## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----eval=FALSE---------------------------------------------------------------
# library(EventStudy)
# 
# IndustryAdjustedModel <- R6::R6Class("IndustryAdjustedModel",
#   inherit = ModelBase,
#   public = list(
#     model_name = "IndustryAdjustedModel",
# 
#     fit = function(data_tbl) {
#       # No estimation needed -- just compute statistics
#       private$.is_fitted <- TRUE
#       private$calculate_statistics(data_tbl)
#     },
# 
#     abnormal_returns = function(data_tbl) {
#       # AR = firm return - industry index return
#       # Assumes data_tbl has an 'industry_return' column
#       data_tbl %>%
#         dplyr::mutate(abnormal_returns = firm_returns - industry_return)
#     }
#   ),
#   private = list(
#     calculate_statistics = function(data_tbl) {
#       estimation_tbl <- data_tbl %>%
#         dplyr::filter(estimation_window == 1)
# 
#       residuals <- estimation_tbl$firm_returns - estimation_tbl$industry_return
#       private$add_residuals(residuals)
#       private$first_order_autocorrelation(residuals)
# 
#       sigma <- sd(residuals, na.rm = TRUE)
#       private$.statistics$sigma <- sigma
#       private$.statistics$degree_of_freedom <- length(residuals) - 1
# 
#       # Forecast error correction
#       event_window_tbl <- data_tbl %>% dplyr::filter(event_window == 1)
#       private$calculate_forecast_error_correction(
#         sigma, nrow(estimation_tbl),
#         estimation_tbl$index_returns,
#         event_window_tbl$index_returns
#       )
#     }
#   )
# )

## ----eval=FALSE---------------------------------------------------------------
# # Create parameter set with custom model
# params <- ParameterSet$new(
#   return_model = IndustryAdjustedModel$new()
# )
# 
# # Run the event study
# task <- EventStudyTask$new(firm_data, index_data, request_data)
# task <- run_event_study(task, params)

## ----eval=FALSE---------------------------------------------------------------
# ExcessReturnModel <- R6::R6Class("ExcessReturnModel",
#   inherit = ModelBase,
#   public = list(
#     model_name = "ExcessReturnModel",
#     risk_free_rate = 0.0001,  # daily risk-free rate
# 
#     initialize = function(risk_free_rate = 0.0001) {
#       self$risk_free_rate <- risk_free_rate
#     },
# 
#     fit = function(data_tbl) {
#       private$.is_fitted <- TRUE
#       estimation_tbl <- data_tbl %>%
#         dplyr::filter(estimation_window == 1)
#       residuals <- estimation_tbl$firm_returns - self$risk_free_rate
#       private$.statistics$sigma <- sd(residuals, na.rm = TRUE)
#       private$.statistics$degree_of_freedom <- length(residuals) - 1
#       private$add_residuals(residuals)
#       private$first_order_autocorrelation(residuals)
#     },
# 
#     abnormal_returns = function(data_tbl) {
#       data_tbl %>%
#         dplyr::mutate(abnormal_returns = firm_returns - self$risk_free_rate)
#     }
#   )
# )

## ----eval=FALSE---------------------------------------------------------------
# # Factor data with date column matching firm/index data
# factor_data <- tibble::tibble(
#   date = dates,
#   smb = smb_returns,
#   hml = hml_returns,
#   risk_free_rate = rf_rate
# )
# 
# task <- EventStudyTask$new(firm_data, index_data, request_data,
#                             factor_tbl = factor_data)
# params <- ParameterSet$new(
#   return_model = FamaFrench3FactorModel$new()
# )
# task <- run_event_study(task, params)