This paper extends the KPSS test to the setting of functional time series. We develop the form of the test statistic, and propose two testing procedures: Monte Carlo and asymptotic. The limit distributions are derived, the procedures are algo- rithmically described and illustrated by an application to yield curves and a simulation study. The KPSS test As an alternative to the Dickey-Fuller style tests for stationarity, we may consider the KPSS test of Kwiatkowski, Phillips, Schmidt and Shin (J. Econometrics, 1992). This test (and those derived from it) have the more "natural" null hypothesis of stationarity (I(0)), where a rejection indicates non-stationarity (I(1) or I(d)).
(PDF) The KPSS test with outliers
The KPSS test, short for, Kwiatkowski-Phillips-Schmidt-Shin (KPSS), is a type of Unit root test that tests for the stationarity of a given series around a deterministic trend. In other words, the test is somewhat similar in spirit with the ADF test. A common misconception, however, is that it can be used interchangeably with the ADF test. The KPSS Test has been developed to complement unit root tests as the last have low power with respect to near unit-root and long-run trend processes. KPSS Test Specification Unlike unit root tests, Kwiatkowski et al. provide straightforward test of the null hypothesis of trend stationarity against the alternative of a unit root. similar to the KPSS statistic must be normalized by the long run variance rather than by the sample variance. We develop extensions of the KPSS test to time series of curves, which we call functional time series (FTS). Many nancial data sets form FTS. The best known and most extensively studied data of this form are yield curves. To test for a using the ADF test, one estimates the following model: 1 −1. first differences 2 ᄏ䅫+ ∑ ii=1 approximate the ARMA dynamics of the time series, β0 is a constant, and t is a trend. If the series has a unit root, β1 = 0 and hypothesis that β1 = 0 given n lagged first differences. =1. The ADF test is a test of the.
[PDF] The Seasonal KPSS Test Examining Possible Applications with
KPSS is another test for checking the stationarity of a time series. The null and alternate hypothesis for the KPSS test are opposite that of the ADF test. Null Hypothesis: The process is trend stationary. Alternate Hypothesis: The series has a unit root (series is not stationary). A function is created to carry out the KPSS test on a time series. In econometrics, Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests are used for testing a null hypothesis that an observable time series is stationary around a deterministic trend (i.e. trend-stationary) against the alternative of a unit root. [1] The KPSS test is now widely used in empirical work to test trend stationarity and works as a complement to standard unit root tests in analyzing the properties of time series data. In this study, we examine bootstrap methods to construct a generalized KPSS test for functional time series. Bootstrap-based functional testing provides an intuitive and efficient estimation of.
(PDF) The Power of the KPSSTest for Cointegration when Residuals are
We propose automatic generalizations of the KPSS‐test for the null hypothesis of stationarity of a univariate time series. We can use these tests for the null hypotheses of trend stationarity, level stationarity and zero mean stationarity. We introduce the asymptotic null distributions and we determine consistency against relevant nonstationary alternatives. The test proposed in Kwiatkowski et al. (1992), often referred to as the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, has been used most extensively to test for stationarity of a time series. It relies on cumulation of squared partial sums of the demeaned and/or detrended series with a correction for autocorrelation using a.
What is the KPSS Test? The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test figures out if a time series is stationary around a mean or linear trend, or is non-stationary due to a unit root. A stationary time series is one where statistical properties — like the mean and variance — are constant over time. Abstract. In this paper, we generalize the KPSS-type test to allow for two structural breaks. Seven models have been defined depending on the way that structural breaks affect the time series.
Results of KPSS test. Download Table
Kwiatkowski et al. (1992, Journal of Econometrics 54: 159-178) introduced the Kwiatkowski, Phillips, Schmidt, and Shin test, in which the null hypothesis is that the series is stationary, to deal with this problem. One shortcoming of the presently available Kwiatkowski, Phillips, Schmidt, and Shin test in Stata is that it uses asymptotic. It has been over twenty years since Kwiatkowski et al. provided a test of whether a series is stationary (henceforth the KPSS test), and as is the case with unit root tests, while the asymptotic properties of the test are well defined, its behavior in small samples is less-well understood.Sephton estimated response surfaces to construct small sample critical values of the test, following.
