Package 'rjd3nowcasting' reference manual

Title:	Nowcasting with 'JDemetra+ 3.0'
Description:	Interface around 'JDemetra+ 3.x' (<https://github.com/jdemetra/jdplus-nowcasting>), TSACE project. It defines and estimates Dynamic Factor Models with the purpose of Nowcasting. News analysis is included in this second version.
Authors:	Corentin Lemasson [aut, cre], Tanguy Barthelemy [ctb, art]
Maintainer:	Corentin Lemasson <[email protected]>
License:	EUPL
Version:	2.0.3
Built:	2024-11-09 05:23:42 UTC
Source:	https://github.com/rjdverse/rjd3nowcasting

Create Dynamic Factor Model

Description

Create Dynamic Factor Model

Usage

create_model(
  nfactors,
  nlags,
  factors_type,
  factors_loading,
  var_init = c("Unconditional", "Zero"),
  var_coefficients = NULL,
  var_errors_variance = NULL,
  measurement_coefficients = NULL,
  measurement_errors_variance = NULL
)
create_model(
  nfactors,
  nlags,
  factors_type,
  factors_loading,
  var_init = c("Unconditional", "Zero"),
  var_coefficients = NULL,
  var_errors_variance = NULL,
  measurement_coefficients = NULL,
  measurement_errors_variance = NULL
)

Arguments

`nfactors`	Integer. Number of factors.
`nlags`	Integer. Number of lags in VAR equations.
`factors_type`	Character vector. Respecting the order of the series in the input data, you must refer here the link between the (transformed) series and the factors. Three options are possible: "M": Variables expressed in terms of monthly growth rates can be linked to a factor representing the underlying monthly growth rate of the economy if "M" is selected "Q": Monthly or quarterly variables that are correlated with the the underlying quarterly growth rate of the economy can be linked to a weighted average of the factors representing the underlying monthly growth rate of the economy. Such a weighted average is meant to represent quarterly growth rates, and it is implemented by selecting "Q" "YoY": The variables can also be linked to the cumulative sum of the last 12 monthly factors. If the model is designed in such a way that the monthly factors represent monthly growth rates, the resulting cumulative sum boils down to the year-on-year growth rate. Thus, variables expressed in terms of year-on-year growth rates or surveys that are correlated with the year-on-year growth rates of the reference series should be linked to the factors using "YoY".
`factors_loading`	Boolean matrix. It represents the factor loading structure. The dimension of the matrix should be 'number of series' x 'number of factors'. For each row representing each series, the user must mention whether the corresponding factor loads on this series.
`var_init`	Character. The first unobserved factors values in the sample is assumed to be either equal to zero or consistent with a normal distribution with mean zero and a variance corresponding to the unconditional variance of the VAR. The latter is the default.
`var_coefficients`	Matrix. The default is NULL meaning that the VAR coefficients will be estimated from scratch. Alternatively, a matrix of pre-defined values can be passed in. Those would come typically from a previous model estimate and will serve as a starting point for the estimation step. The format of the matrix should be the same as the one produced by default by the create_model() function while keeping the 'var_coefficients' argument to its default value NULL.
`var_errors_variance`	Matrix. The default is NULL meaning that the VAR errors variance will be estimated from scratch. Alternatively, a matrix of pre-defined values can be passed in. Those would come typically from a previous model estimate and will serve as a starting point for the estimation step. The format of the matrix should be the same as the one produced by default by the create_model() function while keeping the 'var_errors_variance' argument to its default value NULL.
`measurement_coefficients`	Matrix. The default is NULL meaning that the measurement coefficients will be estimated from scratch. Alternatively, a matrix of pre-defined values can be passed in. Those would come typically from a previous model estimate and will serve as a starting point for the estimation step. The format of the matrix should be the same as the one produced by default by the create_model() function while keeping the 'measurement_coefficients' argument to its default value NULL.
`measurement_errors_variance`	Numeric vector. The default is NULL meaning that the measurement errors variance will be estimated from scratch. Alternatively, a vector of pre-defined values can be passed in. Those would come typically from a previous model estimate and will serve as a starting point for the estimation step. The format of the vector should be the same as the one produced by default by the create_model() function while keeping the 'measurement_errors_variance' argument to its default value NULL.

