Package {bridgr}

Type: Package
Title: Bridging Data Frequencies for Timely Economic Forecasts
Version: 1.0.0
Maintainer: Marc Burri <marc.burri91@gmail.com>
Description: Implements bridge and MIDAS-style mixed-frequency models for nowcasting and forecasting macroeconomic variables by linking higher-frequency indicator variables to a lower-frequency target series. The package standardizes input data, infers regular frequencies, forecasts missing indicator observations, and aggregates indicators to the target frequency before fitting a regression with autoregressive target dynamics. Frequency alignment can be customized through user-supplied conversion rules. For more on bridge and MIDAS models, see Baffigi, A., Golinelli, R., & Parigi, G. (2004) <doi:10.1016/S0169-2070(03)00067-0>, Ghysels, Sinko, & Valkanov (2007) <doi:10.1080/07474930600972467>, Andreou, Ghysels, & Kourtellos (2010) <doi:10.1016/j.jeconom.2010.01.004>, Schumacher (2016) <doi:10.1016/j.ijforecast.2015.07.004>, and Burri (2026) <doi:10.1111/obes.70073>.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.3
LazyData: true
Imports: dplyr, forecast, ggplot2, lifecycle, lubridate, rlang, scales, tsbox, withr
Suggests: knitr, rmarkdown, srr, testthat (≥ 3.0.0)
Config/testthat/edition: 3
Depends: R (≥ 4.1.0)
URL: https://github.com/marcburri/bridgr, https://marcburri.github.io/bridgr/
BugReports: https://github.com/marcburri/bridgr/issues
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-08-21 10:20:24 UTC; marcburri
Author: Marc Burri ORCID iD [aut, cre, cph]
Repository: CRAN
Date/Publication: 2026-08-21 13:10:24 UTC

bridgr: Bridging Data Frequencies for Timely Economic Forecasts

Description

bridgr provides a unified workflow for bridging high-frequency indicators to lower-frequency target series, the practical job at the heart of nowcasting and forecasting macroeconomic variables. It covers classical bridge equations and MIDAS-style mixed-frequency regressions (expalmon, beta, unrestricted / U-MIDAS) under one interface, with automatic frequency alignment, indicator forecasting, and aggregation.

To learn more about bridgr, start with the vignettes: browseVignettes(package = "bridgr")

Details

bridgr is under active maintenance. The public interface centered on mf_model(), forecast(), summary(), and plot() is considered stable for mixed-frequency nowcasting workflows, while future development is expected to expand diagnostics and mixed-frequency model options without changing the core target-and-indicator workflow.

Author(s)

Maintainer: Marc Burri marc.burri91@gmail.com (ORCID) [copyright holder]

See Also

Useful links:

Estimated Parametric Aggregation Parameters

Description

Extracts the estimated parameters of parametric aggregation schemes ("expalmon", "beta") from a fitted mf_model() object. These are the parameters whose implied weights are returned by weights.mf_model().

Usage

aggregation_parameters(object, indicator = NULL, ...)

## S3 method for class 'mf_model'
aggregation_parameters(object, indicator = NULL, ...)

Arguments

object

A fitted "mf_model" object returned by mf_model().

indicator

Optional indicator selector, either an indicator name or a position. When supplied, the parameters for that single indicator are returned directly; when NULL (default), a named list is returned.

...

Unused.

Value

When indicator is NULL, a named list with one element per indicator that used a parametric aggregator, each a numeric parameter vector. When indicator is supplied, that single element, or NULL when the indicator did not use a parametric aggregator.

See Also

weights.mf_model() for the implied aggregation weights.

Examples

gdp_growth <- tsbox::ts_pc(gdp)
gdp_growth <- tsbox::ts_na_omit(gdp_growth)
model <- mf_model(
  target = gdp_growth,
  indic = baro,
  indic_aggregators = "expalmon",
  h = 1
)

aggregation_parameters(model)

Coerce a Mixed-Frequency Forecast to a forecast Object

Description

Converts a "mf_model_forecast" object into a genuine "forecast" object from the forecast package, so that functions such as forecast::accuracy() can be used on bridgr output.

Usage

as.forecast(object, ...)

## S3 method for class 'mf_model_forecast'
as.forecast(object, ...)

Arguments

object

A "mf_model_forecast" object returned by forecast.mf_model().

