LeybourneMcCabe stationarity test
h
= lmctest(y
)h
= lmctest(y
,'ParameterName
',ParameterValue
)
[h
,pValue
]
= lmctest(...)
[h
,pValue
,stat
]
= lmctest(...)
[h
,pValue
,stat
,cValue
]
= lmctest(...)
[h
,pValue
,stat
,cValue
,reg1
]
= lmctest(...)
[h
,pValue
,stat
,cValue
,reg1
,reg2
]
= lmctest(...)
assesses
the null hypothesis that
a univariate time series h
= lmctest(y
)y
is a trend stationary
AR(p) process, against the alternative that it
is a nonstationary ARIMA(p,1,1) process.
accepts
one or more commaseparated parameter name/value pairs. Specify h
= lmctest(y
,'ParameterName
',ParameterValue
)ParameterName
inside
single quotes. Perform multiple tests by passing a vector value for
any parameter. Multiple tests yield vector results.
[
returns pvalues of the
test statistics.h
,pValue
]
= lmctest(...)
[
returns the test statistics.h
,pValue
,stat
]
= lmctest(...)
[
returns critical values for the tests.h
,pValue
,stat
,cValue
]
= lmctest(...)
[
returns a structure of regression statistics
from the maximum likelihood estimation of the reducedform model.h
,pValue
,stat
,cValue
,reg1
]
= lmctest(...)
[
returns a structure of regression statistics
from the OLS estimation of the filtered data on a linear trend.h
,pValue
,stat
,cValue
,reg1
,reg2
]
= lmctest(...)

Vector of timeseries data. The last element is the most recent observation. The test ignores NaN values, which indicate missing entries. 

Scalar or vector of nominal significance levels for the tests. Set values between 0.01 and 0.1. Default: 

Scalar or vector of nonnegative integers indicating the number For best results, give a suitable value for Default: 

Scalar or vector of Boolean values indicating whether or not
to include the deterministic trend term Determine the value of Default: 

String or cell vector of strings indicating which estimate of the variance $${\sigma}_{1}^{2}$$ to use in computing the test
statistic. Values are Default: 

Vector of Boolean decisions for the tests, with length equal
to the number of tests. Values of 

Vector of pvalues of the test statistics, with length equal to the number of tests. Values are righttail probabilities. 

Vector of test statistics, with length equal to the number of tests. For details, see Test Statistics. 

Vector of critical values for the tests, with length equal to the number of tests. Values are for righttail probabilities. 

Structure of regression statistics from the maximum likelihood estimation of the reducedform model. The structure is described in Regression Statistics Structure. 

Structure of regression statistics The structure is described in Regression Statistics Structure. 
[1] Caner, M., and L. Kilian. "Size Distortions of Tests of the Null Hypothesis of Stationarity: Evidence and Implications for the PPP Debate." Journal of International Money and Finance. Vol. 20, 2001, pp. 639–657.
[2] Kwiatkowski, D., P. C. B. Phillips, P. Schmidt and Y. Shin. "Testing the Null Hypothesis of Stationarity against the Alternative of a Unit Root." Journal of Econometrics. Vol. 54, 1992, pp. 159–178.
[3] Leybourne, S. J., and B. P. M. McCabe. "A Consistent Test for a Unit Root." Journal of Business and Economic Statistics. Vol. 12, 1994, pp. 157–166.
[4] Leybourne, S. J., and B. P. M. McCabe. "Modified Stationarity Tests with DataDependent ModelSelection Rules." Journal of Business and Economic Statistics. Vol. 17, 1999, pp. 264–270.
[5] Schwert, G. W. "Effects of Model Specification on Tests for Unit Roots in Macroeconomic Data." Journal of Monetary Economics. Vol. 20, 1987, pp. 73–103.
adftest
 kpsstest
 pptest
 vratiotest