Value

an object of class 'JD3_DfmModel'

Examples


# From scratch
dfm1 <- create_model(nfactors=2,
                     nlags=2,
                     factors_type = c("M", "M", "YoY", "M", "Q"),
                     factors_loading = matrix(data=TRUE, 5, 2),
                     var_init = "Unconditional")

# From a previous estimate
set.seed(100)
data<-ts(matrix(rnorm(500), 100, 5), frequency = 12, start = c(2010,1))
data[100,1]<-data[99:100,2]<-data[(1:100)[-seq(3,100,3)],5]<-NA
est1<-estimate_em(dfm1, data)

dfm2 <- create_model(nfactors=2,
                     nlags=2,
                     factors_type = c("M", "M", "YoY", "M", "Q"),
                     factors_loading = matrix(data=TRUE, 5, 2),
                     var_init = "Unconditional",
                     var_coefficients = est1$dfm$var_coefficients,
                     var_errors_variance = est1$dfm$var_errors_variance,
                     measurement_coefficients = est1$dfm$measurement_coefficients,
                     measurement_errors_variance = est1$dfm$measurement_errors_variance)
#est2<-estimate_em(dfm2, data)

# From scratch
dfm1 <- create_model(nfactors=2,
                     nlags=2,
                     factors_type = c("M", "M", "YoY", "M", "Q"),
                     factors_loading = matrix(data=TRUE, 5, 2),
                     var_init = "Unconditional")

# From a previous estimate
set.seed(100)
data<-ts(matrix(rnorm(500), 100, 5), frequency = 12, start = c(2010,1))
data[100,1]<-data[99:100,2]<-data[(1:100)[-seq(3,100,3)],5]<-NA
est1<-estimate_em(dfm1, data)

dfm2 <- create_model(nfactors=2,
                     nlags=2,
                     factors_type = c("M", "M", "YoY", "M", "Q"),
                     factors_loading = matrix(data=TRUE, 5, 2),
                     var_init = "Unconditional",
                     var_coefficients = est1$dfm$var_coefficients,
                     var_errors_variance = est1$dfm$var_errors_variance,
                     measurement_coefficients = est1$dfm$measurement_coefficients,
                     measurement_errors_variance = est1$dfm$measurement_errors_variance)
#est2<-estimate_em(dfm2, data)

Estimate DFM with Expectations-Maximization algorithm

Description

Estimate DFM with Expectations-Maximization algorithm

Usage

estimate_em(
  dfm,
  data,
  standardized = FALSE,
  input_standardization = NULL,
  pca_init = TRUE,
  max_iter = 100,
  eps = 1e-09,
  re_estimate = TRUE
)
estimate_em(
  dfm,
  data,
  standardized = FALSE,
  input_standardization = NULL,
  pca_init = TRUE,
  max_iter = 100,
  eps = 1e-09,
  re_estimate = TRUE
)

