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##---------------------------------------------------------------------------------------------
# Functions for Classes by Prof. Jin Man Lee
# DePaul University
#
# To use any functions here, you need to run the following code to read into your program
# source("http://bigblue.depaul.edu/jlee141/econdata/R/func_tslib.R")
#

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##---------------------------------------------------------------------------------------------##

adf_fulltest <- function(indata,maxlag){
if (!require("fUnitRoots")) install.packages("fUnitRoots")
maxlag <- maxlag
A <- matrix(nrow=maxlag+1,ncol=7)
for (k in 0:maxlag) {
  k1 <- k + 1
  adf0 <- adfTest(indata, lags = k, type = "nc")    
       A[k1,2] <- round(adf0@test$statistic[1],digit=2) ;  A[k1,3] <- adf0@test$p.value ;  A[k1,1] <- k
  adf0 <- adfTest(indata, lags = k, type = "c")     
       A[k1,4] <- round(adf0@test$statistic[1],digit=2) ;  A[k1,5] <- adf0@test$p.value    
  adf0 <- adfTest(indata, lags = k, type = "ct")   
       A[k1,6] <- round(adf0@test$statistic[1],digit=2) ;  A[k1,7] <- round(adf0@test$p.value,digit=2)     
   }  
colnames(A) <- c("LAG","ADF","P(ADF)","ADF_C","P(ADF_C)","ADF_CT","P(ADF_CT)")
return(A)
}
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# Local Projection Model Estimation
##---------------------------------------------------------------------------------------------##

localproj <- function(endog_data,exog_data,lagend,lagexo,maxlag){
results_lin <- lp_lin(endog_data      , 
                      lags_endog_lin = lagend,     # Number of lags for endogenous data
                      exog_data=exog_data ,
                      lags_exog      = lagexo  ,
                      trend          = 0,     # 0 = no trend, 1 = trend, 2 = trend & trend^2    
                      shock_type     = 1,     # 0 = standard deviation shock, 1 = unit shock
                      confint        = 1.96,  # Width of confidence bands: # 1 = 68%, 1.67 = 90%, 1.96 = 95%
                      hor            = maxlag)    # Number of cores to use. When NULL, the number of cores # is chosen automatically    
g1 <- summary(results_lin)

linear_plots <- plot_lin(results_lin)
linear_plots[[1]]
g2 <- linear_plots[[1]]

lin_plots_all <- sapply(linear_plots, ggplotGrob)
g3 <- marrangeGrob(lin_plots_all, nrow = ncol(endog_data), ncol = ncol(endog_data), top = NULL)
gout <- list(g1,g2,g3)
return(gout)
}

################################################################################################
# Automatic Unit Root Tests using tseries
##--------------------------------------------------------------------------------------------##

unitroot_tests <- function(series) {
       if (!require("tseries")) install.packages("tseries")
        adf_test <- adf.test(series, alternative = "stationary")
        kpss_test <- kpss.test(series, null = "Level", lshort = TRUE)
        pp_test <- pp.test(series)
	
        data.frame(
        Test = c("ADF Test", "PP Test", "KPSS Test"),
        Lag_order = c(adf_test$parameter,
                      pp_test$parameter,
                      kpss_test$parameter),
        Statistic = c(adf_test$statistic,
                      pp_test$statistic,
                      kpss_test$statistic),
        Stationary_P_Value = c(adf_test$p.value, 
                               pp_test$p.value, 
                               kpss_test$p.value))
}

#-----------------------------------------------------------------------------#
# Function for the 
# calculate_price_index(coeff_df, "200801","coeff","yearmm")
# Make sure to have the rest options should have the quotations 
calculate_price_index <- function(data, base_ym, var_input, yearmm_column) {
  # Ensure column names are provided correctly
  if (!yearmm_column %in% names(data) || !var_input %in% names(data)) {
    stop("Column names provided do not exist in the dataframe.")
  }
  
  # Filter to get the base year-month row
  base_val <- data %>%
    filter(get(yearmm_column) == base_ym) %>%
    pull(var_input) %>%
    unlist()
  
  # Check if base_val is empty or NA
  if (length(base_val) == 0 || is.na(base_val)) {
    stop("Base value for specified year-month not found or NA.")
  }
  
  # Calculate the price index using the base value
  index_column <- 100 * data[[var_input]] / base_val[1]
  
  # Return the updated dataframe with the price index
  return(index_column)
}

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