EGARCH, GJR-GARCH, TGARCH, AVGARCH, NGARCH, IGARCH and APARCH Models for Pathogens at Marine Recreational Sites
EGARCH, GJR-GARCH, TGARCH, AVGARCH, NGARCH, IGARCH and APARCH Models for Pathogens at Marine Recreational Sites
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发表时间：
2013
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通讯作者：
G. Ali
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作者：
G. Ali
环境文献缺乏使用的波动性为基础的环境随机过程的模型。为了克服这一缺陷，我们使用EGR 10，IGR 10，TGR 10，GJR-GR 10，NGR 10，AVGR 10和APR 10模型的功能关系的病原体指标时间序列的娱乐活动在海滩。我们使用广义误差，学生t，指数，正态和正态逆高斯分布沿着与他们的偏斜版本来建模病原体指标时间序列。一般来说，浊度、降雨量、露点、河流流量和云量都是重要的变量。EGG、TG、NAG和AVG在输出上没有根本的不同。然而，TGSTAR在捕获病原体指标变量的响应方面可能略优于其余模型。证据支持一些符号偏置效应的冲击。同样程度的干燥天气和潮湿天气条件似乎对病原体有不成比例的影响。Nyblom检验表明估计的参数是稳定的。
The environmental literature lacks the use of volatility based models for environmental stochastic processes. To overcome this deficiency, we use EGARCH, IGARCH, TGARCH, GJR-GARCH, NGARCH, AVGARCH and APARCH models for functional relationships of the pathogen indicators time series for recreational activates at beaches. We use generalized error, Student’s t, exponential, normal and normal inverse Gaussian distributions along with their skewed versions to model pathogen indicator time series. Generally speaking, turbidity, rainfall, dew point, river flow and cloud cover are significant variables. EGARCH, TGARCH, NAGARCH and AVGARCH are not radically different from each other in their output. However, TGARCH could be marginally better than the rest of models in capturing response of the pathogen indicator variable. Evidence supports some sign bias effect of the shocks. Dry weather and wet weather conditions of the same magnitude seem to have disproportionate effect on pathogens. Nyblom test shows that the estimated parameters are stable.