...

Unused.

Details

Conversion requires a target frequency that stats::ts() can represent exactly, that is, a whole number of target periods per calendar year. Annual, semi-annual, quarterly, bi-monthly and monthly targets qualify; daily, weekly and sub-daily targets do not, and are rejected with an informative error rather than silently approximated.

Value

An object of class "forecast", with mean, lower, upper, x, fitted and residuals stored as stats::ts() objects.

See Also

forecast.mf_model() for the native forecast object, which supports every target frequency.

Examples

gdp_growth <- tsbox::ts_pc(gdp)
gdp_growth <- tsbox::ts_na_omit(gdp_growth)
model <- mf_model(
  target = gdp_growth,
  indic = baro,
  indic_predict = "auto.arima",
  indic_aggregators = "mean",
  h = 1
)

converted <- as.forecast(forecast(model))
class(converted)

Forecast a Mixed-Frequency Model

Description

Forecast the target variable from a fitted mf_model() object.

Usage

## S3 method for class 'mf_model'
forecast(object, xreg = NULL, level = c(80, 95), ...)

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

Arguments

object

A "mf_model" object returned by mf_model().

xreg

Optional future regressors in a tsbox::ts_boxable() format. When omitted, the forecast regressor set stored inside object is used. When supplied, xreg must contain the same non-target regressors used when fitting the bridge equation.

level

Prediction interval levels used when the model was estimated with se = TRUE. When uncertainty is unavailable, forecast() still returns the se, lower, and upper components, filled with NA.

...

Unused.

x

A "mf_model_forecast" object returned by forecast.mf_model().

Details

In recursive bridge forecasts, uncertainty typically increases with horizon because later forecast steps depend on forecasted rather than observed target lags and, when needed, completed indicator paths. Under the package's residual-resampling and full-system bootstrap workflows, those simulated disturbances accumulate across steps, so standard errors and interval widths can widen as the forecast horizon extends. The uncertainty-and-scenarios vignette includes one worked example that trims forecast rows by an acceptable prediction-interval width.

Value

An object of class "mf_model_forecast" containing point forecasts, predictive uncertainty summaries, the observed target history, the target-period regressors used for forecasting, and optional full-system bootstrap metadata.

x, invisibly.

Interoperability with the forecast package

"mf_model_forecast" deliberately does not inherit from the forecast package's "forecast" class. Target frequencies supported by mf_model() include daily, weekly and sub-daily series, which stats::ts() cannot represent without silently approximating the calendar, so an object that claimed "forecast" inheritance could not honour it for every model this package fits. Instead, plot() and ggplot2::autoplot() methods are provided directly for "mf_model_forecast", and as.forecast() converts to a genuine "forecast" object whenever the target frequency is regular enough to allow it, for use with functions such as forecast::accuracy().

Examples

gdp_growth <- tsbox::ts_pc(gdp)
gdp_growth <- tsbox::ts_na_omit(gdp_growth)
model <- mf_model(
  target = gdp_growth,
  indic = baro,
  indic_predict = "auto.arima",
  indic_aggregators = "mean",
  h = 1
)

forecast(model)

Swiss Economic Indicators

Description

A collection of datasets containing economic indicators for Switzerland.

Usage

gdp

baro

wea

fcurve

Details

Datasets

Examples

# Load and plot `baro`
data(baro)
library(tsbox)
suppressMessages(ts_plot(baro))

Indicator Names of a Mixed-Frequency Model

Description

Returns the identifiers of the high-frequency indicators used by a fitted mf_model() object, in the order they enter the bridge equation.

Usage

indicators(object, ...)

## S3 method for class 'mf_model'
indicators(object, ...)

Arguments

object

A fitted "mf_model" object returned by mf_model().

...

Unused.

Value

A character vector of indicator identifiers.

Examples

gdp_growth <- tsbox::ts_pc(gdp)
gdp_growth <- tsbox::ts_na_omit(gdp_growth)
model <- mf_model(
  target = gdp_growth,
  indic = baro,
  indic_predict = "auto.arima",
  indic_aggregators = "mean",
  h = 1
)

indicators(model)

Estimate a Mixed-Frequency Model

Description

Estimate a bridge model that links one lower-frequency target series to one or more higher-frequency indicator series. Indicators are aligned to the target frequency by forecasting any missing higher-frequency observations and aggregating them within each target period.

