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help for ^wntstmvq^        (STB-60 sts19; help file revised)
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Multivariate Ljung-Box portmanteau (Q) test for white noise
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    ^wntstmvq^ varlist  [^if^ exp] [^in^ range] [^,^ ^L^ags^(^#^)^ ^V^arlags^(^#^)^]


^wntstmvq^ is for use with time-series data.  You must ^tsset^ your data before
using ^wntstmvq^; see help @tsset@. The sample may not contain gaps.

varlist may contain time-series operators; see help @varlist@.


Description
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^wntstmvq^ performs the multivariate Ljung-Box portmanteau (or Q) test for
white noise in a set of timeseries. This test is a generalization of the
univariate Ljung-Box portmanteau (Q) test implemented in Stata as ^wntestq^.
The multivariate form of the test is described in Lutkepohl (1993).

The null hypothesis of the multivariate test is that the autocorrelation
functions of all series in ^varlist^ have no significant elements for lags
1-^lags^. The ^lags^ parameter may be specified by the user. If the series
in ^varlist^ are residuals from a vector autoregression, the ^varlags^ option
should be specified to provide the order of the VAR. Under the null
hypothesis, the test statistic is distributed Chi-squared with degrees of
freedom equal to p^^2 (^lags^-^varlags^) where p is the number of series in
^varlist^. A rejection indicates that at least one series is not white noise.

Although portmanteau statistics are commonly applied in diagnosing time series
models, some caution should be exercised with their use in a cointegration
context. Jacobson (1995, p. 179) states "[O]ne should exercise some care when
using the portmanteau statistic for evaluating the fit of a cointegration 
model. This observation is due to the facts that cointegration implies the
presence of unit roots and an assumption underlying the properties of the
portmanteau statistic is that of a stationary process disqualifying roots on
the unit circle. There is to my knowledge no theoretical result justifying the
use of portmanteau statistics in connection with potential unit roots.
Nevertheless these tests are being used..."


Options
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^lags(^#^)^ specifies the maximum lag order to be used in the test. If not
specified, It takes on a default value of min(N/2-2,40) where N is the number
of observations available.

^varlags(^#^)^ specifies the order of the VAR (vector autoregression) used
to produce the series in ^varlist^. If provided, ^varlags^ must not
exceed ^lags^.


Examples
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	. ^wntstmvq eps1 eps2 eps3 eps4^
	
	. ^wntstmvq eps1 eps2 eps3 eps4, lags(24)^
		
	. ^wntstmvq eps1 eps2 eps3 eps4, lags(24) varlags(4)^


References
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Jacobson, T. 1995. On the Determination of Lag Order in Vector
Autoregressions of Cointegrated Systems. Computational Statistics, 10:177-92.

Lutkepohl, Helmut. Introduction to Multiple Time Series Analysis. 2d ed.
1993. Berlin: Springer-Verlag.

Authors
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Richard Sperling, The Ohio State University, USA
rsperling@@boo.net

Christopher F Baum, Boston College, USA
baum@@bc.edu



Also see
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 Manual:  ^[R] wntestq^
On-line:  help for @wntestq@, @ac@
