Fits a Bayesian time-varying regression on a 'tidyFit' R6
class. The function can be used with regress
.
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).
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
.
See also
.fit.bayes
, .fit.mslm
and m
methods
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