Fits a Bayesian time-varying regression on a 'tidyFit' R6
class. The function can be used with regress
.
# S3 method for class 'tvp'
.fit(self, data = NULL)
A fitted 'tidyFit' class model.
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).
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
.
.fit.bayes
, .fit.mslm
and m
methods
# 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 = 10, 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) 0.678 1.29 0.00779 0.628 196307
#> 2 tvp (Intercept) 0.742 1.48 0.180 0.606 196308
#> 3 tvp (Intercept) 1.00 1.36 0.728 0.307 196309
#> 4 tvp (Intercept) 1.43 1.81 1.07 0.334 196310
#> 5 tvp (Intercept) 0.849 1.56 0.369 0.537 196311
#> 6 tvp (Intercept) 0.398 1.22 -0.308 0.662 196312
#> 7 tvp (Intercept) -0.0740 0.768 -0.653 0.637 196401
#> 8 tvp (Intercept) -0.0771 0.295 -0.484 0.336 196402
#> 9 tvp (Intercept) -0.267 0.254 -0.834 0.493 196403
#> 10 tvp (Intercept) -0.766 -0.0158 -1.51 0.702 196404
#> # ℹ 4,946 more rows