FALSE
)
reg_td(
  name,
  period,
  start,
  length,
  groups = c(1, 2, 3, 4, 5, 6, 0),
  contrast = TRUE,
  variance = 1,
  fixed = FALSE
)

Arguments

fixed

Title

Description

Title

Usage

sae(name, ar, fixedar = FALSE, lag = 1, zeroinit = FALSE)
sae(name, ar, fixedar = FALSE, lag = 1, zeroinit = FALSE)

Arguments

zeroinit

Title

Description

Title

Usage

sarima(
  name,
  period,
  orders,
  seasonal,
  parameters = NULL,
  fixedparameters = FALSE,
  var = 1,
  fixedvariance = FALSE
)
sarima(
  name,
  period,
  orders,
  seasonal,
  parameters = NULL,
  fixedparameters = FALSE,
  var = 1,
  fixedvariance = FALSE
)

Arguments

fixedvariance

Title

Description

Title

Usage

seasonal(
  name,
  period,
  type = c("Trigonometric", "Crude", "HarrisonStevens", "Dummy"),
  variance = 0.01,
  fixed = FALSE
)
seasonal(
  name,
  period,
  type = c("Trigonometric", "Crude", "HarrisonStevens", "Dummy"),
  variance = 0.01,
  fixed = FALSE
)

Arguments

fixed

Title

Description

Title

Usage

seasonalbreaks(
  y,
  period = NA,
  level = 1,
  slope = 1,
  noise = 1,
  seasonal = c("HarrisonStevens", "Trigonometric", "Dummy", "Crude", "Fixed", "Unused"),
  X = NULL,
  X.td = NULL
)
seasonalbreaks(
  y,
  period = NA,
  level = 1,
  slope = 1,
  noise = 1,
  seasonal = c("HarrisonStevens", "Trigonometric", "Dummy", "Crude", "Fixed", "Unused"),
  X = NULL,
  X.td = NULL
)

Arguments

`y`	input time series.
`period`	annual frequency.
`level`	-1 = no level, 0 = fixed level, 1 = sotchastic level
`slope`
`noise`
`seasonal`	Seasonal model
`X`	Regression variables (same length as y) or NULL
`X.td`	Groups of days for trading days regressors. The length of the array must be 7. It indicates to what group each week day belongs. The first item corresponds to Mondays and the last one to Sundays. The group used for contrasts (usually Sundays) is identified by 0. The other groups are identified by 1, 2,... n (<= 6). For instance, usual trading days are defined by `c(1,2,3,4,5,6,0)`, week days by `c(1,1,1,1,1,0,0)`, etc...

Examples

 x<-rjd3toolkit::retail$BookStores
 seasonalbreaks(x)
x<-rjd3toolkit::retail$BookStores
 seasonalbreaks(x)

Title

Description

Title

Usage

signal(object, obs = 1, pos = NULL, loading = NULL, stdev = FALSE)
signal(object, obs = 1, pos = NULL, loading = NULL, stdev = FALSE)

Arguments

stdev

Retrieves the components of the model (univariate case) or the components corresponding to a given equation (multivariate case)

Description

Retrieves the components of the model (univariate case) or the components corresponding to a given equation (multivariate case)

Usage

smoothed_components(model, equation = 1, fast = TRUE)
smoothed_components(model, equation = 1, fast = TRUE)

Arguments

`model`	Estimated state space model
`equation`	Equation containing the components
`fast`	if true, only the components are computed. Otherwise, their stdev are also computed (not returned but available for future use).

Value

A matrix with the components

Retrieves the stdev of the components of the model (univariate case) or of the components corresponding to a given equation (multivariate case)

Description

Retrieves the stdev of the components of the model (univariate case) or of the components corresponding to a given equation (multivariate case)

Usage

smoothed_components_stdev(model, equation = 1)
smoothed_components_stdev(model, equation = 1)

Arguments

`model`	Estimated state space model
`equation`	Equation containing the components

Value

A matrix with the stdev of the components

Title

Description

Title

Usage

smoothed_states(model)
smoothed_states(model)

Arguments

model

Title

Description

Title

Usage

smoothed_states_stdev(model)
smoothed_states_stdev(model)

Arguments

model

Title

Description

Title

Usage

splines_daily(name, startYear, nodes, start = 1, variance = 1, fixed = FALSE)
splines_daily(name, startYear, nodes, start = 1, variance = 1, fixed = FALSE)