Usage

mf_model(
  target,
  indic,
  missing = "error",
  indic_predict = NULL,
  indic_aggregators = NULL,
  indic_lags = 0,
  target_lags = 0,
  h = 1,
  frequency_conversions = NULL,
  se = FALSE,
  bootstrap = NULL,
  full_system_bootstrap = FALSE,
  stationarity = "none",
  solver_options = NULL
)

bridge(...)

Arguments

target

A single target series in a tsbox::ts_boxable() format, such as a data frame or tibble with time and value/values columns, or a regular time-series object supported by tsbox.

indic

One or more indicator series in a tsbox::ts_boxable() format, such as data frames / tibbles with time and value/values columns, or regular time-series objects supported by tsbox.

missing

Character string controlling how explicit missing values in submitted target or indic series are handled. missing = "error" keeps the strict default and aborts on explicit missing values. missing = "drop" removes explicit missing values with a warning before downstream analysis, which is most useful when they correspond to supported ragged-edge gaps at the ends of series. missing = "impute" replaces explicit missing values by series-wise linear interpolation with endpoint carry before model fitting. Missing timestamps are always an error.

indic_predict

A character vector of indicator forecasting methods. Length must be 1 or equal to the number of indicator series. Setting indic_predict = "direct" switches to direct MIDAS-style alignment: the indicators are not forecasted. Each target period that overlaps the observed sample receives the most recent complete high-frequency block at the same period-relative position that the newest observation occupies within its own period – a MIDAS-with-leads alignment that stays calendar-consistent when target periods hold varying numbers of high-frequency observations (e.g. 13-Saturday quarters on a 12-slot weekly ladder). Forecast periods beyond the newest observation receive complete blocks assigned backward from the end of the sample. Direct alignment must be used for all indicators at once. For indic_predict = "mean", missing high-frequency observations are filled with the mean of the latest available obs_per_target high-frequency observations, and that same mean is extended across the forecast horizon.

indic_aggregators

A character vector of aggregation methods or a list of numeric weights. Length must be 1 or equal to the number of indicator series. Numeric weights must sum to one and have the appropriate length for the inferred target-period block size. "unrestricted" keeps one separate coefficient per high-frequency observation within the target period. The parametric aggregators use two coefficients each: "expalmon" uses ⁠(linear, quadratic)⁠, and "beta" uses ⁠(left_shape, right_shape)⁠ as the normalized beta shape parameters. When indic_predict = "direct", the supplied aggregation is applied directly to the aligned complete high-frequency blocks within each target period.

indic_lags

A non-negative integer giving the number of target-period lags to add for each aggregated indicator.

target_lags

A non-negative integer giving the autoregressive order in the target equation.

h

A positive integer forecast horizon measured in target periods.

frequency_conversions

A named numeric vector used to customize the regular frequency ladder. Supported names are spm, mph, hpd, dpw, wpm, mpq, and qpy.

se

Logical flag indicating whether coefficient standard errors and prediction intervals should be computed. When TRUE, mf_model() reports HAC standard errors for the linear target equation, or Delta-HAC standard errors when parametric aggregation weights are estimated jointly.

bootstrap

A list of uncertainty controls. Currently only list(N = 100, block_length = NULL) is supported. N is the number of predictive simulation paths used when se = TRUE. If full_system_bootstrap = TRUE, the same N controls the number of full-system target-period block-bootstrap replications used for prediction intervals. block_length is only used by the full-system bootstrap. When block_length is NULL, mf_model() uses ceiling(n^(1/3)) based on the final target-period sample size.

full_system_bootstrap

Logical flag indicating whether prediction intervals and coefficient standard errors should be based on a full-system target-period block bootstrap instead of residual resampling and HAC / Delta-HAC uncertainty from the fitted target equation. This option is only used when se = TRUE. Because it refits the full bridge workflow on every draw, full_system_bootstrap = TRUE can be substantially slower than the default residual-resampling intervals.

stationarity

Character string controlling optional heuristic lower-order stationarity diagnostics. stationarity = "none" skips diagnostics. stationarity = "warn" checks submitted target and indicator series for a KPSS-style differencing signal and large variance shifts, and warns when those heuristics suggest that upstream transformations such as differences, growth rates, log changes, or demeaning may be appropriate.

