Rolling Window Model

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.

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()

• RollingWindowModel$fit()

• RollingWindowModel$abnormal_returns()

• RollingWindowModel$clone()

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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 observations for a valid window.

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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.

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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.

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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.