Arguments

fixed

Title

Description

Title

Usage

splines_regular(
  name,
  period,
  nnodes = 0,
  nodes = NULL,
  start = 1,
  variance = 1,
  fixed = FALSE
)
splines_regular(
  name,
  period,
  nnodes = 0,
  nodes = NULL,
  start = 1,
  variance = 1,
  fixed = FALSE
)

Arguments

fixed

Title

Description

Title

Usage

ssf(initialization, dynamics, measurement)
ssf(initialization, dynamics, measurement)

Arguments

measurement

Title

Description

Title

Usage

sts(
  y,
  X = NULL,
  X.td = NULL,
  level = 1,
  slope = 1,
  cycle = -1,
  noise = 1,
  seasonal = c("Trigonometric", "Dummy", "Crude", "HarrisonStevens", "Fixed", "Unused"),
  diffuse.regs = TRUE,
  tol = 1e-09
)
sts(
  y,
  X = NULL,
  X.td = NULL,
  level = 1,
  slope = 1,
  cycle = -1,
  noise = 1,
  seasonal = c("Trigonometric", "Dummy", "Crude", "HarrisonStevens", "Fixed", "Unused"),
  diffuse.regs = TRUE,
  tol = 1e-09
)

Arguments

`y`	input time series.
`X`	Regression variables (same length as y) or NULL
`X.td`	Groups of days for trading days regressors. The length of the array must be 7. It indicates to what group each week day belongs. The first item corresponds to Mondays and the last one to Sundays. The group used for contrasts (usually Sundays) is identified by 0. The other groups are identified by 1, 2,... n (<= 6). For instance, usual trading days are defined by `c(1,2,3,4,5,6,0)`, week days by `c(1,1,1,1,1,0,0)`, etc...
`level`	-1 = no level, 0 = fixed level, 1 = sotchastic level
`slope`
`cycle`
`noise`
`seasonal`	Seasonal model
`diffuse.regs`
`tol`

Examples

 x<-rjd3toolkit::retail$BookStores
 sts(x)
x<-rjd3toolkit::retail$BookStores
 sts(x)

Forecast with STS model

Description

Forecast with STS model

Usage

sts_forecast(y, model = c("none", "td2", "td3", "td7", "full"), nf = 12)
sts_forecast(y, model = c("none", "td2", "td3", "td7", "full"), nf = 12)

Arguments

y

Series

model

Model for calendar effects

td2: leap year + week days (week-end derived)
td3: leap year + week days + saturdays (sundays derived)
td7: leap year + all days (sundays derived)
full: td3 + easter effect
none: no calendar effect

nf

number of forecasts

Examples

fcasts<-sts_forecast(rjd3toolkit::ABS$X0.2.09.10.M)
fcasts<-sts_forecast(rjd3toolkit::ABS$X0.2.09.10.M)

Title

Description

Title

Usage

sts_outliers(
  y,
  period = NA,
  X = NULL,
  X.td = NULL,
  level = 1,
  slope = 1,
  noise = 1,
  seasonal = c("Trigonometric", "Dummy", "Crude", "HarrisonStevens", "Fixed", "Unused"),
  ao = TRUE,
  ls = TRUE,
  so = FALSE,
  cv = 0,
  tcv = 0,
  estimation.forward = c("Score", "Point", "Full"),
  estimation.backward = c("Point", "Score", "Full")
)
sts_outliers(
  y,
  period = NA,
  X = NULL,
  X.td = NULL,
  level = 1,
  slope = 1,
  noise = 1,
  seasonal = c("Trigonometric", "Dummy", "Crude", "HarrisonStevens", "Fixed", "Unused"),
  ao = TRUE,
  ls = TRUE,
  so = FALSE,
  cv = 0,
  tcv = 0,
  estimation.forward = c("Score", "Point", "Full"),
  estimation.backward = c("Point", "Score", "Full")
)

Arguments

`y`	input time series.
`period`	annual frequency.
`X`	Regression variables (same length as y) or NULL
`X.td`	Groups of days for trading days regressors. The length of the array must be 7. It indicates to what group each week day belongs. The first item corresponds to Mondays and the last one to Sundays. The group used for contrasts (usually Sundays) is identified by 0. The other groups are identified by 1, 2,... n (<= 6). For instance, usual trading days are defined by `c(1,2,3,4,5,6,0)`, week days by `c(1,1,1,1,1,0,0)`, etc...
`level`	-1 = no level, 0 = fixed level, 1 = sotchastic level
`slope`
`noise`
`seasonal`	Seasonal model
`ao`, `ls`, `so`	boolean indicating if additive outliers (`ao`), level shift (`ls`) and seasonal outliers (`so`) should be detected.
`cv`
`tcv`
`estimation.forward`
`estimation.backward`