solver_options

A list of optional controls for joint parametric-weight optimization. Supported entries are: method for the optimizer ("L-BFGS-B", "BFGS", "Nelder-Mead", or "nlminb"), maxiter for the iteration budget per optimization run, n_starts for the number of multi-start attempts, seed for reproducible random restarts, trace for optimizer verbosity, warn to control whether non-convergence warnings are emitted, reltol for the relative convergence tolerance passed through to the selected optimizer backend, and start_values for user-supplied initial parameter values. Documented defaults are method = "L-BFGS-B", maxiter = 1000, n_starts = 5, trace = 0, warn = TRUE, and reltol = 1e-8. start_values can be either a numeric vector or a named list. For a numeric vector, values are concatenated in indicator order across the parametric aggregators. Within each indicator, the parameter order is ⁠(linear, quadratic)⁠ for "expalmon" and ⁠(left_shape, right_shape)⁠ for "beta". Named-list start_values must provide exactly the required number of values for each parametric indicator. Users can override reltol or suppress convergence warnings through solver_options = list(reltol = ..., warn = FALSE). These controls are ignored unless at least one indicator uses a parametric aggregator.

...

Arguments forwarded to mf_model().

Details

Supported indicator forecasting methods are "mean", "last", "auto.arima", "ets", and "direct". Supported aggregation methods are "mean", "last", "sum", "unrestricted", "expalmon", "beta", or a numeric weight vector supplied inside a list(). "unrestricted" expands each high-frequency observation within a target period into its own bridge regressor, which corresponds to a U-MIDAS style specification when the frequency gap is small. When one or more indicators use a parametric aggregator, the corresponding aggregation weights are estimated jointly against the final bridge-model objective rather than one indicator at a time.

Unrestricted mixed-frequency regressions can become parameter-heavy quickly. When indic_aggregators = "unrestricted", mf_model() warns if the final estimation sample contains fewer than 10 observations per predictor in the bridge regression.

The package assumes a regular frequency ladder second -> minute -> hour -> day -> week -> month -> quarter -> year. The default number of lower-level observations per higher-level unit is:

Users can override any subset of these values with frequency_conversions. If a target period contains more high-frequency observations than implied by the current mapping, mf_model() keeps the most recent observations and emits a summarized warning. If a target period contains fewer observations than required, the call fails. Month-, quarter-, and year-based input dates are standardized to period starts when needed for frequency recognition.

Value

An object of class "mf_model" containing the standardized input series, inferred frequencies, aligned estimation and forecast datasets, the fitted target model, fitted indicator models, and metadata required by forecast.mf_model() and summary.mf_model().

Model specification

bridgr does not use a formula interface. Mixed-frequency bridge models are specified through separate target and indic series plus the forecasting, aggregation, and lag controls because the package must standardize, align, extend, and aggregate the time-series inputs before the final target regression formula can be assembled.

Terminology

Throughout bridgr, a target is the lower-frequency response series to be forecasted. An indicator is any higher-frequency predictor series aligned to the target frequency before the final regression is fit. Indicator forecasting refers to how end-of-sample indicator values are completed when the target horizon extends beyond the latest observed indicator block. Aggregation refers to how high-frequency indicator values within a target period are combined into bridge regressors. Direct prediction skips indicator forecasting and aligns the latest complete high-frequency blocks backward from the forecast horizon, while unrestricted aggregation keeps one separate coefficient per within-period high-frequency observation.

Input standardization

Submitted series are converted to a common internal table with id, time, and numeric values columns before model fitting. This means that additional input attributes beyond the series identifier, timestamps, and numeric values are not preserved in the fitted object unless they are re-encoded in those standardized columns.

Input assumptions

bridgr assumes that submitted target and indicator series are ordered by time within each series, free of duplicate timestamps and explicit missing values, and regular enough for the package to infer a supported target and indicator frequency. It also assumes that indicator series are at least as high-frequency as the target. Violations of these assumptions are rejected during preprocessing and validation rather than being silently repaired.

Stationarity

bridgr assumes that users provide target and indicator series on a scale that is appropriate for bridge-style forecasting. In practice this often means working with growth rates, differences, or other transformed series prepared upstream. This expectation primarily concerns the lower-order moments that matter most for bridge-style forecasting, typically the mean and variance of the submitted series. By default the package does not automatically enforce stationarity, but stationarity = "warn" enables heuristic pre-fit diagnostics for strong linear trends and variance shifts and points users toward differences, growth rates, log changes, demeaning, or other variance-stabilizing transformations when those heuristics are triggered.

