Markov-Switching Regression for tidyfit — .model.mslm • tidyfit

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

Usage

# S3 method for mslm
.model(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       estim…¹  index std.e…² Regim…³ Regim…⁴ Regim…⁵ Regim…⁶
#>    <chr> <chr>        <dbl>  <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
#>  1 mslm  (Intercep…   2.99  201801    1.15 8.11e-1   0.189    3.96   -1.17
#>  2 mslm  (Intercep…   0.639 201802    1.59 3.52e-1   0.648    3.96   -1.17
#>  3 mslm  (Intercep…  -0.926 201803    1.82 4.70e-2   0.953    3.96   -1.17
#>  4 mslm  (Intercep…   1.39  201804    1.46 4.99e-1   0.501    3.96   -1.17
#>  5 mslm  (Intercep…   3.04  201805    1.14 8.20e-1   0.180    3.96   -1.17
#>  6 mslm  (Intercep…   0.188 201806    1.66 2.64e-1   0.736    3.96   -1.17
#>  7 mslm  (Intercep…   2.70  201807    1.21 7.53e-1   0.247    3.96   -1.17
#>  8 mslm  (Intercep…   3.21  201808    1.10 8.52e-1   0.148    3.96   -1.17
#>  9 mslm  (Intercep…   0.163 201809    1.66 2.59e-1   0.741    3.96   -1.17
#> 10 mslm  (Intercep…  -1.16  201810    1.86 8.79e-4   0.999    3.96   -1.17
#> # … with 98 more rows, and abbreviated variable names ¹estimate,
#> #   ²std.error, ³`Regime 1 Prob`, ⁴`Regime 2 Prob`, ⁵`Regime 1 Beta`,
#> #   ⁶`Regime 2 Beta`

Markov-Switching Regression for `tidyfit`