Arguments

`dfm`	an object of class 'JD3_DfmModel'. Typically generated by the create_model() function.
`data`	an mts object.
`standardized`	Boolean. Indicate whether the input series were already standardized or not. Default is FALSE, meaning that a standardization of the series will be preliminary applied as part of the process.
`input_standardization`	Matrix. Mean and standard deviation of the variables to consider for the pre-processing step of standardization. Default is NULL, meaning that they will be re-calculated based on the data. Typically, it can be filled with the output of the function 'get_results()$preprocessing$sample_mean_stdev' applied on a previous estimate of the model. If provided manually, it must be a two columns matrix with the mean in the first column and the standard deviation in the second column. In the rows, the order of the variables should also be respected (similar to the data). Note that this argument must be filled if the re_estimate argument is set to FALSE. On the other hand, it is ignored if the standardized argument is set to TRUE.
`pca_init`	Boolean. Indicate whether a principal components analysis is performed beforehand and used as initial condition for the EM algorithm.
`max_iter`	Integer. Maximum number of iterations.
`eps`	Numeric. EM algorithm is run until the percentage likelihood does not increase by more than the eps value (1e-9 is the default) or until the maximum number of iterations is hit.
`re_estimate`	Boolean. Indicate whether the model will be re-estimated or not. Default is TRUE. Could be set to FALSE if, for some reasons during the production process, we wanted to freeze to model for some periods of time. It is not recommended to freeze the model for a long period.

Value

an object of class 'JD3_DfmEstimates'

Examples

set.seed(100)
data<-ts(matrix(rnorm(500), 100, 5), frequency = 12, start = c(2010,1))
data[100,1]<-data[99:100,2]<-data[(1:100)[-seq(3,100,3)],5]<-NA
dfm <- create_model(nfactors=2,
                    nlags=2,
                    factors_type = c("M", "M", "YoY", "M", "Q"),
                    factors_loading = matrix(data=TRUE, 5, 2),
                    var_init = "Unconditional")
est_em<-estimate_em(dfm, data)

#est_em<-estimate_em(dfm, data, re_estimate=FALSE) # model not re-estimated

set.seed(100)
data<-ts(matrix(rnorm(500), 100, 5), frequency = 12, start = c(2010,1))
data[100,1]<-data[99:100,2]<-data[(1:100)[-seq(3,100,3)],5]<-NA
dfm <- create_model(nfactors=2,
                    nlags=2,
                    factors_type = c("M", "M", "YoY", "M", "Q"),
                    factors_loading = matrix(data=TRUE, 5, 2),
                    var_init = "Unconditional")
est_em<-estimate_em(dfm, data)

#est_em<-estimate_em(dfm, data, re_estimate=FALSE) # model not re-estimated

Estimate DFM with Maximum Likelihood

Description

Estimate DFM with Maximum Likelihood

Usage

estimate_ml(
  dfm,
  data,
  standardized = FALSE,
  input_standardization = NULL,
  pca_init = TRUE,
  em_init = TRUE,
  em_max_iter = 100,
  em_eps = 1e-09,
  max_iter = 1000,
  max_block_iter = 5,
  simpl_model_iter = 15,
  independent_var_shocks = FALSE,
  mixedEstimation = TRUE,
  eps = 1e-09,
  re_estimate = TRUE
)
estimate_ml(
  dfm,
  data,
  standardized = FALSE,
  input_standardization = NULL,
  pca_init = TRUE,
  em_init = TRUE,
  em_max_iter = 100,
  em_eps = 1e-09,
  max_iter = 1000,
  max_block_iter = 5,
  simpl_model_iter = 15,
  independent_var_shocks = FALSE,
  mixedEstimation = TRUE,
  eps = 1e-09,
  re_estimate = TRUE
)

