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)A fitted 'tidyFit' class model.
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.“
Sanchez-Espigares JA, Lopez-Moreno A (2021). MSwM: Fitting Markov Switching Models. R package version 1.5, https://CRAN.R-project.org/package=MSwM.
# 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>