Finite sample inference for generic autoregressive models

09/23/2020
by   Hien Duy Nguyen, et al.
0

Autoregressive stationary processes are fundamental modeling tools in time series analysis. To conduct inference for such models usually requires asymptotic limit theorems. We establish finite sample-valid tools for hypothesis testing and confidence set construction in such settings. Further results are established in the always-valid and sequential inference framework.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/06/2023

Uniform Inference for Cointegrated Vector Autoregressive Processes

Uniformly valid inference for cointegrated vector autoregressive process...
research
10/17/2019

Finite sample deviation and variance bounds for first order autoregressive processes

In this paper, we study finite-sample properties of the least squares es...
research
05/29/2021

Randomization Inference of Periodicity in Unequally Spaced Time Series with Application to Exoplanet Detection

The estimation of periodicity is a fundamental task in many scientific a...
research
07/23/2018

Score Permutation Based Finite Sample Inference for Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) Models

A standard model of (conditional) heteroscedasticity, i.e., the phenomen...
research
04/27/2021

Changepoint detection in random coefficient autoregressive models

We propose a family of CUSUM-based statistics to detect the presence of ...
research
06/17/2021

Entrywise limit theorems of eigenvectors and their one-step refinement for sparse random graphs

We establish finite-sample Berry-Esseen theorems for the entrywise limit...
research
02/28/2023

Finite sample inference for empirical Bayesian methods

In recent years, empirical Bayesian (EB) inference has become an attract...

Please sign up or login with your details

Forgot password? Click here to reset