TseriesTests {fSeries} | R Documentation |
A collection and description of functions for testing various
aspects of time series, including independence, normality of
the distribution, and neglected nonlinearities.
The family of time series tests includes the following hypothesis tests:
bdsTest | Brock–Dechert–Scheinkman test for iid series, |
jbTest | Jarque–Bera Test for normality, |
tnnTest | Teraesvirta NN test for neglected nonlinearity, |
wnnTest | White NN test for neglected nonlinearity. |
bdsTest(x, m = 3, eps = seq(0.5*sd(x), 2*sd(x), length = 4), trace = FALSE) jbTest(x) tnnTest(x, lag = 1, type = c("Chisq", "F"), scale = TRUE, ...) wnnTest(x, lag = 1, qstar = 2, q = 10, range = 4, type = c("Chisq", "F"), scale = TRUE, ...)
eps |
a numeric vector of epsilon values for close points. The
BDS test is computed for each element of eps . It should
be set in terms of the standard deviation of x .
|
lag |
an integer which specifies the model order in terms of lags. |
m |
an integer indicating that the BDS test statistic is computed
for embedding dimensions 2 , ..., m .
|
q |
an integer representing the number of phantom hidden units used to compute the test statistic. |
qstar |
the test is conducted using qstar principal
components of the phantom hidden units. The first principal
component is omitted since in most cases it appears to be collinear
with the input vector of lagged variables. This strategy preserves
power while still conserving degrees of freedom.
|
range |
the input to hidden unit weights are initialized uniformly
over [-range/2, range/2] .
|
scale |
a logical indicating whether the data should be scaled
before computing the test statistic. The default arguments to
scale are used.
|
trace |
a logical indicating whether some informational output is traced. |
type |
indicates which variant of a test will be computed. The choice must be one of the elements specified in the input vector, by default the first entry is selected. |
x |
a numeric vector or time series. |
... |
further arguments to be passed from or to methods. |
Brock–Dechert–Sheinkman Test:
The bdsTest
test examines the spatial dependence of the
observed series. To do this, the series is embedded in m
-space
and the dependence of x
is examined by counting near
points. Points for which the distance is less than eps
are
called near. The BDS test statistic is asymptotically standard Normal.
Note, that missing values are not allowed.
There is a special print method for objects of class "bdsTest"
which by default uses 4 digits to format real numbers.
[tseries:bds.test]
Jarque–Bera Test:
The jbTest
evaluates the hypothesis that the series has a
normal distribution with unspecified mean and variance, against the
alternative that the series does not have a normal distribution. The
test is based on the sample skewness and kurtosis. For a true normal
distribution, the sample skewness should be near 0 and the sample
kurtosis should be near 3. The Jarque-Bera test determines whether
the sample skewness and kurtosis are unusually different than their
expected values, as measured by a chi-square statistic. The Jarque-Bera
test is an asymptotic test, and should not be used with small samples.
You may want to use the lillieTest
for small samples.
[jb.test]
Phillips–Perron Test:
The general regression equation which incorporates a constant and a
linear trend is used and the Z(alpha)
or Z(t_alpha)
statistic for a first order autoregressive coefficient equals one are
computed. To estimate sigma^2
the Newey-West estimator is
used. If lshort
is TRUE
, then the truncation lag
parameter is set to trunc(4*(n/100)^0.25)
, otherwise
trunc(12*(n/100)^0.25)
is used. The p-values are interpolated
from Table 4.1 and 4.2, p. 103 of Banerjee et al. (1993). If the
computed statistic is outside the table of critical values, then a
warning message is generated.
[tseries:pp.test]
Teraesvirta Neural Network Test:
The null is the hypotheses of linearity in mean
. This test
uses a Taylor series expansion of the activation function to
arrive at a suitable test statistic. If type
equals "F"
,
then the F-statistic instead of the Chi-Squared statistic
is used in analogy to the classical linear regression.
Missing values are not allowed.
