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

.model.tvp(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)

# Stand-alone function (using low niter for illustration)
fit <- m("tvp", Return ~ ., data, index_col = "Date", niter = 50)
fit
#> # A tibble: 1 × 5
#>   estimator_fct        `size (MB)` grid_id  model_object settings        
#>   <chr>                      <dbl> <chr>    <list>       <list>          
#> 1 shrinkTVP::shrinkTVP        1.59 #0010000 <tidyFit>    <tibble [1 × 3]>

# 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)   0.0402 0.179 -0.0151        0.0779 196307
#>  2 tvp   (Intercept)   0.0612 0.298 -0.0398        0.108  196308
#>  3 tvp   (Intercept)   0.0624 0.254 -0.0582        0.107  196309
#>  4 tvp   (Intercept)   0.0835 0.270 -0.0260        0.112  196310
#>  5 tvp   (Intercept)   0.0903 0.387 -0.0442        0.132  196311
#>  6 tvp   (Intercept)   0.0843 0.256 -0.0213        0.115  196312
#>  7 tvp   (Intercept)   0.116  0.311 -0.0165        0.123  196401
#>  8 tvp   (Intercept)   0.126  0.420 -0.0291        0.147  196402
#>  9 tvp   (Intercept)   0.151  0.396 -0.00356       0.127  196403
#> 10 tvp   (Intercept)   0.170  0.460 -0.00275       0.155  196404
#> # ℹ 4,946 more rows

Bayesian Time-Varying Regression for `tidyfit`