Deprecated bridge() wrapper

bridge() is retained for compatibility and forwards to mf_model() with a deprecation warning.

References

Baffigi, A., Golinelli, R., & Parigi, G. (2004). Bridge models to forecast the euro area GDP. International Journal of Forecasting, 20(3), 447-460. doi:10.1016/S0169-2070(03)00067-0

Ghysels, E., Sinko, A., & Valkanov, R. (2007). MIDAS regressions: Further results and new directions. Econometric Reviews, 26(1), 53-90. doi:10.1080/07474930600972467

Andreou, E., Ghysels, E., & Kourtellos, A. (2010). Regression models with mixed sampling frequencies. Journal of Econometrics, 158(2), 246-261. doi:10.1016/j.jeconom.2010.01.004

Schumacher, C. (2016). A comparison of MIDAS and bridge equations. International Journal of Forecasting, 32(2), 257-270. doi:10.1016/j.ijforecast.2015.07.004

Burri, M. (2026). Nowcasting Swiss GDP Growth From Public Lead Texts: Simple Methods Are Sufficient. Oxford Bulletin of Economics and Statistics, 1-25. doi:10.1111/obes.70073

Examples

gdp_growth <- tsbox::ts_pc(gdp)
gdp_growth <- tsbox::ts_na_omit(gdp_growth)
gdp_growth <- dplyr::slice_tail(gdp_growth, n = 12)
baro_small <- dplyr::slice_tail(baro, n = 36)

mf_model(
  target = gdp_growth,
  indic = baro_small,
  indic_predict = "auto.arima",
  indic_aggregators = "mean",
  indic_lags = 1,
  target_lags = 1,
  h = 1,
  frequency_conversions = c(mpq = 3),
  se = TRUE,
  bootstrap = list(N = 2),
  solver_options = list(seed = 123, n_starts = 1)
)

Accessor Methods for Mixed-Frequency Models

Description

Access standard model summaries from a fitted mf_model() object.

Usage

## S3 method for class 'mf_model'
coef(object, ...)

## S3 method for class 'mf_model'
confint(object, parm = NULL, level = 0.95, ...)

## S3 method for class 'mf_model'
formula(x, ...)

## S3 method for class 'mf_model'
nobs(object, ...)

## S3 method for class 'mf_model'
vcov(object, ...)

## S3 method for class 'mf_model'
fitted(object, ...)

## S3 method for class 'mf_model'
residuals(object, ...)

## S3 method for class 'mf_model'
model.frame(formula, which = c("estimation", "forecast"), ...)

## S3 method for class 'mf_model'
variable.names(object, which = c("all", "xreg", "target_lags"), ...)

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

Arguments

object, x, formula

A fitted "mf_model" object returned by mf_model(). The formula spelling is required by the stats::model.frame() generic and carries the same meaning.

...

Unused.

parm, level

Passed to confint(). Confidence intervals are computed from the coefficient covariance matrix returned by stats::vcov(), which may be the HAC or Delta-HAC covariance when se = TRUE. Critical values use a t-distribution with residual degrees of freedom from the fitted target equation; this is conservative relative to asymptotic normal critical values but is common practice in applied econometrics.

which

For model.frame(), which modelling frame to return, "estimation" (default) or "forecast". For variable.names(), which group of regressor names to return, "all" (default), "xreg" or "target_lags".

Details

residuals.mf_model() returns target-equation residuals on the same standardized scale as the fitted target series, so they can be passed directly to downstream residual diagnostics.

model.frame.mf_model() returns the aligned modelling data. Use which = "estimation" for the in-sample bridge-equation data, and which = "forecast" for the future target-period regressor path the forecast is produced from. The latter is the frame to modify and pass back as xreg when constructing scenarios.

variable.names.mf_model() returns the names of the bridge-equation regressors. which = "xreg" returns the non-target-lag regressors, which are exactly the series a custom xreg must supply when forecasting a scenario, and pairs with model.frame(object, which = "forecast").

Value

The requested model summary, usually delegated from the stored target regression fit.

x, invisibly.

