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)    0.318 0.924 -0.0986        0.380 196307
#>  2 tvp   (Intercept)    0.332 0.967 -0.131         0.391 196308
#>  3 tvp   (Intercept)    0.338 1.00  -0.247         0.419 196309
#>  4 tvp   (Intercept)    0.360 0.992 -0.256         0.408 196310
#>  5 tvp   (Intercept)    0.370 0.927 -0.295         0.397 196311
#>  6 tvp   (Intercept)    0.394 0.914 -0.309         0.401 196312
#>  7 tvp   (Intercept)    0.384 0.927 -0.237         0.365 196401
#>  8 tvp   (Intercept)    0.366 0.863 -0.116         0.353 196402
#>  9 tvp   (Intercept)    0.372 0.866 -0.156         0.369 196403
#> 10 tvp   (Intercept)    0.358 0.912 -0.0512        0.371 196404
#> # ℹ 4,946 more rows

Bayesian Time-Varying Regression for `tidyfit`