tests/testthat/test-hp_filter.R

In MacroFilters: Robust Trend-Cycle Decomposition for Macroeconomic Time Series

# ── Tests: hp_filter() ────────────────────────────────────────────────────────

# ── 1. Identity: trend + cycle == data ───────────────────────────────────────

test_that("HP trend + cycle reconstructs original data", {
  set.seed(1)
  y <- cumsum(rnorm(100))
  result <- hp_filter(y, lambda = 1600)
  expect_equal(result$trend + result$cycle, result$data, tolerance = 1e-9)
})

test_that("Identity holds for ts input with auto-lambda", {
  set.seed(2)
  y_ts <- ts(cumsum(rnorm(80)), start = c(2000, 1), frequency = 4)
  result <- hp_filter(y_ts)
  expect_equal(result$trend + result$cycle, result$data, tolerance = 1e-9)
})

# ── 2. Invariance: same result regardless of input class ─────────────────────

test_that("HP filter produces identical results across input classes", {
  set.seed(42)
  y_num <- cumsum(rnorm(100))
  y_ts  <- ts(y_num, start = c(2000, 1), frequency = 4)

  res_num <- hp_filter(y_num, lambda = 1600)
  res_ts  <- hp_filter(y_ts, lambda = 1600)

  expect_equal(res_num$trend, res_ts$trend, tolerance = 1e-12)
  expect_equal(res_num$cycle, res_ts$cycle, tolerance = 1e-12)
})

test_that("HP filter produces identical results with xts input", {
  skip_if_not_installed("xts")
  set.seed(42)
  y_num <- cumsum(rnorm(100))
  dates <- seq(as.Date("2000-01-01"), by = "quarter", length.out = 100)
  y_xts <- xts::xts(y_num, order.by = dates)

  res_num <- hp_filter(y_num, lambda = 1600)
  res_xts <- hp_filter(y_xts, lambda = 1600)

  expect_equal(res_num$trend, res_xts$trend, tolerance = 1e-12)
  expect_equal(res_num$cycle, res_xts$cycle, tolerance = 1e-12)
})

test_that("HP filter produces identical results with zoo input", {
  skip_if_not_installed("zoo")
  set.seed(42)
  y_num <- cumsum(rnorm(100))
  dates <- seq(as.Date("2000-01-01"), by = "quarter", length.out = 100)
  y_zoo <- zoo::zoo(y_num, order.by = dates)

  res_num <- hp_filter(y_num, lambda = 1600)
  res_zoo <- hp_filter(y_zoo, lambda = 1600)

  expect_equal(res_num$trend, res_zoo$trend, tolerance = 1e-12)
  expect_equal(res_num$cycle, res_zoo$cycle, tolerance = 1e-12)
})

# ── 3. Stress tests ──────────────────────────────────────────────────────────

test_that("HP filter works with minimum-length series (n = 3, 4, 5)", {
  for (n in 3:5) {
    y <- seq_len(n) * 1.0
    result <- hp_filter(y, lambda = 1600)
    expect_equal(result$trend + result$cycle, result$data, tolerance = 1e-9)
  }
})

test_that("HP filter errors when n < 3", {
  expect_error(hp_filter(c(1, 2), lambda = 1600), "at least 3")
  expect_error(hp_filter(1, lambda = 1600), "at least 3")
})

test_that("HP filter handles long series (n = 10000)", {
  set.seed(99)
  y <- cumsum(rnorm(10000))
  result <- hp_filter(y, lambda = 1600)
  expect_equal(result$trend + result$cycle, result$data, tolerance = 1e-9)
})

test_that("Auto-lambda from ts frequency = 4 gives lambda = 1600", {
  y_ts <- ts(1:50 * 1.0, frequency = 4)
  result <- hp_filter(y_ts)
  expect_equal(result$meta$lambda, 6.25 * 4^4)
})

test_that("Auto-lambda from ts frequency = 12 gives lambda = 129600", {
  y_ts <- ts(1:50 * 1.0, frequency = 12)
  result <- hp_filter(y_ts)
  expect_equal(result$meta$lambda, 6.25 * 12^4)
})

test_that("freq parameter overrides default when lambda is NULL", {
  y <- 1:50 * 1.0
  result <- suppressWarnings(hp_filter(y, freq = 12))
  expect_equal(result$meta$lambda, 6.25 * 12^4)
})

test_that("Warning emitted when frequency cannot be determined", {
  y <- cumsum(rnorm(50))
  expect_warning(hp_filter(y), "Cannot determine series frequency")
})

# ── 4. Output format ─────────────────────────────────────────────────────────

test_that("HP filter returns macrofilter object with correct meta", {
  set.seed(7)
  y <- cumsum(rnorm(100))
  result <- hp_filter(y, lambda = 1600)

  expect_s3_class(result, "macrofilter")
  expect_equal(result$meta$method, "HP")
  expect_equal(result$meta$lambda, 1600)
  expect_true(!is.null(result$meta$compute_time))
  expect_true(is.numeric(result$meta$compute_time))
})

test_that("Explicit lambda is stored exactly in meta", {
  y <- 1:20 * 1.0
  result <- hp_filter(y, lambda = 6.25)
  expect_equal(result$meta$lambda, 6.25)
})