tests/testthat/test-basic.r

In frequencyConnectedness: Spectral Decomposition of Connectedness Measures

# Various tests
library(vars)

context("Rolling spillovers")
test_that("NA data has descriptive error", {
	data(exampleSim)
	exampleSim <- exampleSim[1:200,]
        exampleSim[2, 1] = NaN
        params_est = list(p = 2, type = "const")
        
	H <- 200
	expect_error(spilloverRollingDY09(exampleSim, n.ahead = 100, no.corr = F, "VAR", params_est = params_est, window = 100), "Your data contains")
})

context("FEVD checks")
test_that("The FFT FEVD reconstructs on the original one", {
	data(exampleSim)
	exampleSim <- exampleSim[1:100,]
	est <- VAR(exampleSim, p = 4, type ="const")
	H <- 200
	expect_equivalent(
		frequencyConnectedness::fevd(est, n.ahead = H, no.corr = F), 
		Reduce('+', frequencyConnectedness::fftFEVD(est, n.ahead = H, no.corr = F))
		)
})

test_that("The FFT GenFEVD recunstructs on the original one", {
	data(exampleSim)
	exampleSim <- exampleSim[1:100,]
	est <- VAR(exampleSim, p = 4, type ="const")
	H <- 200
	expect_equivalent(
		frequencyConnectedness::genFEVD(est, n.ahead = H, no.corr = F), 
		Reduce('+', frequencyConnectedness::fftGenFEVD(est, n.ahead = H, no.corr = F))
		)
})

test_that("The FEVD from our package is the same as from vars package", {
	data(exampleSim)
	exampleSim <- exampleSim[1:100,]
	est <- VAR(exampleSim, p = 4, type ="const")
	H <- 200
	expect_true(
		all(
			frequencyConnectedness::fevd(est, n.ahead = H, no.corr = F)-
			t(sapply(vars::fevd(est, H), function(i) i[H,]))
			<1e-5
			)
		)
})

context("Integrity of DY12")

# These estimates were done using trusted algorithm, so backchecking
test_that("The DY12 spillover gives the proofed result table", {
	data <- read.csv("simul_data.csv", header = F)
	output <- as.matrix(read.csv("DY12.csv", header = F))
	t <- as.matrix(frequencyConnectedness::spilloverDY12(VAR(data, p = 2, type = "const"), 100, no.corr = F)$tables[[1]])
	colnames(t) <- rownames(t) <- colnames(output) <- rownames(output) <- NULL

	expect_equal(100*t, output, tolerance = 1e-3)
})

context("Spillovers checks")

test_that("If bounds do not cover the whole partition, give warning", {	
	bounds <- c(pi + 0.001, 2.5530220, 1.8705131, 0.6906649, 0.4298473, 0.000001)

	expect_warning(
		frequencyConnectedness::getPartition(bounds, 200), 
		"The selected partition does not cover the whole range."
		)
})

test_that("If the partition is not decreasing, produce an error.", {
	bounds <- c(2,3,1,4,5)
	expect_error(
		frequencyConnectedness::getPartition(bounds, 300), 
		"The bounds must be in decreasing order."
		)
})

test_that("If the bounds are too detailed for the number of steps ahead, produce an error.", {
	bounds <- c(3.1425927, 2.5530220, 1.8705131, 0.6906649, 0.4298473, 0.4130271, 0.0000000)
	expect_error(
		frequencyConnectedness::getPartition(bounds, 200)
		)
})


estimates <- list(
	simple_VAR1 = {
		data(exampleSim)
		exampleSim <- exampleSim[1:100,]
		est <- VAR(exampleSim, p = 4, type ="const")
	},
	simple_VAR2 = {
		data(volatilities)
		volatilities <- volatilities[1:1200,]
		est <- VAR(na.omit(volatilities), p = 10, type = "const")
	})
for (i in 1:length(estimates)) {
	context(sprintf("Sanity checks on estimate %s.", names(estimates)[i]))
	test_that("The BK absolute spillovers tables reconstruct perfectly in both cases", {
		# Create random bounds that cover the whole range
		bounds <- c(pi + 0.001, 2.5530220, 1.8705131, 0.6906649, 0.4298473, 0)
		H <- 200
		expect_equivalent(
			frequencyConnectedness::spilloverDY09(estimates[[i]], n.ahead = H, no.corr=F)$tables[[1]], 
			Reduce(`+`, frequencyConnectedness::spilloverBK09(estimates[[i]], n.ahead = H, partition = bounds, no.corr=F)$tables)
			)
		expect_equivalent(
			frequencyConnectedness::spilloverDY12(estimates[[i]], n.ahead = H, no.corr=F)$tables[[1]], 
			Reduce(`+`, frequencyConnectedness::spilloverBK12(estimates[[i]], n.ahead = H, partition = bounds, no.corr=F)$tables)
			)
		expect_equivalent(
			frequencyConnectedness::spilloverDY09(estimates[[i]], n.ahead = H, no.corr=T)$tables[[1]], 
			Reduce(`+`, frequencyConnectedness::spilloverBK09(estimates[[i]], n.ahead = H, partition = bounds, no.corr=T)$tables)
			)
		expect_equivalent(
			frequencyConnectedness::spilloverDY12(estimates[[i]], n.ahead = H, no.corr=T)$tables[[1]], 
			Reduce(`+`, frequencyConnectedness::spilloverBK12(estimates[[i]], n.ahead = H, partition = bounds, no.corr=T)$tables)
			)
	})

