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The most striking evidence of non-stationarity is shown in Figure 3.
GWR is efficient in handling spatially varying relationships between variables and has been widely employed in situations where non-stationarity exists.
Testing for Non-stationarity using the Unit Root Test
The results allow the rejection of the non-stationarity hypothesis for the entire period (1862-2013), and the two sub-periods.
Non-stationarity can be formally identified through testing for the presence of unit roots.
The non-parametric Phillips-Perron (PP) test and parametric Augmented Dickey-Fuller (ADF) test rejected the non-stationarity hypothesis for index returns at the level for all daily, weekly and monthly data.
The speech signal is by nature a non-stationary process, which expresses its non-stationarity by changing parameters during the time.
This enables us to explore both short-run dynamics and long-run equilibrium relationships among the variables of interest, accounting for non-stationarity in the data and heterogeneity across countries in their short-run dynamic relationships.
Only when the non-stationarity of the variables involved in estimation is confirmed, we proceed with the panel cointegration analysis.
The null hypothesis is that there is unit root non-stationarity in the time series data.
Overall, this evidence suggests non-stationarity and the presence of a unit root in all three series in all countries examined.
The t-statistics for all series are greater (less negative) than the critical values in levels leading to the acceptance of the null hypothesis of non-stationarity.

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