Performs the bootstrap combined Lagrange multiplier (LM) test for autoregressive conditional heteroskedastic (ARCH) errors in vector autoregressive (VAR) models of Catani and Ahlgren (2016).
The tests of Eklund and Ter<U+00E4>svirta (2007), as well as the Multivariate LM test for ARCH as described for example in L<U+00FC>tkepohl (2006, sect. 16.5), are also included if the arguments ET respectively MARCH are set to TRUE. The bootstrap procedure for those are the same as in Catani and Ahlgren (2016).
archBootTest(fit, h = 2, B = 499, CA = TRUE, ET = TRUE, MARCH = TRUE,
dist = "norm", skT.param = c(0, 1, 0, 5))
# S3 method for archBootTest
print(x, ...)the lag length of the alternative VAR(h) model for the errors.
the number of bootstrap simulations.
if TRUE, the Catani and Ahlgren (2017) test will run.
if TRUE, the Eklund and Ter<U+00E4>svirta (2007) test will run.
if TRUE, the Multivariate LM test for ARCH will run. See e.g. L<U+00FC>tkepohl (2006, sect. 16.5).
the error distribution. Either "norm" for the standard normal distribution, or "skT" for the skew-t distribution. The parameters of the skew-t distribution can be set with the skT.param argument. Can also be a function that returns random draws as an (N-p) x K matrix or a vector of length (N-p) * K.
a vector of four parameters for the skew-t distribution in case "skT" was used for the dist argument. The function rmst is used to draw the errors and the parameters are passed as skT.param = c(xi, Omega, alpha, nu)
Object with class attribute ‘archBootTest’.
further arguments passed to or from other methods.
a list of class "ACtest".
the fit argument object.
the type of object of fit.
the lag length h of the alternative VAR(h) model for the errors.
the number of bootstrap simulations.
the number of series/equations in the fitted VAR model.
the CA input argument.
the ET input argument.
the MARCH input argument.
the dist argument object.
the Cholesky-standardized residuals.
the combined LM statistic of Catani and Ahlgren (2016), computed as 1 - min(P(CA_LMi)).
the bootstrap P. value of the combined LM test of Catani and Ahlgren (2016).
the LM statistics of Catani and Ahlgren (2016) for each time series.
an (N-p) x K matrix of the bootstrap LM statistics for each time series (columns) and bootstrap sample (rows), for the Catani and Ahlgren (2016) test.
a vector of length K with the univariate bootstrap P. values for each time series, for the Catani and Ahlgren (2016) test.
the LM statistic of the Eklund and Ter<U+00E4>svirta (2007) test.
the P.value of the Eklund and Ter<U+00E4>svirta (2007) LM test statistic.
the bootstrap P.value of the Eklund and Ter<U+00E4>svirta (2007) test.
the bootstrap LM test statistics for the Eklund and Ter<U+00E4>svirta (2007) test.
the LM statistic of the Multivariate LM test for ARCH. See e.g. L<U+00FC>tkepohl (2006, sect. 16.5).
the P.value of the MARCH LM test statistic.
the bootstrap P.value of the MARCH test.
the bootstrap LM test statistics for the MARCH test.
who ran the test and when.
computation time taken to run the test.
how the function ACtest() was called.
See the pdf version of the manual at the package's CRAN page for mathematical details of the test.
Catani, P. and Ahlgren, N. (2016). Combined Lagrange multiplier test for ARCH in vector autoregressive models, Economics and Statistics, <doi:10.1016/j.ecosta.2016.10.006>.
Dufour, J.-M., Khalaf, L., and Beaulieu, M.-C. (2010). Multivariate residual-based finite-sample tests for serial dependence and arch effects with applications to asset pricing models, Journal of Applied Econometrics, 25 (2010) 263--285.
Eklund, B. and Ter<U+00E4>svirta, T. (2007). Testing constancy of the error covariance matrix in vector models, Journal of Econometrics, 140, 753-780.
Engle, R.F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica, 50, 987-1007.
L<U+00FC>tkepohl, H. (2006), New Introduction to Multiple Time Series Analysis, Springer, New York.
VARfit to estimate a VAR(p).
# NOT RUN {
fit <- VARfit(y = VodafoneCDS, p = 3, const = TRUE, trend = FALSE)
test <- archBootTest(fit = fit, h = 5, B = 199, CA = TRUE, ET = TRUE, MARCH = TRUE, dist = "norm")
# }
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