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

# S3 method for class '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.

See also

.fit.tvp and m methods

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        0.519 #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  (Intercept)    2.99  201801      1.09        0.811              0.189
#>  2 mslm  (Intercept)    0.640 201802      1.57        0.352              0.648
#>  3 mslm  (Intercept)   -0.926 201803      1.81        0.0472             0.953
#>  4 mslm  (Intercept)    1.39  201804      1.43        0.500              0.500
#>  5 mslm  (Intercept)    3.04  201805      1.08        0.820              0.180
#>  6 mslm  (Intercept)    0.189 201806      1.64        0.264              0.736
#>  7 mslm  (Intercept)    2.70  201807      1.16        0.754              0.246
#>  8 mslm  (Intercept)    3.21  201808      1.04        0.853              0.147
#>  9 mslm  (Intercept)    0.164 201809      1.65        0.260              0.740
#> 10 mslm  (Intercept)   -1.16  201810      1.85        0.000885           0.999
#> # ℹ 98 more rows
#> # ℹ 2 more variables: `Regime 1 Beta` <dbl>, `Regime 2 Beta` <dbl>