aCompiler: What the package does (short line)

#'
#' Calculates ADF Stationarity Test
#'
#' Calculates ADF Test Info based from the urca package
#'
#'@param y Data series
#'@param type The type of test. Whether it includes a drift or trend component.
#'@param lags Number of lags to test
#'@param selectlags Whether a model criterion should be used to determine a lag length.
#'
#'@author Nicholas Dregne and Helena Ristov
#'
#'@export

adf.test <- function (y, type = c("none", "drift", "trend"), lags = 1, selectlags = c("Fixed", "AIC", "BIC")) 
{
  
  if (ncol(as.matrix(y)) > 1) stop("\ny is not a vector or univariate time series.\n")
  if (any(is.na(y)))          stop("\nNAs in y.\n")
  
    y <- as.vector(y)
  lag <- as.integer(lags)
  
  if (lag < 0) stop("\nLags must be set to an non negative integer value.\n")
  
  DNAME <- deparse(substitute(y))
  x.name <- deparse(substitute(y))
  lags <- lags + 1
  z <- diff(y)
  n <- length(z)
  x <- embed(z, lags)
  z.diff <- x[, 1]
  z.lag.1 <- y[lags:n]
  tt <- lags:n
  
  if (lags > 1) {
    if (selectlags != "Fixed") {
      critRes <- rep(NA, lags)
      for (i in 2:(lags)) {
        z.diff.lag = x[, 2:i]
        if (type == "none") 
          result <- lm(z.diff ~ z.lag.1 - 1 + z.diff.lag)
        if (type == "drift") 
          result <- lm(z.diff ~ z.lag.1 + 1 + z.diff.lag)
        if (type == "trend") 
          result <- lm(z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)
        critRes[i] <- AIC(result, k = switch(selectlags, AIC = 2, BIC = log(length(z.diff))))
      }
      lags <- which.min(critRes)
    }
    z.diff.lag = x[, 2:lags]
    if (type == "none") {
      result <- lm(z.diff ~ z.lag.1 - 1 + z.diff.lag)
      tau <- coef(summary(result))[1, 3]
      teststat <- as.matrix(tau)
      colnames(teststat) <- "tau1"
    }
    if (type == "drift") {
      result <- lm(z.diff ~ z.lag.1 + 1 + z.diff.lag)
      tau <- coef(summary(result))[2, 3]
      phi1.reg <- lm(z.diff ~ -1 + z.diff.lag)
      phi1 <- anova(phi1.reg, result)$F[2]
      teststat <- as.matrix(t(c(tau, phi1)))
      colnames(teststat) <- c("tau2", "phi1")
    }
    if (type == "trend") {
      result <- lm(z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)
      tau <- coef(summary(result))[2, 3]
      phi2.reg <- lm(z.diff ~ -1 + z.diff.lag)
      phi3.reg <- lm(z.diff ~ z.diff.lag)
      phi2 <- anova(phi2.reg, result)$F[2]
      phi3 <- anova(phi3.reg, result)$F[2]
      teststat <- as.matrix(t(c(tau, phi2, phi3)))
      colnames(teststat) <- c("tau3", "phi2", "phi3")
    }
  } else {
    if (type == "none") {
      result <- lm(z.diff ~ z.lag.1 - 1)
      tau <- coef(summary(result))[1, 3]
      teststat <- as.matrix(tau)
      colnames(teststat) <- "tau1"
    }
    if (type == "drift") {
      result <- lm(z.diff ~ z.lag.1 + 1)
      phi1.reg <- lm(z.diff ~ -1)
      phi1 <- anova(phi1.reg, result)$F[2]
      tau <- coef(summary(result))[2, 3]
      teststat <- as.matrix(t(c(tau, phi1)))
      colnames(teststat) <- c("tau2", "phi1")
    }
    if (type == "trend") {
      result <- lm(z.diff ~ z.lag.1 + 1 + tt)
      phi2.reg <- lm(z.diff ~ -1)
      phi3.reg <- lm(z.diff ~ 1)
      phi2 <- anova(phi2.reg, result)$F[2]
      phi3 <- anova(phi3.reg, result)$F[2]
      tau <- coef(summary(result))[2, 3]
      teststat <- as.matrix(t(c(tau, phi2, phi3)))
      colnames(teststat) <- c("tau3", "phi2", "phi3")
    }
  }
  
  rownames(teststat) <- "statistic"
  testreg <- summary(result)
  res <- residuals(testreg)
  if (n < 25)             rowselec <- 1
  if (25 <= n & n < 50)   rowselec <- 2
  if (50 <= n & n < 100)  rowselec <- 3
  if (100 <= n & n < 250) rowselec <- 4
  if (250 <= n & n < 500) rowselec <- 5
  if (n >= 500)           rowselec <- 6
  
  if (type == "none") {
    cval.tau1 <- rbind(c(-2.66, -1.95, -1.6), c(-2.62, -1.95, -1.61), c(-2.6, -1.95, -1.61), c(-2.58, -1.95, -1.62), c(-2.58, -1.95, -1.62), c(-2.58, -1.95, -1.62))
    cvals <- t(cval.tau1[rowselec, ])
    testnames <- "tau1"
  }
  
  if (type == "drift") {
    cval.tau2 <- rbind(c(-3.75, -3, -2.63), c(-3.58, -2.93, -2.6), c(-3.51, -2.89, -2.58), c(-3.46, -2.88, -2.57), c(-3.44, -2.87, -2.57), c(-3.43, -2.86, -2.57))
    cval.phi1 <- rbind(c(7.88, 5.18, 4.12), c(7.06, 4.86, 3.94), c(6.7, 4.71, 3.86), c(6.52, 4.63, 3.81), c(6.47, 4.61, 3.79), c(6.43, 4.59, 3.78))
    cvals <- rbind(cval.tau2[rowselec, ], cval.phi1[rowselec, ])
    testnames <- c("tau2", "phi1")
  }
  
  if (type == "trend") {
    cval.tau3 <- rbind(c(-4.38, -3.6, -3.24), c(-4.15, -3.5, -3.18), c(-4.04, -3.45, -3.15), c(-3.99, -3.43, -3.13), c(-3.98, -3.42, -3.13), c(-3.96, -3.41, -3.12))
    cval.phi2 <- rbind(c(8.21, 5.68, 4.67), c(7.02, 5.13, 4.31), c(6.5, 4.88, 4.16), c(6.22, 4.75, 4.07), c(6.15, 4.71, 4.05), c(6.09, 4.68, 4.03))
    cval.phi3 <- rbind(c(10.61, 7.24, 5.91), c(9.31, 6.73, 5.61), c(8.73, 6.49, 5.47), c(8.43, 6.49, 5.47), c(8.34, 6.3, 5.36), c(8.27, 6.25, 5.34))
    cvals <- rbind(cval.tau3[rowselec, ], cval.phi2[rowselec, ], cval.phi3[rowselec, ])
    testnames <- c("tau3", "phi2", "phi3")
  }
  
  colnames(cvals) <- c("1pct", "5pct", "10pct")
  rownames(cvals) <- testnames
  
  Output <- list(cvals = cvals, teststat = teststat, testreg = testreg, res = res)
  return(Output)
}