regression - 调整滚动回归

Question

我在http://christophj.github.io/replicating/r/replicating-goyal-welch-2008/上找到了以下 R 代码，我想调整代码以获得我的 OOS 估计值的滚动回归。我尝试在 R 中使用 rollRegres 函数，但它不起作用。此外，我的估计应该针对滚动回归的新值进行更新。代码如下所示：

get_statistics <- function(ts_df, indep, dep, h=1, start=1871, end=2005, est_periods_OOS = 20) {
  #### IS ANALYSIS
  #1. Historical mean model
  avg   <- mean(window(ts_df, start, end)[, dep], na.rm=TRUE)
  IS_error_N <- (window(ts_df, start, end)[, dep] - avg)
  #2. OLS model
  reg <- dyn$lm(eval(parse(text=dep)) ~ lag(eval(parse(text=indep)), -1), data=window(ts_df, start, end)) 
  IS_error_A <- reg$residuals
  ### 
  ####OOS ANALYSIS
  OOS_error_N <- numeric(end - start - est_periods_OOS)
  OOS_error_A <- numeric(end - start - est_periods_OOS)
  #Only use information that is available up to the time at which the forecast is made
  j <- 0
  for (i in (start + est_periods_OOS):(end-1)) {
    j <- j + 1
    #Get the actual ERP that you want to predict
    actual_ERP <- as.numeric(window(ts_df, i+1, i+1)[, dep])
    #1. Historical mean model
    OOS_error_N[j] <- actual_ERP - mean(window(ts_df, start, i)[, dep], na.rm=TRUE)
    #2. OLS model
    reg_OOS <- dyn$lm(eval(parse(text=dep)) ~ lag(eval(parse(text=indep)), -1), 
                      data=window(ts_df, start, i))
    #Compute_error
    df <- data.frame(x=as.numeric(window(ts_df, i, i)[, indep]))
    names(df) <- indep
    pred_ERP   <- predict.lm(reg_OOS, newdata=df)
    OOS_error_A[j] <-  pred_ERP - actual_ERP
  }
  
  #Compute statistics 
  MSE_N <- mean(OOS_error_N^2)
  MSE_A <- mean(OOS_error_A^2)
  T <- length(!is.na(ts_df[, dep]))
  OOS_R2  <- 1 - MSE_A/MSE_N
  #Is the -1 enough (maybe -2 needed because of lag)?
  OOS_oR2 <- OOS_R2 - (1-OOS_R2)*(reg$df.residual)/(T - 1) 
  dRMSE <- sqrt(MSE_N) - sqrt(MSE_A)
  ##
  #### CREATE PLOT
  IS  <- cumsum(IS_error_N[2:length(IS_error_N)]^2)-cumsum(IS_error_A^2)
  OOS <- cumsum(OOS_error_N^2)-cumsum(OOS_error_A^2)
  df  <- data.frame(x=seq.int(from=start + 1 + est_periods_OOS, to=end), 
                    IS=IS[(1 + est_periods_OOS):length(IS)], 
                    OOS=OOS) #Because you lose one observation due to the lag
  #Shift IS errors vertically, so that the IS line begins 
  # at zero on the date of first OOS prediction. (see Goyal/Welch (2008, p. 1465))
  df$IS <- df$IS - df$IS[1] 
  df  <- melt(df, id.var="x") 
  plotGG <- ggplot(df) + 
    geom_line(aes(x=x, y=value,color=variable)) + 
    geom_rect(data=data.frame(),#Needed by ggplot2, otherwise not transparent
              aes(xmin=1973, xmax=1975,ymin=-0.2,ymax=0.2), 
              fill='red',
              alpha=0.1) + 
    scale_y_continuous('Cumulative SSE Difference', limits=c(-0.2, 0.2)) + 
    scale_x_continuous('Year')
  ##
  return(list(IS_error_N = IS_error_N,
              IS_error_A = reg$residuals,
              OOS_error_N = OOS_error_N,
              OOS_error_A = OOS_error_A,
              IS_R2 = summary(reg)$r.squared, 
              IS_aR2 = summary(reg)$adj.r.squared, 
              OOS_R2  = OOS_R2,
              OOS_oR2 = OOS_oR2,
              dRMSE = dRMSE,
              plotGG = plotGG))
}

任何人都可以帮忙。提前谢谢了。

标签： regressionrolling-computationadjustment