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help for ^kpss^ (STB-57: sts15)
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Kwiatkowski-Phillips-Schmidt-Shin test for stationarity
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^kpss^ varname [^if^ exp] [^in^ range] [^,^ ^m^axlag^(^#^)^ ^notrend^ ]
^kpss^ is for use with time-series data. You must ^tsset^ your data before
using ^kpss^; see help @tsset@.
varname may contain time-series operators; see help @varlist@.
Description
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^kpss^ performs the Kwiatkowski, Phillips, Schmidt, Shin (KPSS, 1992)
test for stationarity of a time series. This test differs from those in
common use (such as ^dfuller^ and ^pperron^) by having a null hypothesis
of stationarity. The test may be conducted under the null of either trend
stationarity (the default) or level stationarity. Inference from this test
is complementary to that derived from those based on the Dickey-Fuller
distribution (such as ^dfgls^, ^dfuller^ and ^pperron^). The KPSS test
is often used in conjunction with those tests to investigate the possibility
that a series is fractionally integrated (that is, neither I(1) nor I(0)):
see Lee and Schmidt (1996). As such, it is complementary to ^gphudak^.
The maximum lag order for the test is by default calculated from the sample
size using a rule provided by Schwert (1989) using c=12 and d=4 in his
terminology. The maximum lag order may also be provided with the ^maxlag^
option and may be zero. If the maximum lag order is at least one, the test
is performed for each lag, with the sample size held constant over lags
at the maximum available sample.
Approximate critical values for the KPSS test are taken from KPSS, 1992.
The KPSS test statistic for each lag is placed in the return array.
Options
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^maxlag(^#^)^ specifies the maximum lag order to be used in calculating the
test. If omitted, the maximum lag order is calculated as described above.
^notrend^ indicates that level stationarity, rather than trend stationarity,
is the null hypothesis.
Examples
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. ^kpss gnp^
. ^kpss D.gnp, maxlag(12) notrend^
. ^kpss gnp if tin(1973q1,1995q4)^
References
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Kwiatkowski, D., Phillips, P.C.B., Schmidt, P. and Y. Shin. Testing the
null hypothesis of stationarity against the alternative of a unit root:
How sure are we that economic time series have a unit root? Journal of
Econometrics, 54, 1992, 159-178.
Lee, D. and P. Schmidt. On the power of the KPSS test of stationarity
against fractionally-integrated alternatives. Journal of Econometrics,
73, 1996, 285-302.
Schwert, G.W. Tests for Unit Roots: A Monte Carlo Investigation. Journal of
Business and Economic Statistics, 7, 1989, 147-160.
Acknowledgements
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A version of this code written in the RATS programming language by
John Barkoulas served as a guide for the development of the Stata code.
Author
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Christopher F Baum, Boston College, USA
baum@@bc.edu
Also see
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On-line: help for @dfuller@, @pperron@, @time@, @tsset@, @dfgls@ (if
installed), @gphudak@ (if installed)