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6.2.3  Goods and services tax (GST)

GST is a broadly-based value-added tax applied to most goods and services consumed within New Zealand. Forecasting models for GST have ranged from using many macroeconomic drivers to just a few. The main macroeconomic driver used in all the GST forecasts is nominal consumption, so this was the variable used in the benchmark model and the associated forecast error decompositions.

Figure 5 – Goods and services tax (GST) and nominal consumption

The top plots show GST (solid black) and its forecast (dashed black), scaled nominal consumption (solid red) and its forecast (dashed red), the associated tax ratio (solid blue) and its forecast (dashed blue), and the tax ratio trend (solid green). The remaining time series plots and boxplots show the percentage forecast errors due to forecasting GST (black), nominal consumption (red), tax ratio (blue), tax ratio trend (green) and residual error (cyan).
Figure 5: Goods and services tax (GST) and nominal consumption.
Source:The Treasury

The decompositions (21) and (22) are shown in Figure 5 together with GST and its forecast, nominal consumption and its forecast, the associated tax ratio and its forecast, and the tax ratio trend. Forecasts of GST and nominal consumption have been in relatively close agreement with actual outcomes up until 2001, but both have underestimated actual outcomes from 2001. The tax ratio trend provides a good fit to the tax ratios, despite their rapid increase over the 2002-2004 period.

Since GST is a flat-rate tax, we would expect the tax ratio and its trend to be relatively constant and, if nominal consumption were a good proxy for the GST tax base, this constant should be 11.1%. However, as noted above, after maintaining a value around 11% until 2001, the tax ratios have moved from 11.1% in 2002 to 11.6% in 2005. Evidently, nominal consumption is an imperfect proxy for the GST tax base. This is because some components of nominal consumption are not subject to GST, such as housing rentals, and there are items that are subject to GST that do not form part of nominal consumption, such as new dwelling construction costs. The latter is a likely candidate for the up-swing in the tax ratios seen here. Residential investment has experienced something of a boom in New Zealand over the last few years, which has increased the GST take, but has not increased the nominal consumption base as measured by Statistics New Zealand.

The boxplots in Figure 5 show that, for decomposition (21), the forecast errors for nominal consumption are significantly biased downwards and it is these that are contributing to the same significant bias of the GST forecast errors. The tax ratio percentage forecast errors are not biased, but are more volatile than the percentage forecast errors for nominal consumption. For decomposition (22), the boxplots show no signs of bias, but the percentage forecast errors due to forecasting the tax ratio trend are approximately twice as volatile as those of the non-systematic error component, implying that there are forecast gains to be had, even with the simple benchmark model used here. These observations are supported by the summary statistics given in Table 6.

Table 6 – Summary statistics for GST revenue percentage forecast errors

GST revenue Yt and its components: nominal consumption Xt, associated tax ratio Rt, tax ratio trend αt and residual et

  Yt Xt Rt αt et
Bias -1.70 -1.61 -0.09 -0.22 0.13
Standard deviation 2.22 1.51 1.93 1.71 0.84
RMSE 2.72 2.16 1.84 1.65 0.81
Lag one autocorrelation 0.16 0.20 0.29 0.35 -0.12

Source: The Treasury

The lag one autocorrelations of the various components shown in Table 6 are not significantly different from zero. In addition, the components of each decomposition (21) and (22) showed no evidence of significant cross-correlation at all lags.

6.2.4  Corporate tax

This is the sum of net company income tax, non-resident withholding tax (NRWT) and foreign dividend withholding payments (FDWP). We include these other two withholding taxes in the definition of corporate tax as companies will typically get a credit towards their income tax for at least some of any NRWT or FDWP paid. A variety of forecasting models has been used over the years to forecast corporate tax. The macroeconomic variable at the heart of all of these is operating surplus and so this was the variable used in the benchmark model and the associated forecast error decompositions.


Figure 6 – Corporate tax and operating surplus
The top plots show corporate tax (solid black) and its forecast (dashed black), scaled operating surplus (solid red) and its forecast (dashed red), the associated tax ratio (solid blue) and its forecast (dashed blue), and the tax ratio trend (solid green). The remaining time series plots and boxplots show the percentage forecast errors due to forecasting corporate tax (black), operating surplus (red), tax ratio (blue), tax ratio trend (green) and residual error (cyan).
Figure 6: Corporate tax and operating surplus.
Source: The Treasury

The decompositions (21) and (22) are shown in Figure 6 together with corporate tax and its forecast, operating surplus and its forecast, the associated tax ratio and its forecast, and the tax ratio trend. Forecasts of corporate tax have tended to overestimate actual outcomes in the 1995-2000 period and underestimate actual outcomes in the 2001-2005 period. Forecasts of operating surplus have tended to underestimate actual outcomes over the entire 1995-2005 period. The tax ratio trend provides a good fit to the tax ratios, despite their rapid increase over the 2002-2005 period. Note that the tax ratio forecasts appear to be overestimating actual outcomes when the tax ratio trends downwards, and underestimating actual outcomes when it trends upwards.

Company income tax, NRWT and FDWP are all levied at fixed tax rates, and so we might expect the tax ratio and its trend to be relatively constant. The resulting average tax rate can fluctuate owing to the utilisation of tax losses, the claiming of tax credits and the timing of revenue recognition, all of which can vary a great deal from year to year. However, none of these adequately explains the upward trend in the tax ratio and its trend in recent years. As in the case of GST, the tax-base proxy adopted (operating surplus) appears to be less than perfect. One possible explanation might be that investment returns, while taxable, do not form part of the economic measure of operating surplus.

The boxplots in Figure 6 show that, for decomposition (21), the forecast errors for operating surplus are significantly biased downwards and the tax ratio forecast errors are, if anything, biased the other way. As a consequence, corporate tax percentage forecast errors show no evidence of bias. However the volatility of the tax ratio percentage forecast errors is considerably greater than (more than twice) that of the operating surplus percentage forecast errors, and this is the primary source of the considerable volatility present in the corporate tax percentage forecast errors. The decomposition (22) of the tax ratio percentage forecast errors also highlights the inaccuracy of the tax ratio forecasts as forecasts of the tax ratio trend by comparison to the volatility of the non-systematic error component. Better tax ratio forecasts are needed and could be achieved, even with the simple benchmark model used here. These observations are supported by the summary statistics given in Table 7.

Table 7 – Summary statistics for corporate tax revenue percentage forecast errors

Corporate tax revenue Yt and its components: operating surplus Xt, associated tax ratio Rt, tax ratio trend αt and residual et

  Yt Xt Rt αt et
Bias -0.73 -3.23 2.51 2.76 -0.25
Standard deviation 12.10 4.60 12.97 11.98 4.79
RMSE 11.56 5.45 12.62 11.75 4.58
Lag one autocorrelation 0.49 0.18 0.47 0.75 -0.43

Source: The Treasury

Of the lag one autocorrelations given in Table 7, only that for the percentage forecast errors due to forecasting the tax ratio trend αt is significantly different from zero and this reflects the pattern of the tax ratio forecasts discussed earlier. In addition, the components of each decomposition (21) and (22) showed no evidence of significant cross-correlation at all lags.

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