Arguments

`dfm`	an object of class 'JD3_DfmModel'. Typically generated by the create_model() function.
`data`	an mts object.
`standardized`	Boolean. Indicate whether the input series were already standardized or not. Default is FALSE, meaning that a standardization of the series will be preliminary applied as part of the process.
`input_standardization`	Matrix. Mean and standard deviation of the variables to consider for the pre-processing step of standardization. Default is NULL, meaning that they will be re-calculated based on the data. Typically, it can be filled with the output of the function 'get_results()$preprocessing$sample_mean_stdev' applied on a previous estimate of the model. If provided manually, it must be a two columns matrix with the mean in the first column and the standard deviation in the second column. In the rows, the order of the variables should also be respected (similar to the data). Note that this argument must be filled if the re_estimate argument is set to FALSE. On the other hand, it is ignored if the standardized argument is set to TRUE.
`pca_init`	Boolean. Indicate whether a principal components analysis is performed beforehand and used as initial condition for either the EM algorithm (if em_init=TRUE) or directly for the ML estimation.
`em_init`	Boolean. Indicate whether the EM algorithm is performed beforehand and used as initial condition for the ML estimation.
`em_max_iter`	Integer. Maximum number of iterations of the EM algorithm. Ignored if em_init = FALSE.
`em_eps`	Numeric. EM algorithm is run until the percentage likelihood does not increase by more than the eps value (1e-9 is the default) or until the maximum number of iterations is hit. Ignored if em_init = FALSE.
`max_iter`	Integer. Maximum number of iterations for the ML estimation.
`max_block_iter`	Integer. Maximum number of iterations in optimization by block. The model parameters are divided in two blocks: one related to the measurement equations and one to the VAR equations. While the EM algorithm requires one iteration per block, the numerical optimization allows us to set the number of iterations desired per block.
`simpl_model_iter`	Integer. Number of simplified model iterations allowed.
`independent_var_shocks`	Boolean. Whether we assume that shocks in the VAR block are independent.
`mixedEstimation`	Boolean. The mixed estimation option alternates between the iterations for the VAR block alone and simultaneous iterations for the two blocks.
`eps`	Numeric. ML estimation is run until the percentage likelihood does not increase by more than the eps value (1e-9 is the default) or until the maximum number of iterations is hit.
`re_estimate`	Boolean. Indicate whether the model will be re-estimated or not. Default is TRUE. Could be set to FALSE if, for some reasons during the production process, we wanted to freeze to model for some periods of time. It is not recommended to freeze the model for a long period.

Value

an object of class 'JD3_DfmEstimates'

Examples

set.seed(100)
data<-ts(matrix(rnorm(500), 100, 5), frequency = 12, start = c(2010,1))
data[100,1]<-data[99:100,2]<-data[(1:100)[-seq(3,100,3)],5]<-NA
dfm <- create_model(nfactors=2,
                    nlags=2,
                    factors_type = c("M", "M", "YoY", "M", "Q"),
                    factors_loading = matrix(data=TRUE, 5, 2),
                    var_init = "Unconditional")
est_ml<-estimate_ml(dfm, data)

#est_ml<-estimate_ml(dfm, data, re_estimate=FALSE) # model not re-estimated

set.seed(100)
data<-ts(matrix(rnorm(500), 100, 5), frequency = 12, start = c(2010,1))
data[100,1]<-data[99:100,2]<-data[(1:100)[-seq(3,100,3)],5]<-NA
dfm <- create_model(nfactors=2,
                    nlags=2,
                    factors_type = c("M", "M", "YoY", "M", "Q"),
                    factors_loading = matrix(data=TRUE, 5, 2),
                    var_init = "Unconditional")
est_ml<-estimate_ml(dfm, data)

#est_ml<-estimate_ml(dfm, data, re_estimate=FALSE) # model not re-estimated

Estimate DFM with Principal components Analysis

Description

Estimate DFM with Principal components Analysis

Usage

estimate_pca(
  dfm,
  data,
  standardized = FALSE,
  input_standardization = NULL,
  re_estimate = TRUE
)
estimate_pca(
  dfm,
  data,
  standardized = FALSE,
  input_standardization = NULL,
  re_estimate = TRUE
)

