Bayesian Time-Varying Regression for tidyfit

Fits a Bayesian time-varying regression on a 'tidyFit' R6 class. The function can be used with regress.

Usage

# S3 method for tvp
.fit(self, data = NULL)

Arguments

self: a 'tidyFit' R6 class.
data: a data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr).

Value

A fitted 'tidyFit' class model.

Details

Hyperparameters:

None. Cross validation not applicable.

Important method arguments (passed to m)

mod_type
niter (number of MCMC iterations)

The function provides a wrapper for shrinkTVP::shrinkTVP. See ?shrinkTVP for more details.

Implementation

An argument index_col can be passed, which allows a custom index to be added to coef(m("tvp")) (e.g. a date index, see Examples).

References

Peter Knaus, Angela Bitto-Nemling, Annalisa Cadonna and Sylvia Frühwirth-Schnatter (2021). Shrinkage in the Time-Varying Parameter Model Framework Using the R Package shrinkTVP. Journal of Statistical Software 100(13), 1--32. doi:10.18637/jss.v100.i13 .

Author

Johann Pfitzinger

Examples

# Load data
data <- tidyfit::Factor_Industry_Returns
data <- dplyr::filter(data, Industry == "HiTec")
data <- dplyr::select(data, -Industry)

# Within 'regress' function (using low niter for illustration)
fit <- regress(data, Return ~ ., m("tvp", niter = 50, index_col = "Date"))
tidyr::unnest(coef(fit), model_info)
#> # A tibble: 4,956 × 7
#> # Groups:   model [1]
#>    model term        estimate upper  lower posterior.sd  index
#>    <chr> <chr>          <dbl> <dbl>  <dbl>        <dbl>  <dbl>
#>  1 tvp   (Intercept)    0.436 0.852 0.104         0.264 196307
#>  2 tvp   (Intercept)    0.430 0.860 0.0939        0.269 196308
#>  3 tvp   (Intercept)    0.433 0.851 0.106         0.261 196309
#>  4 tvp   (Intercept)    0.442 0.844 0.195         0.254 196310
#>  5 tvp   (Intercept)    0.436 0.851 0.120         0.262 196311
#>  6 tvp   (Intercept)    0.434 0.836 0.0989        0.263 196312
#>  7 tvp   (Intercept)    0.437 0.817 0.0613        0.253 196401
#>  8 tvp   (Intercept)    0.439 0.814 0.200         0.242 196402
#>  9 tvp   (Intercept)    0.441 0.816 0.187         0.236 196403
#> 10 tvp   (Intercept)    0.440 0.829 0.223         0.236 196404
#> # ℹ 4,946 more rows

Bayesian Time-Varying Regression for `tidyfit`