Contents
30 40 10 20 10 0 1 19 37 55 73 91 109 127 145 163 181 199 217 235 253 271 289 307 325 343 361 379 397 415 433 451 469 487 -20 – A DETERMINISTIC TREND PROCESS 25 30 15 20 0 5 10 -5 1 40 79 118 157 196 235 274 313 352 391 430 469 AUTOREGRESSIVE PROCESSES WITH DIFFERING VALUES OF φ(0, 0.8, 1) 15 Phi=1 5 10Phi=0.8 Phi=0
Why do we need to test for non-stationarity?
STATIONARITY AND UNIT-ROOT TESTING STATIONARITY AND UNIT-ROOT TESTING Why do we need to test for non-stationarity? The stationarity or otherwise of a series can strongly influence its behaviour and properties – e.g. persistence of shocks will be infinite for nonstationary series.
Why are unit root tests called Autoregressive?
Autoregressive unit root tests are based on testing the null hypothesis that φ=1(difference stationary) against the alternative hypothesis that φ<1 (trend stationary). They are called unit root tests because under the null hypothesis the autoregressive polynomial of zt, φ(z)=(1−φz)=0, has a root equal to unity.
Which is root test for null hypothesis ytis i ( 1 )?
The ADF and PP unit root tests are for the null hypothesis that a time series ytis I(1). Stationarity tests, on the other hand, are for the null that ytis I(0). The most commonly used stationarity test, the KPSS test, is due to Kwiatkowski, Phillips, Schmidt and Shin (1992).
What is the definition of weak stationarity?
With autocovariance functions, we can define the covariance stationarity, or weak stationarity. Inthe literature, usually stationarity means weak stationarity, unless otherwise specified. Definition 2(Stationarity or weak stationarity) The time series{Xt, t∈Z}(where Zis theinteger set) is said to be stationary if(I)E(X2
How does stationarity affect the behaviour of a series?
The stationarity or otherwise of a series can strongly influence its behaviour and properties – e.g. persistence of shocks will be infinite for nonstationary series. Spurious regressions: If two variables are trending over time, a regression of one on the other could have a high R2 even if the two are totally unrelated.
Which is the correct formula for non stationarity?
Typically, the explosive case is ignored and we use φ=1to characterize the non-stationarity because φ>1ddoes not dbdescribemany ddata series in economics andd finance.