Arguments

`dfm`	an object of class 'JD3_DfmModel'. Typically generated by the create_model() function.
`data`	an mts object.
`standardized`	Boolean. Indicate whether the input series were already standardized or not. Default is FALSE, meaning that a standardization of the series will be preliminary applied as part of the process.
`input_standardization`	Matrix. Mean and standard deviation of the variables to consider for the pre-processing step of standardization. Default is NULL, meaning that they will be re-calculated based on the data. Typically, it can be filled with the output of the function 'get_results()$preprocessing$sample_mean_stdev' applied on a previous estimate of the model. If provided manually, it must be a two columns matrix with the mean in the first column and the standard deviation in the second column. In the rows, the order of the variables should also be respected (similar to the data). Note that this argument must be filled if the re_estimate argument is set to FALSE. On the other hand, it is ignored if the standardized argument is set to TRUE.
`re_estimate`	Boolean. Indicate whether the model will be re-estimated or not. Default is TRUE. Could be set to FALSE if, for some reasons during the production process, we wanted to freeze to model for some periods of time. It is not recommended to freeze the model for a long period.

Value

an object of class 'JD3_DfmEstimates'

Examples

set.seed(100)
data<-ts(matrix(rnorm(500), 100, 5), frequency = 12, start = c(2010,1))
data[100,1]<-data[99:100,2]<-data[(1:100)[-seq(3,100,3)],5]<-NA
dfm <- create_model(nfactors=2,
                    nlags=2,
                    factors_type = c("M", "M", "YoY", "M", "Q"),
                    factors_loading = matrix(data=TRUE, 5, 2),
                    var_init = "Unconditional")
est_pca<-estimate_pca(dfm, data)

#est_pca<-estimate_pca(dfm, data, re_estimate=FALSE) # model not re-estimated

set.seed(100)
data<-ts(matrix(rnorm(500), 100, 5), frequency = 12, start = c(2010,1))
data[100,1]<-data[99:100,2]<-data[(1:100)[-seq(3,100,3)],5]<-NA
dfm <- create_model(nfactors=2,
                    nlags=2,
                    factors_type = c("M", "M", "YoY", "M", "Q"),
                    factors_loading = matrix(data=TRUE, 5, 2),
                    var_init = "Unconditional")
est_pca<-estimate_pca(dfm, data)

#est_pca<-estimate_pca(dfm, data, re_estimate=FALSE) # model not re-estimated

Get DFM forecasts

Description

Get DFM forecasts

Usage

get_forecasts(dfm_estimates, n_fcst = 3)
get_forecasts(dfm_estimates, n_fcst = 3)

Arguments

`dfm_estimates`	an object of class 'JD3_DfmEstimates'
`n_fcst`	Integer. Number of forecast periods required.

Value

an object of class 'JD3_DfmForecasts'

Examples

set.seed(100)
data<-ts(matrix(rnorm(500), 100, 5), frequency = 12, start = c(2010,1))
data[100,1]<-data[99:100,2]<-data[(1:100)[-seq(3,100,3)],5]<-NA
dfm <- create_model(nfactors=2,
                    nlags=2,
                    factors_type = c("M", "M", "YoY", "M", "Q"),
                    factors_loading = matrix(data=TRUE, 5, 2),
                    var_init = "Unconditional")
est_em<-estimate_em(dfm, data)
fcst<-get_forecasts(est_em, n_fcst = 2)

set.seed(100)
data<-ts(matrix(rnorm(500), 100, 5), frequency = 12, start = c(2010,1))
data[100,1]<-data[99:100,2]<-data[(1:100)[-seq(3,100,3)],5]<-NA
dfm <- create_model(nfactors=2,
                    nlags=2,
                    factors_type = c("M", "M", "YoY", "M", "Q"),
                    factors_loading = matrix(data=TRUE, 5, 2),
                    var_init = "Unconditional")
est_em<-estimate_em(dfm, data)
fcst<-get_forecasts(est_em, n_fcst = 2)

DFM News analysis

Description

DFM News analysis

Usage

get_news(dfm_estimates, new_data, target_series = NULL, n_fcst = 3)
get_news(dfm_estimates, new_data, target_series = NULL, n_fcst = 3)

Arguments

`dfm_estimates`	an object of class 'JD3_DfmEstimates'. Typically generated by the functions estimate_pca(), estimate_em() or estimate_ml().
`new_data`	an mts object containing the updated dataset.
`target_series`	the name of the series of interest. By default, the first series is considered.
`n_fcst`	the number of forecasting periods to consider. Default is 3.

