Simultaneous Inference of Trend in Partially Linear Time Series
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We introduce a new methodology to conduct simultaneous inference of non-parametric trend in a partially linear time series regression model where the trend is a multivariate unknown function. In particular, we construct a simultaneous confidence region (SCR) for the trend function by extending the high-dimensional Gaussian approximation to dependent processes with continuous index sets. Our results allow for a more general dependence structure compared to previous works and are widely applicable to a variety of linear and non-linear auto-regressive processes. We demonstrate the validity of our proposed inference approach by examining the finite-sample performance in the simulation study. The method is also applied to a real example in time series: the forward premium regression, where we construct the SCR for the foreign exchange risk premium in the exchange rate data.
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