Examples

 x<-rjd3toolkit::retail$BookStores
 sts_outliers(x)
x<-rjd3toolkit::retail$BookStores
 sts_outliers(x)

Title

Description

Title

Usage

sts_raw(
  y,
  period = NA,
  X = NULL,
  X.td = NULL,
  level = 1,
  slope = 1,
  cycle = -1,
  noise = 1,
  seasonal = c("Trigonometric", "Dummy", "Crude", "HarrisonStevens", "Fixed", "Unused"),
  diffuse.regs = TRUE,
  tol = 1e-09
)
sts_raw(
  y,
  period = NA,
  X = NULL,
  X.td = NULL,
  level = 1,
  slope = 1,
  cycle = -1,
  noise = 1,
  seasonal = c("Trigonometric", "Dummy", "Crude", "HarrisonStevens", "Fixed", "Unused"),
  diffuse.regs = TRUE,
  tol = 1e-09
)

Arguments

`y`
`period`
`X`
`X.td`
`level`
`slope`
`cycle`
`noise`
`seasonal`
`diffuse.regs`
`tol`

Title

Description

Title

Usage

var_loading(pos, weights)
var_loading(pos, weights)

Arguments

weights

Title

Description

Title

Usage

var_locallevel(name, std, scale = 1, fixed = FALSE, initial = NaN)
var_locallevel(name, std, scale = 1, fixed = FALSE, initial = NaN)

Arguments

initial

Title

Description

Title

Usage

var_locallineartrend(
  name,
  lstd,
  sstd = NULL,
  levelScale = 1,
  slopeScale = 1,
  fixedLevelScale = FALSE,
  fixedSlopeScale = FALSE
)
var_locallineartrend(
  name,
  lstd,
  sstd = NULL,
  levelScale = 1,
  slopeScale = 1,
  fixedLevelScale = FALSE,
  fixedSlopeScale = FALSE
)

Arguments

fixedSlopeScale

Title

Description

Title

Usage

var_noise(name, std, scale = 1, fixed = FALSE)
var_noise(name, std, scale = 1, fixed = FALSE)

Arguments

fixed

Time Varying Regressor

Description

Time Varying Regressor

Usage

var_reg(name, x, stderr, scale = 1, fixed = FALSE)
var_reg(name, x, stderr, scale = 1, fixed = FALSE)

Arguments

`x`	Regression variable. Numerics
`stderr`	Standard error of the innovations of the coefficient (1 in extrapolation)
`scale`	Scaling factor
`fixed`	Fixed scaling factor

Examples

 x<-rjd3toolkit::retail$BookStores
 std<-rep(1, length(x))
 std[c(20, 50, 150)]<-5
 v<-var_reg("vx", x, std, 0.1)
x<-rjd3toolkit::retail$BookStores
 std<-rep(1, length(x))
 std[c(20, 50, 150)]<-5
 v<-var_reg("vx", x, std, 0.1)

Title

Description

Title

Usage

var_seasonal(
  name,
  period,
  type = c("Trigonometric", "Crude", "HarrisonStevens", "Dummy"),
  std,
  scale = 1,
  fixed = FALSE
)
var_seasonal(
  name,
  period,
  type = c("Trigonometric", "Crude", "HarrisonStevens", "Dummy"),
  std,
  scale = 1,
  fixed = FALSE
)

Arguments

fixed

Package 'rjd3sts'

Help Index

Title

Description

Usage

Arguments

Add a building block to the considered equation

Description

Usage

Arguments

Title

Description

Usage

Arguments

Autoregressive model

Description

Usage

Arguments

Details

Autoregressive Integrated Moving Average (ARIMA) Model

Description

Usage

Arguments

Autoregressive Moving Average (ARMA) Model

Description

Usage

Arguments

Title

Description

Usage

Arguments

Title

Description

Usage

Arguments

Create equation

Description

Usage

Arguments

Estimate a SSF Model

Description

Usage

Arguments

Title

Description

Usage

Arguments

Title

Description

Usage

Arguments

Title

Description

Usage

Arguments

Title

Description

Usage

Arguments

Title

Description

Usage

Arguments

Title

Description

Usage

Arguments

Title

Description

Usage

Arguments

Title

Description

Usage

Arguments

Local Level

Description

Usage

Arguments

Details