Markov-Switching Regression for tidyfit

Fits a Markov-Switching regression on a 'tidyFit' R6 class. The function can be used with regress.

Usage

# S3 method for mslm
.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)

k (the number of regimes)
sw (logical vector indicating which coefficients switch)
control (additional fitting parameters)

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

Implementation

Note that only the regression method with 'lm' is implemented at this stage.

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

If no sw argument is passed, all coefficients are permitted to switch between regimes.``

References

Sanchez-Espigares JA, Lopez-Moreno A (2021). MSwM: Fitting Markov Switching Models. R package version 1.5, https://CRAN.R-project.org/package=MSwM.

Author

Johann Pfitzinger

Examples

# Load data
data <- tidyfit::Factor_Industry_Returns
data <- dplyr::filter(data, Industry == "HiTec", Date >= 201801)
data <- dplyr::select(data, -Industry)

ctr <- list(maxiter = 100, parallelization = FALSE)

# Stand-alone function
fit <- m("mslm", Return ~ HML, data, index_col = "Date", k = 2, control = ctr)
fit
#> # A tibble: 1 × 5
#>   estimator_fct `size (MB)` grid_id  model_object settings        
#>   <chr>               <dbl> <chr>    <list>       <list>          
#> 1 MSwM::msmFit        0.102 #0010000 <tidyFit>    <tibble [1 × 3]>

# Within 'regress' function
fit <- regress(data, Return ~ HML,
               m("mslm", index_col = "Date", k = 2, control = ctr))
tidyr::unnest(coef(fit), model_info)
#> # A tibble: 108 × 9
#> # Groups:   model [1]
#>    model term    estimate  index std.error `Regime 1 Prob` `Regime 2 Prob`
#>    <chr> <chr>      <dbl>  <dbl>     <dbl>           <dbl>           <dbl>
#>  1 mslm  (Inter…    2.99  201801      1.19        0.811              0.189
#>  2 mslm  (Inter…    0.639 201802      1.62        0.352              0.648
#>  3 mslm  (Inter…   -0.926 201803      1.85        0.0470             0.953
#>  4 mslm  (Inter…    1.39  201804      1.49        0.499              0.501
#>  5 mslm  (Inter…    3.04  201805      1.18        0.820              0.180
#>  6 mslm  (Inter…    0.188 201806      1.69        0.264              0.736
#>  7 mslm  (Inter…    2.70  201807      1.25        0.753              0.247
#>  8 mslm  (Inter…    3.21  201808      1.14        0.852              0.148
#>  9 mslm  (Inter…    0.163 201809      1.69        0.259              0.741
#> 10 mslm  (Inter…   -1.16  201810      1.88        0.000879           0.999
#> # ℹ 98 more rows
#> # ℹ 2 more variables: `Regime 1 Beta` <dbl>, `Regime 2 Beta` <dbl>

Markov-Switching Regression for `tidyfit`