The TIMESERIES Procedure
- Overview
- Getting Started
-
Syntax
-
Details
Accumulation Missing Value Interpretation Time Series Transformation Time Series Differencing Descriptive Statistics Seasonal Decomposition Correlation Analysis Cross-Correlation Analysis Spectral Density Analysis Singular Spectrum Analysis Data Set Output OUT= Data Set OUTCORR= Data Set OUTCROSSCORR= Data Set OUTDECOMP= Data Set OUTPROCINFO= Data Set OUTSEASON= Data Set OUTSPECTRA= Data Set OUTSSA= Data Set OUTSUM= Data Set OUTTREND= Data Set STATUS Variable Values Printed Output ODS Table Names ODS Graphics Names
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Examples
- References
| CORR Statement |
- CORR statistics < / options > ;
A CORR statement can be used with the TIMESERIES procedure to specify options related to time domain analysis of the accumulated time series. Only one CORR statement is allowed.
The following time domain statistics are available:
- LAG
time lag
- N
number of variance products
- ACOV
autocovariances
- ACF
autocorrelations
- ACFSTD
autocorrelation standard errors
- ACF2STD
an indicator of whether autocorrelations are less than (–1), greater than (1), or within (0) two standard errors of zero
- ACFNORM
normalized autocorrelations
- ACFPROB
autocorrelation probabilities
- ACFLPROB
autocorrelation log probabilities
- PACF
partial autocorrelations
- PACFSTD
partial autocorrelation standard errors
- PACF2STD
an indicator of whether partial autocorrelation are less than (–1), greater than (1), or within (0) two standard errors of zero
- PACFNORM
partial normalized autocorrelations
- PACFPROB
partial autocorrelation probabilities
- PACFLPROB
partial autocorrelation log probabilities
- IACF
inverse autocorrelations
- IACFSTD
inverse autocorrelation standard errors
- IACF2STD
an indicator of whether the inverse autocorrelation is less than (–1), greater than (1) or within (0) two standard errors of zero
- IACFNORM
normalized inverse autocorrelations
- IACFPROB
inverse autocorrelation probabilities
- IACFLPROB
inverse autocorrelation log probabilities
- WN
white noise test statistics
- WNPROB
white noise test probabilities
- WNLPROB
white noise test log probabilities
If none of the correlation statistics are specified, the default is as follows:
corr lag n acov acf acfstd pacf pacfstd iacf iacfstd wn wnprob;
The following options can be specified in the CORR statement following the slash (/):
- NLAG= number
specifies the number of lags to be stored in the OUTCORR= data set or to be plotted. The default is 24 or three times the length of the seasonal cycle, whichever is smaller. The LAGS= option takes precedence over the NLAG= option.
- LAGS= (numlist)
specifies the list of lags to be stored in OUTCORR= data set or to be plotted. The list of lags must be separated by spaces or commas. For example, LAGS=(1,3) specifies the first then third lag.
- NPARMS= number
specifies the number of parameters used in the model that created the residual time series. The number of parameters determines the degrees of freedom associated with the Ljung-Box statistics. The default is NPARMS=0. This option is useful when analyzing the residuals of a time series model with the number of parameters specified by NPARMS=number option.
- TRANSPOSE= NO|YES
specifies which values are recorded as column names in the OUTCORR= data set. TRANSPOSE=YES specifies that lags be recorded as the column names instead of correlation statistics as the column names. The TRANSPOSE=NO option is useful for graphing the correlation results with SAS/GRAPH procedures. The TRANSPOSE=YES option is useful for analyzing the correlation results with other SAS procedures such as the CLUSTER procedure of SAS/STAT or SAS Enterprise Miner software. The default is TRANSPOSE=NO.