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Rolling Window Model

Source: R/models_time_varying.R
RollingWindowModel.Rd

Event study model with time-varying parameters estimated via a rolling OLS window over the estimation period. The last rolling window's parameters are used for event-window prediction. This captures parameter instability that is common in financial return data.

See also

Other eventstudy-models: BHARModel, Carhart4FactorModel, ComparisonPeriodMeanAdjustedModel, DCCGARCHModel, FamaFrench3FactorModel, FamaFrench5FactorModel, GARCHModel, LinearFactorModel, LogReturn, MarketAdjustedModel, MarketModel, ModelBase, ReturnCalculation, SimpleReturn, VolatilityModel, VolumeModel

Super class

ModelBase -> RollingWindowModel

Public fields

model_name

Name of the model.

window_size

Rolling window size. Default 60.

min_obs

Minimum observations required. Default 30.

Methods

Public methods

RollingWindowModel$new()

Create a new RollingWindowModel.

Usage

RollingWindowModel$new(window_size = 60L, min_obs = 30L)

Arguments

window_size

Size of the rolling window.

min_obs

Minimum valid (complete-pair) observations required inside each individual rolling sub-window for that sub-window to be fit. Distinct from validate_task()'s min_estimation_obs, which is a whole-estimation-window advisory threshold checked once per event, not per sub-window.

RollingWindowModel$fit()

Fit the rolling window model on the estimation window.

Usage

RollingWindowModel$fit(data_tbl)

Arguments

data_tbl

Data frame or tibble with firm_returns, index_returns, estimation_window, event_window columns.

RollingWindowModel$abnormal_returns()

Calculate abnormal returns using the last rolling window parameters.

Usage

RollingWindowModel$abnormal_returns(data_tbl)

Arguments

data_tbl

Data frame or tibble.

RollingWindowModel$clone()

The objects of this class are cloneable with this method.

Usage

RollingWindowModel$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.