MVWNtest | R Documentation |

`MVWNtest`

performs multivariate tests for white noise.
It performs both the Ljung-Box Q-test and the LM-test on individual series
for a sequence of lag lengths.
`summary.MVWN_stats`

prints a summary of these statistics to screen.

MVWNtest(x, maxlag, printResults)

`x` |
A matrix of variables to be included in the system, typically model residuals. |

`maxlag` |
The number of lags for serial correlation tests. |

`printResults` |
An indicator to print results to screen. |

An S3 object of type `MVWN_stats`

containing the test results,
including the following parameters:

`Q`

A 1xp vector of Q statistics for individual series.

`pvQ`

A 1xp vector of P-values for Q-test on individual series.

`LM`

A 1xp vector of LM statistics for individual series.

`pvLM`

A 1xp vector of P-values for LM-test on individual series.

`mvQ`

A multivariate Q statistic.

`pvMVQ`

A p-value for multivariate Q-statistic using

`p^2*maxlag`

degrees of freedom.`maxlag`

The number of lags for serial correlation tests.

`p`

The number of variables in the system.

The LM test is consistent for heteroskedastic series; the Q-test is not.

`FCVARoptions`

to set default estimation options.
`FCVARestn`

produces the residuals intended for this test.
`LagSelect`

uses this test as part of the lag order selection process.
`summary.MVWN_stats`

prints a summary of the `MVWN_stats`

statistics to screen.

Other FCVAR postestimation functions:
`FCVARboot()`

,
`FCVARhypoTest()`

,
`GetCharPolyRoots()`

,
`plot.FCVAR_roots()`

,
`summary.FCVAR_roots()`

,
`summary.MVWN_stats()`

opt <- FCVARoptions() opt$gridSearch <- 0 # Disable grid search in optimization. opt$dbMin <- c(0.01, 0.01) # Set lower bound for d,b. opt$dbMax <- c(2.00, 2.00) # Set upper bound for d,b. opt$constrained <- 0 # Impose restriction dbMax >= d >= b >= dbMin ? 1 <- yes, 0 <- no. x <- votingJNP2014[, c("lib", "ir_can", "un_can")] results <- FCVARestn(x, k = 2, r = 1, opt) MVWNtest_stats <- MVWNtest(x = results$Residuals, maxlag = 12, printResults = 1) set.seed(27) WN <- stats::rnorm(100) RW <- cumsum(stats::rnorm(100)) MVWN_x <- as.matrix(data.frame(WN = WN, RW = RW)) MVWNtest_stats <- MVWNtest(x = MVWN_x, maxlag = 10, printResults = 1)

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