[tseries:teraesvirta.test]
White Neural Network Test:
The null is the hypotheses of linearity in ``mean''. This
type of test is consistent against arbitrary nonlinearity
in mean. If type
equals "F"
, then the F-statistic
instead of the Chi-Squared statistic is used in analogy to the
classical linear regression.
[tseries:white.test]
A list with class "htest"
containing the following
components:
$statistic |
the value of the test statistic. |
$parameter |
the lag order. |
$p.value |
the p-value of the test. |
$method |
a character string indicating what type of test was performed. |
$data.name |
a character string giving the name of the data. |
$alternative |
a character string describing the alternative hypothesis. |
There is nothing really new in this package. The benefit you will get from this help page, that we bring together a collection of time series tests from several R-packages which may be of interest for economists and financial engineers.
On the other hand the user can still use the underlying function calls from the imported R-packages.
The output of the various hypothesis tests is an object of class
htest
. The associated print
method gives an unique
report about the test results.
Adrian Trapletti for the tests from R's tseries package,
Blake LeBaron for the bds
C program,
Diethelm Wuertz for the Rmetrics R-port.
Brock, W.A., Dechert W.D., Sheinkman J.A. (1987); A Test of Independence Based on the Correlation Dimension, SSRI no. 8702, Department of Economics, University of Wisconsin, Madison.
Conover, W.J. (1980); Practical Nonparametric Statistics, New York, Wiley.
Cromwell J.B., Labys W.C., Terraza M. (1994); Univariate Tests for Time Series Models, Sage, Thousand Oaks, CA, pages 32–36.
Lee T.H., White H., Granger C.W.J. (1993); Testing for neglected nonlinearity in time series models, Journal of Econometrics 56, 269–290.
Teraesvirta T., Lin C.F., Granger C.W.J. (1993); Power of the Neural Network Linearity Test, Journal of Time Series Analysis 14, 209–220.
classicalTests
,
regressionTests
.
## bdsTest - xmpSeries("\nNext: Brock-Dechert-Sheinkman Test for iid Series >") # iid Time Series: par(mfrow = c(3,1)) x = rnorm(100) plot(x, type = "l", main = "iid Time Series") bdsTest(x, m = 3) # Non Identically Distributed Time Series: x = c(rnorm(50), runif(50)) plot(x, type = "l", main = "Non-iid Time Series") bdsTest(x, m = 3) # Non Independent Innovations from Quadratic Map: x = rep(0.2, 100) for (i in 2:100) x[i] = 4*(1-x[i-1])*x[i-1] plot(x, type = "l", main = "Quadratic Map") bdsTest(x, m = 3) ## jbTest - xmpSeries("\nNext: Jarque-Bera Test for Normality >") # Null: x = rnorm(100) jbTest(x) # Alternative: x = runif(100) jbTest(x) ## tnnTest - xmpSeries("\nNext: Teraesvirta NN test for Neglected Nonlinearity >") # Time Series Non-linear in ``mean'' regression par(mfrow = c(2, 1)) n = 1000 x = runif(1000, -1, 1) tnnTest(x) # Generate time series which is nonlinear in ``mean'' x[1] = 0.0 for (i in (2:n)) { x[i] = 0.4*x[i-1] + tanh(x[i-1]) + rnorm (1, sd = 0.5) } plot(x, main = "Teraesvirta Test", type = "l") tnnTest(x) ## wnnTest - xmpSeries("\nNext: White NN test for Neglected Nonlinearity >") # Time Series Non-Linear in ``mean'' Regression par(mfrow = c(2, 1)) n = 1000 x = runif(1000, -1, 1) wnnTest(x) # Generate time series which is nonlinear in ``mean'' x[1] = 0.0 for (i in (2:n)) { x[i] = 0.4*x[i-1] + tanh(x[i-1]) + rnorm (1, sd = 0.5) } plot(x, main = "White Test", type = "l") wnnTest(x)