Value

An object of class 'JD3_DfmNews'

References

Banbura and Modugno (2010) - Maximum likelihood estimation of factor models on data sets with arbitrary pattern of missing data

Examples

set.seed(100)
data_t1<-ts(matrix(rnorm(500), 100, 5), frequency = 12, start = c(2010,1))
data_t1[100,1]<-data_t1[99:100,2]<-data_t1[(1:100)[-seq(3,100,3)],5]<-NA
data_t2<-ts(rbind(data_t1, rep(NA,5)), frequency = 12, start = c(2010,1))
data_t2[100,1]<-data_t2[99,2]<-data_t2[101,3]<-data_t2[101,4]<-1

dfm_model <- create_model(nfactors=2,
                          nlags=2,
                          factors_type = c("M", "M", "YoY", "M", "Q"),
                          factors_loading = matrix(TRUE, 5, 2),
                          var_init = "Unconditional")

est_em<-estimate_em(dfm_model, data_t1)
# or to use any previous frozen model:
# est_em_frozen<-estimate_em(dfm_model, data_t1, re_estimate = FALSE)

news<-get_news(est_em, data_t2, target_series = "Series 2", n_fcst = 2)

set.seed(100)
data_t1<-ts(matrix(rnorm(500), 100, 5), frequency = 12, start = c(2010,1))
data_t1[100,1]<-data_t1[99:100,2]<-data_t1[(1:100)[-seq(3,100,3)],5]<-NA
data_t2<-ts(rbind(data_t1, rep(NA,5)), frequency = 12, start = c(2010,1))
data_t2[100,1]<-data_t2[99,2]<-data_t2[101,3]<-data_t2[101,4]<-1

dfm_model <- create_model(nfactors=2,
                          nlags=2,
                          factors_type = c("M", "M", "YoY", "M", "Q"),
                          factors_loading = matrix(TRUE, 5, 2),
                          var_init = "Unconditional")

est_em<-estimate_em(dfm_model, data_t1)
# or to use any previous frozen model:
# est_em_frozen<-estimate_em(dfm_model, data_t1, re_estimate = FALSE)

news<-get_news(est_em, data_t2, target_series = "Series 2", n_fcst = 2)

Get DFM results

Description

Get DFM results

Usage

get_results(dfm_estimates)
get_results(dfm_estimates)

Arguments

dfm_estimates

an object of class 'JD3_DfmEstimates'

Value

an object of class 'JD3_DfmResults'

Examples

set.seed(100)
data<-ts(matrix(rnorm(500), 100, 5), frequency = 12, start = c(2010,1))
data[100,1]<-data[99:100,2]<-data[(1:100)[-seq(3,100,3)],5]<-NA
dfm <- create_model(nfactors=2,
                    nlags=2,
                    factors_type = c("M", "M", "YoY", "M", "Q"),
                    factors_loading = matrix(data=TRUE, 5, 2),
                    var_init = "Unconditional")
est_em<-estimate_em(dfm, data)
rslt_em<-get_results(est_em)

set.seed(100)
data<-ts(matrix(rnorm(500), 100, 5), frequency = 12, start = c(2010,1))
data[100,1]<-data[99:100,2]<-data[(1:100)[-seq(3,100,3)],5]<-NA
dfm <- create_model(nfactors=2,
                    nlags=2,
                    factors_type = c("M", "M", "YoY", "M", "Q"),
                    factors_loading = matrix(data=TRUE, 5, 2),
                    var_init = "Unconditional")
est_em<-estimate_em(dfm, data)
rslt_em<-get_results(est_em)

Plot function for objects of class 'JD3_DfmForecasts'

Description

Plot function for objects of class 'JD3_DfmForecasts'

Usage

## S3 method for class 'JD3_DfmForecasts'
plot(x, series_name = NULL, ...)
## S3 method for class 'JD3_DfmForecasts'
plot(x, series_name = NULL, ...)

