Fits a Markov-Switching regression on a 'tidyFit' R6
class. The function can be used with regress
.
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).
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.
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… 3.65 201801 0.769 0.00000000338 1.00
#> 2 mslm (Inter… 3.56 201802 0.774 0.00796 0.992
#> 3 mslm (Inter… -0.439 201803 0.984 0.361 0.639
#> 4 mslm (Inter… 3.58 201804 0.773 0.00594 0.994
#> 5 mslm (Inter… 3.65 201805 0.769 0.0000000513 1.00
#> 6 mslm (Inter… 3.43 201806 0.782 0.0189 0.981
#> 7 mslm (Inter… 3.65 201807 0.769 0.000214 1.00
#> 8 mslm (Inter… 3.65 201808 0.769 0.0000000190 1.00
#> 9 mslm (Inter… 3.56 201809 0.774 0.00740 0.993
#> 10 mslm (Inter… -7.04 201810 1.26 0.945 0.0549
#> # ℹ 98 more rows
#> # ℹ 2 more variables: `Regime 1 Beta` <dbl>, `Regime 2 Beta` <dbl>