In this paper we introduce a sequential seasonal unit root testing approach which explicitly addresses its application to high frequency data. The main idea is to see which unit roots at higher frequency data can also be found in temporally aggregated data. We illustrate our procedure to the...

This paper demonstrates that unit root tests can suffer from inflated Type I error rates when data are cointegrated. Results from Monte Carlo simulations show that three commonly used unit root tests - the ADF, Phillips-Perron, and DF-GLS tests - frequently overreject the true null of a unit...

In ESTAR models it is usually difficult to determine parameter estimates, as it can be observed in the literature. We show that the phenomena of getting strongly biased estimators is a consequence of the so-called identification problem, the problem of properly distinguishing the transition...

We demonstrate that short-run real exchange effective rate changes are dominated by nominal effective exchange rate changes, while inflation rates are sticky and contribute little to short-run real exchange rate changes. These observations allow a rather accurate real-time approximation of the...

Ng and Perron (2001) designed a unit root test, which incorporates the properties of DF-GLS and Phillips Perron test. Ng and Perron claim that the test performs exceptionally well especially in the presence of a negative moving average. However, the performance of the test depends heavily on the...

In this paper, we apply the wavelet methods in the popular Augmented Dickey-Fuller and M types of unit root tests. Moreover, we provide an extensive comparison of the wavelet based unit root tests which also includes the recent contributions in the literature. Moreover, we derive the asymptotic...

Determining good parameter estimates in ESTAR models is known to be diffcult. We show that the phenomena of getting strongly biased estimators is a consequence of the so-called identifcation problem, the problem of properly distinguishing the transition function in relation to extreme parameter...

Determining good parameter estimates in ESTAR models is known to be difficult. We showthat the phenomena of getting strongly biased estimators is a consequence of the so-calledidentication problem, the problem of properly distinguishing the transition function in relationto extreme parameter...

We examine the asymptotic behavior of unit root tests against nonlinear alternativesof the exponential smooth transition type if the data is erroneously nonlinearly transformed...