# Simultaneous Inference of a Partially Linear Model in Time Series

arXiv: https://arxiv.org/abs/2212.10359 · HTML: https://mlsanity.com/paper/2212.10359/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2022.

## Built on by (most cited first, 1 of 1)

- [Inference in Partially Linear Models under Dependent Data with Deep Neural Networks](https://mlsanity.com/paper/2410.22574/) (2024)

## Abstract (arXiv)

We introduce a new methodology to conduct simultaneous inference of the nonparametric component in partially linear time series regression models where the nonparametric part is a multivariate unknown function. In particular, we construct a simultaneous confidence region (SCR) for the multivariate 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 nonlinear autoregressive processes. We demonstrate the validity of our proposed methodology by examining the finite-sample performance in the simulation study. Finally, an application in time series, the forward premium regression, is presented, where we construct the SCR for the foreign exchange risk premium from the exchange rate and macroeconomic data.