Examples

gdp_growth <- tsbox::ts_pc(gdp)
gdp_growth <- tsbox::ts_na_omit(gdp_growth)
model <- mf_model(
  target = gdp_growth,
  indic = baro,
  indic_predict = "auto.arima",
  indic_aggregators = "mean",
  h = 1
)

coef(model)

Plot a Mixed-Frequency Model

Description

Visualize a fitted mf_model() object either as an in-sample fit or as a forecast with prediction intervals.

Usage

## S3 method for class 'mf_model'
plot(
  x,
  type = c("fit", "forecast"),
  level = 95,
  history_n = 50,
  xlab = NULL,
  ylab = NULL,
  main = NULL,
  ...
)

Arguments

x

A "mf_model" object returned by mf_model().

type

Plot type. Use "forecast" to plot the observed target history together with the bridge forecast, or "fit" to compare the observed target to the in-sample fitted values.

level

Forecast interval level passed to forecast.mf_model() when type = "forecast".

history_n

Number of historical target observations to display. Defaults to the most recent 50. Set to NULL to show the full history.

xlab, ylab, main

Optional axis and title labels. When omitted, sensible defaults are chosen from type.

...

Additional arguments passed to theme_bridgr().

Value

A ggplot2 object.

Examples

gdp_growth <- tsbox::ts_pc(gdp)
gdp_growth <- tsbox::ts_na_omit(gdp_growth)
model <- mf_model(
  target = gdp_growth,
  indic = baro,
  indic_predict = "auto.arima",
  indic_aggregators = "mean",
  h = 1
)

plot(model)

Plot a Mixed-Frequency Forecast

Description

Visualize a "mf_model_forecast" object returned by forecast.mf_model(), showing the observed target history together with the bridge forecast and, when available, a prediction interval.

Usage

## S3 method for class 'mf_model_forecast'
plot(
  x,
  level = NULL,
  history_n = 50,
  xlab = NULL,
  ylab = NULL,
  main = NULL,
  ...
)

## S3 method for class 'mf_model_forecast'
autoplot(object, ...)

Arguments

x

A "mf_model_forecast" object returned by forecast.mf_model().

level

Prediction interval level to display. Must be one of the levels the forecast was computed with. Defaults to the first available level.

history_n

Number of historical target observations to display. Defaults to the most recent 50. Set to NULL to show the full history.

xlab, ylab, main

Optional axis and title labels.

...

Additional arguments passed to theme_bridgr().

object

A "mf_model_forecast" object, for the ggplot2::autoplot() method.

Details

These methods are provided directly for "mf_model_forecast" rather than inherited from the forecast package, so that plotting works for every target frequency mf_model() supports, including daily and weekly targets that stats::ts() cannot represent. See as.forecast() to convert to a "forecast" object where the frequency allows it.

Value

A ggplot2 object.

Examples

gdp_growth <- tsbox::ts_pc(gdp)
gdp_growth <- tsbox::ts_na_omit(gdp_growth)
model <- mf_model(
  target = gdp_growth,
  indic = baro,
  indic_predict = "auto.arima",
  indic_aggregators = "mean",
  h = 1
)

plot(forecast(model))

Print a Mixed-Frequency Model Summary

Description

Print a Mixed-Frequency Model Summary

Usage

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

Arguments

x

A "summary.mf_model" object returned by summary.mf_model().

...

Unused.

Value

x, invisibly.

Examples

gdp_growth <- tsbox::ts_pc(gdp)
gdp_growth <- tsbox::ts_na_omit(gdp_growth)
model <- mf_model(
  target = gdp_growth,
  indic = baro,
  indic_predict = "auto.arima",
  indic_aggregators = "mean",
  h = 1
)

print(summary(model))

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

forecast

forecast

ggplot2

autoplot

Summarize a Mixed-Frequency Model

Description

Computes the summary quantities for a fitted mf_model() object and returns them as a "summary.mf_model" object. Following the convention of summary.lm(), the summary is a data object in its own right: the report is rendered by print.summary.mf_model() rather than by summary() itself, so the individual quantities can be extracted programmatically.

Usage

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

Arguments

object

A "mf_model" object returned by mf_model().

...

Unused.

