![kpss test eviews kpss test eviews](https://www.eviews.com/EViews9/overview/panel_tests.png)
Open the series `pe_aus`, the price-earnings ratio for Australia. Also open the Excel file `M3B_Table_unit_root_tests.xlsx`, which you can fill in to summarize your results. Open the EViews workfile `Module3B_data.wf1`, pagefile `PE_ratios`. It appears that in first differences the Malaysian real effective exchange rate is stationary. However, it appears to be stationary in the second half of the time period (after the Asian chrisis). ))Ĭoncluding on the basis on visual inspection of Malaysian time series (real effective exchange rate) it appears that it is nonstationary. Tsdisplay( reer_mys $ reer_mys % >% diff(. # print the results of stationarity testingĪccording to the visual inspection and formal stationarity testing the **South African time series appears to be stationary**. Tsdisplay( my_pe_df $ pe_saf, las = 1, col = "blue ") # Module 3B: Statistical Properties of Time Series Data (2004) support this.# IMF Online Course: Macroeconomic forecasting Both of the latter studies suggest that this choice of kernel leads to more accur ate estimates of the variance of the time series, in finite samples, than do other kernels. The two most common choices for the kernel are the Bartlett kernel, which was employed in the original KPSS paper or the Q uadratic Spectral kernel, as used by Andrews (1991) and Newey and West (1994). Two quantities have to be chosen in order to construct the non-parametric estimator, s 2 - (i) The so-called "kernel"function, k m(j) and (ii) the "bandwidth, or "lag order", m. (where the range of summation is from t = j + 1 to T) is the j'th empirical autocovariance. Where the range of summation is from j = 1 to (T-1) k m (j) is discussed below and
![kpss test eviews kpss test eviews](https://nomanarshed.files.wordpress.com/2014/09/unit-root.jpg)
Then the estimator of the long-run variance will be of the form: Let e t denote the resulting t'th residual (t = 1, 2. (2004).ĭepending on the form of the null hypothesis for the KPSS test, we regress the time-series on either a constant, or a constant and linear trend variable, using OLS. For a really good discussion of this, see Hobijn et al. (1997), indicate that the behaviour/performance of the KPSS test can depend critically on the choice of this estimator. Lots of different estimators for this variance can be used, and studies such as those of Den Haan and Levin This denominator provides a consistent estimate of the long-run variance of the time-series.
![kpss test eviews kpss test eviews](https://i.ytimg.com/vi/UkX1WXfyWOw/maxresdefault.jpg)
The remaining two decisions relate to the construction of the denominator in the formula for the KPSS test statistic itself. Is the null going to be that the time-series is "level stationary", or is it going to be"stationary about a deterministic trend"? Let's suppose that we've decided on this (and hence on the form of the alternative hypothesis). The first of these is associated with the formulation of the null and alternative hypotheses. When the KPSS test (Kwiatkowski et al, 1992) is used, there are basically three choices that need to be made in the construction of the test statistic. Now, let's look at the specific question that's been raised here.