4 resultados para Tax revenue forecasting

em WestminsterResearch - UK


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This paper applies Gaussian estimation methods to continuous time models for modelling overseas visitors into the UK. The use of continuous time modelling is widely used in economics and finance but not in tourism forecasting. Using monthly data for 1986–2010, various continuous time models are estimated and compared to autoregressive integrated moving average (ARIMA) and autoregressive fractionally integrated moving average (ARFIMA) models. Dynamic forecasts are obtained over different periods. The empirical results show that the ARIMA model performs very well, but that the constant elasticity of variance (CEV) continuous time model has the lowest root mean squared error (RMSE) over a short period.

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This article analyzes the effects of globalization on implicit tax rates (ITRs) on labor income, capital income, and consumption in the EU15 and Central and Eastern European New Member States (CEE NMS). We find supportive evidence for an increase in the ITR on labor income in the EU15, but no effect on the ITR on capital income. There is evidence of convergence in terms of the ITR on consumption, as countries with higher than average ITR on consumption respond to globalization by decreasing their tax rates. There are important differences among the welfare regimes within the EU15. Social-democratic countries have decreased the tax burden on capital, but increased that on labor due to globalization. Globalization exerts a pressure to increase taxes on labor income in the conservative and liberal regimes as well. Taxes on consumption decrease in response to globalization in the conservative and social-democratic regimes. In the CEE NMS, there is no effect of globalization on the ITR on labor and capital income, but we find a negative impact on the ITR on consumption in the CEE NMS with higher than average ITR on consumption. (JEL H23, H24, H25, F19, F21)

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This paper provides an empirical study to assess the forecasting performance of a wide range of models for predicting volatility and VaR in the Madrid Stock Exchange. The models performance was measured by using different loss functions and criteria. The results show that FIAPARCH processes capture and forecast more accurately the dynamics of IBEX-35 returns volatility. It is also observed that assuming a heavy-tailed distribution does not improve models ability for predicting volatility. However, when the aim is forecasting VaR, we find evidence of that the Student’s t FIAPARCH outperforms the models it nests the lower the target quantile.