R: DEPRECATED, USE 'quantile_residual_tests' INSTEAD! Quantile...

quantileResidualTests {uGMAR}

R Documentation

DEPRECATED, USE `quantile_residual_tests` INSTEAD! Quantile residual tests for GMAR, StMAR , and G-StMAR models

Description

quantileResidualTests performs quantile residual tests for GMAR, StMAR, and G-StMAR models, testing normality, autocorrelation, and conditional heteroscedasticity of the quantile residuals. DEPRECATED, USE quantile_residual_tests INSTEAD!

Usage

quantileResidualTests(
  gsmar,
  lags_ac = c(1, 3, 6, 12),
  lags_ch = lags_ac,
  nsimu = 1,
  print_res = TRUE,
  lagsAC = NULL,
  lagsCH = NULL,
  printRes = NULL
)

Arguments

`gsmar`	a class 'gsmar' object, typically generated by `fitGSMAR` or `GSMAR`.
`lags_ac`	a numeric vector of positive integers specifying the lags for which autocorrelation is tested.
`lags_ch`	a numeric vector of positive integers specifying the lags for which conditional heteroscedasticity is tested.
`nsimu`	a positive integer specifying to how many simulated observations the covariance matrix Omega (see Kalliovirta (2012)) should be based on. If smaller than data size, then omega will be based on the given data and not on simulated data. Having the covariance matrix omega based on a large simulated sample might improve the tests size properties.
`print_res`	a logical argument defining whether the results should be printed or not.
`lagsAC`	deprecated! Use `lags_ac` instead.
`lagsCH`	deprecated! Use `lags_ch` instead.
`printRes`	deprecated! Use `print_res` instead.

Details

DEPRECATED! USE quantile_residual_tests INSTEAD!

For a correctly specified GSMAR model employing the maximum likelihood estimator, the quantile residuals are asymptotically independent with standard normal distribution. They can hence be used in a similar manner to conventional Pearson's residuals. For more details about quantile residual based diagnostics, and in particular, about the quantile residual tests, see the cited article by Kalliovirta (2012).

Value

Returns an object of class 'qrtest' containing the test results in data frames. In the cases of autocorrelation and conditional heteroscedasticity tests, the returned object also contains the associated individual statistics and their standard errors, discussed in Kalliovirta (2012) at the pages 369-370.

Suggested packages

Install the suggested package "gsl" for faster evaluations in the cases of StMAR and G-StMAR models. For large StMAR and G-StMAR models with large data, the evaluations may take significantly long time without the package "gsl".

References

Galbraith, R., Galbraith, J. 1974. On the inverses of some patterned matrices arising in the theory of stationary time series. Journal of Applied Probability 11, 63-71.
Kalliovirta L. (2012) Misspecification tests based on quantile residuals. The Econometrics Journal, 15, 358-393.
Kalliovirta L., Meitz M. and Saikkonen P. 2015. Gaussian Mixture Autoregressive model for univariate time series. Journal of Time Series Analysis, 36(2), 247-266.
Meitz M., Preve D., Saikkonen P. 2023. A mixture autoregressive model based on Student's t-distribution. Communications in Statistics - Theory and Methods, 52(2), 499-515.
Virolainen S. 2022. A mixture autoregressive model based on Gaussian and Student's t-distributions. Studies in Nonlinear Dynamics & Econometrics, 26(4) 559-580.

DEPRECATED, USE quantile_residual_tests INSTEAD! Quantile residual tests for GMAR, StMAR , and G-StMAR models