Strong confidence intervals for autoregression
Vladimir Vovk
TL;DR
This paper introduces a game-theoretic approach to compute non-asymptotic confidence intervals for first-order autoregressive models, ensuring high-probability coverage in sequential analysis.
Contribution
It presents a novel sequential confidence interval method for autoregression based on game-theoretic probability, guaranteeing coverage with high probability.
Findings
Provides confidence intervals that always cover the true parameter with high probability
Applicable to sequential data analysis in autoregressive models
Offers a non-asymptotic, high-confidence estimation method
Abstract
In this short note I apply the methodology of game-theoretic probability to calculating non-asymptotic confidence intervals for the coefficient of a simple first order scalar autoregressive model. The most distinctive feature of the proposed procedure is that with high probability it produces confidence intervals that always cover the true parameter value when applied sequentially.
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Taxonomy
TopicsFinancial Risk and Volatility Modeling · Monetary Policy and Economic Impact · Statistical Methods and Inference