Forecasting for quantile self-exciting threshold autoregressive time series models

Yuzhi Cai

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Forecasting Techniques and Applications Statistical Methods and Inference Time Series Analysis and Forecasting

Abstract

Self-exciting threshold autoregressive time series models have been used extensively, and the conditional mean obtained from these models can be used to predict the future value of a random variable. In this paper we consider quantile forecasts of a time series based on the quantile self-exciting threshold autoregressive time series models proposed by Cai and Stander (2008) and present a new forecasting method for them. Simulation studies and application to real time series show that the method works very well.

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References

2004Unit Root Quantile Autoregression Inference 2011Quantile regression for dynamic panel data with fixed effects 1980Threshold autoregression, limit cycles and cyclical data- with discussion

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2010Threshold quantile autoregressive models 2013Quantile Double AR Time Series Models for Financial Returns 2012Semi-parametric quantile estimation for double threshold autoregressive models with heteroskedasticity 2018General Quantile Time Series Regressions for Applications in Population Demographics

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