Kpss test eviews manual#
Kpss test eviews series#

The differenced revenue and GDP series are stationary (tests not shown), so both series are I(1), and GDP is possibly trend-stationary. # Phillips-Ouliaris demeaned = -33.219, Truncation lag parameter = 0, p-value = 0.01 # Cointegrated: We reject the null of no cointegration # KPSS Trend = 0.065567, Truncation lag parameter = 3, p-value = 0.1 # ADF test fails to reject the null of non-stationarity at 5% level # KPSS Trend = 0.24371, Truncation lag parameter = 3, p-value = 0.01 # Testing log-transformed series for stationarity: Revenue is clearly non-stationary For simplicity I will follow the 2-Step approach of Engele & Granger here, although I note that the more sophisticated Johannsen procedure is available in the urca package. This requires a battery of tests to examine the properties of the data before specifying a model 2.

The summary also shows that we have 23 years of quarterly revenue data but only 11 years of quarterly GDP data.ДЄn ECM is only well specified if both series are integrated of the same order and cointegrated.

# Within 11.25 0.0271 0.17 -0.3771 0.4951ДЇor log revenue, the standard deviation between quarters is actually slightly higher than the within-quarter standard deviation, indicating a strong seasonal component. Tfmv(data, 3:4, D) %>% qsu(pid = lrev + lgdp ~ Quarter) Summarizing the log-differenced using a function designed for panel data allows us to assess the extent of seasonality relative to overall variation. The data was not seasonally adjusted as revenue and GDP exhibit similar seasonal patterns. Main = 'Revenue and GDP in Quarterly Log-Differences', Plot(na_omit(D(X)), legend.loc = 'topleft', Main = 'Domestic Revenue and GDP (in Logs)', Settfm(data, Date = as.Date(paste(Year, unattrib(Quarter) * 3L, '1', sep = '/'))) Library(xts) # Extensible time-series + pretty plots Library(jtools) # Enhanced regression summary Library(sandwich) # Robust standard errors Library(collapse) # Data transformation and time series operators First we examine the data, which is in local currency and was transformed using the natural logarithm. This blog post will briefly demonstrate the specification of an ECM to forecast the tax revenue of a developing economy 1. The correct way to deal with cointegrated variables is to specify and Error Correction Model (ECM). Since however both revenue and GDP are typically non-stationary series, this relationship often takes the form of cointegration. In the absence of large abrupt shifts in the tax base, domestic revenue can be assumed to have a linear relationship with GDP. The simple regression approach regresses tax revenue on its own lags and GDP with some lags. Approach (iii) typically results in the most accurate short-term forecasts.

Kpss test eviews manual#

The IMF Financial Programming Manual reviews 3 of them: (i) the effective tax rate approach (ii) the elasticity approach and (iii) the regression approach. There are several ways to forecast tax revenue.