Value

An object of class "summary.mf_model", a list with components:

target_name, target_frequency, h, nobs

Target series name, inferred target frequency unit, forecast horizon, and number of estimation rows.

regressor_names

Character vector of bridge-equation regressors.

coefficients

Numeric matrix with columns "Estimate", "Std. Error", "t value" and "Pr(>|t|)". Standard errors come from vcov.mf_model(), so they are the HAC, Delta-HAC or bootstrap standard errors when the model was fitted with se = TRUE.

coefficient_method

Method used for the coefficient standard errors, or NULL when the model was fitted without uncertainty.

r.squared, adj.r.squared, sigma, df.residual

Fit measures for the target equation.

indicators

Data frame of per-indicator frequency, completion method and aggregation scheme, one row per indicator.

custom_weights

Named list of user-supplied numeric aggregation weights, empty when none were used.

parametric_weights, parametric_parameters

Named lists of estimated parametric aggregation weights and their underlying parameters, empty when no parametric aggregator was used.

uncertainty, bootstrap, optimization

Uncertainty settings, bootstrap diagnostics, and joint parametric-optimization diagnostics.

See Also

coef.mf_model(), confint.mf_model() and vcov.mf_model() for extracting individual quantities directly from the fitted model.

Examples

gdp_growth <- tsbox::ts_pc(gdp)
gdp_growth <- tsbox::ts_na_omit(gdp_growth)
model <- mf_model(
  target = gdp_growth,
  indic = baro,
  indic_predict = "auto.arima",
  indic_aggregators = "mean",
  h = 1
)

model_summary <- summary(model)
model_summary

# The coefficient matrix is available programmatically, as for `lm()`.
coef(model_summary)

bridgr Plot Theme and Color Scales

Description

Plot styling helpers for bridgr graphics, based on the visual defaults used in the reviser package.

Usage

theme_bridgr(
  base_size = 12,
  legend_position = "bottom",
  legend_direction = "horizontal",
  ...
)

colors_bridgr()

scale_color_bridgr(...)

scale_fill_bridgr(...)

Arguments

base_size

Base text size for the plot theme.

legend_position

Legend position passed to ggplot2::theme().

legend_direction

Legend direction passed to ggplot2::theme().

...

Additional arguments. In theme_bridgr(), legend.position and legend.direction are accepted for backward compatibility. In the scale helpers, ... is forwarded to the ggplot2 scale constructors.

Value

A ggplot2 theme, color palette, or scale.

Examples

ggplot2::ggplot(mtcars, ggplot2::aes(wt, mpg, color = factor(cyl))) +
  ggplot2::geom_point() +
  theme_bridgr()

colors_bridgr()[1:3]

Aggregation Weights of a Mixed-Frequency Model

Description

Extracts the within-period aggregation weights applied to each indicator. For parametric aggregators ("expalmon", "beta") these are the weights implied by the estimated parameters; for user-supplied numeric aggregators they are the supplied weights.

Usage

## S3 method for class 'mf_model'
weights(object, indicator = NULL, ...)

Arguments

object

A fitted "mf_model" object returned by mf_model().

indicator

Optional indicator selector, either an indicator name or a position. When supplied, the weights for that single indicator are returned directly; when NULL (default), a named list covering every indicator is returned.

...

Unused.

Details

The deterministic rules imply the fixed weight vectors of \tilde x_t = \sum_m w_m x_{t,m}: "mean" gives w_m = 1/M, "last" gives w_M = 1 and zero elsewhere, and "sum" gives w_m = 1. These are returned alongside the estimated and user-supplied weights, so the accessor reports the weights actually applied whichever aggregator was chosen. "unrestricted" has no single weight vector, because it estimates one coefficient per within-period observation instead of aggregating; it returns NULL, as does an indicator aligned with indic_predict = "direct".

Value

When indicator is NULL, a named list with one element per indicator, each either a numeric weight vector or NULL for indicators whose aggregator does not imply a fixed weight vector ("unrestricted"). When indicator is supplied, that single element.

See Also

aggregation_parameters() for the underlying parametric parameters, and indicators() for the available indicator names.

Examples

gdp_growth <- tsbox::ts_pc(gdp)
gdp_growth <- tsbox::ts_na_omit(gdp_growth)
model <- mf_model(
  target = gdp_growth,
  indic = baro,
  indic_aggregators = "expalmon",
  h = 1
)

weights(model)
weights(model, indicator = 1)