Arguments

`x`	an object of class 'JD3_DfmForecasts'
`series_name`	Character. Name of the series to plot. By default, the first series will be plotted.
`...`	further arguments passed to ts.plot().

Plot function for objects of class 'JD3_DfmNews'

Description

Plot function for objects of class 'JD3_DfmNews'

Usage

## S3 method for class 'JD3_DfmNews'
plot(x, ...)
## S3 method for class 'JD3_DfmNews'
plot(x, ...)

Arguments

`x`	an object of class 'JD3_DfmNews'
`...`	further arguments passed to barplot().

Print function for objects of class 'JD3_DfmEstimates'

Description

Print function for objects of class 'JD3_DfmEstimates'

Usage

## S3 method for class 'JD3_DfmEstimates'
print(x, ...)
## S3 method for class 'JD3_DfmEstimates'
print(x, ...)

Arguments

`x`	an object of class 'JD3_DfmEstimates'
`...`	further arguments passed to the print() function.

Print function for objects of class 'JD3_DfmForecasts'

Description

Print function for objects of class 'JD3_DfmForecasts'

Usage

## S3 method for class 'JD3_DfmForecasts'
print(x, ...)
## S3 method for class 'JD3_DfmForecasts'
print(x, ...)

Arguments

`x`	an object of class 'JD3_DfmForecasts'
`...`	further arguments passed to the print() function.

Print function for objects of class 'JD3_DfmNews'

Description

Print function for objects of class 'JD3_DfmNews'

Usage

## S3 method for class 'JD3_DfmNews'
print(x, ...)
## S3 method for class 'JD3_DfmNews'
print(x, ...)

Arguments

`x`	an object of class 'JD3_DfmNews'
`...`	further arguments passed to the print() function.

Print function for objects of class 'JD3_DfmResults'

Description

Print function for objects of class 'JD3_DfmResults'

Usage

## S3 method for class 'JD3_DfmResults'
print(x, ...)
## S3 method for class 'JD3_DfmResults'
print(x, ...)

Arguments

`x`	an object of class 'JD3_DfmResults'
`...`	further arguments passed to the print() function.

Summary function for objects of class 'JD3_DfmNews'

Description

Summary function for objects of class 'JD3_DfmNews'

Usage

## S3 method for class 'JD3_DfmNews'
summary(object, ...)
## S3 method for class 'JD3_DfmNews'
summary(object, ...)

Arguments

`object`	an object of class 'JD3_DfmNews'
`...`	further arguments passed to the print() function.

Package 'rjd3nowcasting'

Help Index

Create Dynamic Factor Model

Description

Usage

Arguments

Value

Examples

Estimate DFM with Expectations-Maximization algorithm

Description

Usage

Arguments

Value

Examples

Estimate DFM with Maximum Likelihood

Description

Usage

Arguments

Value

Examples

Estimate DFM with Principal components Analysis

Description

Usage

Arguments

Value

Examples

Get DFM forecasts

Description

Usage

Arguments

Value

Examples

DFM News analysis

Description

Usage

Arguments

Value

References

Examples

Get DFM results

Description

Usage

Arguments

Value

Examples

Plot function for objects of class 'JD3_DfmForecasts'

Description

Usage

Arguments

Plot function for objects of class 'JD3_DfmNews'

Description

Usage

Arguments

Print function for objects of class 'JD3_DfmEstimates'

Description

Usage

Arguments

Print function for objects of class 'JD3_DfmForecasts'

Description

Usage

Arguments

Print function for objects of class 'JD3_DfmNews'

Description

Usage

Arguments

Print function for objects of class 'JD3_DfmResults'

Description

Usage

Arguments

Summary function for objects of class 'JD3_DfmNews'

Description

Usage

Arguments