	test_that("The BK absolute spillovers reconstruct perfectly in both cases", {
		# Create random bounds that cover the whole range
		bounds <- c(pi + 0.001, 2.5530220, 1.8705131, 0.6906649, 0.4298473, 0)
		H <- 200
		expect_equivalent(
			frequencyConnectedness::overall(frequencyConnectedness::spilloverDY09(estimates[[i]], n.ahead = H, no.corr=F))[[1]], 
			Reduce('+', frequencyConnectedness::overall(frequencyConnectedness::spilloverBK09(estimates[[i]], n.ahead = H, partition = bounds, no.corr=F), within = F))
			)
		expect_equivalent(
			frequencyConnectedness::overall(frequencyConnectedness::spilloverDY12(estimates[[i]], n.ahead = H, no.corr=F))[[1]], 
			Reduce('+', frequencyConnectedness::overall(frequencyConnectedness::spilloverBK12(estimates[[i]], n.ahead = H, partition = bounds, no.corr=F), within = F))
			)
	})

	test_that("Test that spillovers are smaller than hundred", {
		# Create random bounds that cover the whole range
		H <- 200

		expect_true(
			frequencyConnectedness::overall(frequencyConnectedness::spilloverDY09(estimates[[i]], n.ahead = H, no.corr=F)) < 100
			)
		expect_true(
			frequencyConnectedness::overall(frequencyConnectedness::spilloverDY12(estimates[[i]], n.ahead = H, no.corr=F)) < 100 
			)
		expect_true(
			frequencyConnectedness::overall(frequencyConnectedness::spilloverDY09(estimates[[i]], n.ahead = H, no.corr=T)) < 100
			)
		expect_true(
			frequencyConnectedness::overall(frequencyConnectedness::spilloverDY12(estimates[[i]], n.ahead = H, no.corr=T)) < 100
			)
	})

	test_that("Test that TO spillovers are smaller than hundred", {
		# Create random bounds that cover the whole range
		H <- 200

		expect_true(
			all(
				frequencyConnectedness::to(frequencyConnectedness::spilloverDY09(estimates[[i]], n.ahead = H, no.corr=F))[[1]] < 100)
			)
		expect_true(
			all(
				frequencyConnectedness::to(frequencyConnectedness::spilloverDY12(estimates[[i]], n.ahead = H, no.corr=F))[[1]] < 100)
			)
	})

	test_that("Test that TO spillovers sum up to the overall", {
		# Create random bounds that cover the whole range
		H <- 200

		expect_equivalent(
			sum(frequencyConnectedness::to(frequencyConnectedness::spilloverDY09(estimates[[i]], n.ahead = H, no.corr=F))[[1]]), 
			frequencyConnectedness::overall(frequencyConnectedness::spilloverDY09(estimates[[i]], n.ahead = H, no.corr=F))[[1]]
			)
		expect_equivalent(
			sum(frequencyConnectedness::to(frequencyConnectedness::spilloverDY12(estimates[[i]], n.ahead = H, no.corr=F))[[1]]), 
			frequencyConnectedness::overall(frequencyConnectedness::spilloverDY12(estimates[[i]], n.ahead = H, no.corr=F))[[1]]
			)
	})

	test_that("Test that FROM spillovers sum up to the overall", {
		# Create random bounds that cover the whole range
		H <- 200

		expect_equivalent(
			sum(frequencyConnectedness::from(frequencyConnectedness::spilloverDY09(estimates[[i]], n.ahead = H, no.corr=F))[[1]]), 
			frequencyConnectedness::overall(frequencyConnectedness::spilloverDY09(estimates[[i]], n.ahead = H, no.corr=F))[[1]]
			)
		expect_equivalent(
			sum(frequencyConnectedness::from(frequencyConnectedness::spilloverDY12(estimates[[i]], n.ahead = H, no.corr=F))[[1]]), 
			frequencyConnectedness::overall(frequencyConnectedness::spilloverDY12(estimates[[i]], n.ahead = H, no.corr=F))[[1]]
			)
	})

	test_that("Test that for no.corr=T DY09 == DY12.", {
		# Create random bounds that cover the whole range
		H <- 200

		expect_equivalent(
			frequencyConnectedness::overall(frequencyConnectedness::spilloverDY09(estimates[[i]], n.ahead = H, no.corr=T))[[1]], 
			frequencyConnectedness::overall(frequencyConnectedness::spilloverDY12(estimates[[i]], n.ahead = H, no.corr=T))[[1]]
			)
	})

}