# Bootstrap-based inferential improvements in beta autoregressive moving average model

## TL;DR

This paper improves small sample inference in beta autoregressive moving average models by applying bootstrap bias corrections and strategies, leading to more reliable results in finite samples.

## Contribution

It introduces bootstrap bias correction methods and confidence interval strategies specifically for beta ARMA models, enhancing small sample inference accuracy.

## Key findings

- Bootstrap corrections outperform traditional methods in simulations
- Finite sample inference is significantly improved with bootstrap strategies
- Empirical application demonstrates practical effectiveness

## Abstract

We consider the issue of performing accurate small sample inference in beta autoregressive moving average model, which is useful for modeling and forecasting continuous variables that assumes values in the interval $(0,1)$. The inferences based on conditional maximum likelihood estimation have good asymptotic properties, but their performances in small samples may be poor. This way, we propose bootstrap bias corrections of the point estimators and different bootstrap strategies for confidence interval improvements. Our Monte Carlo simulations show that finite sample inference based on bootstrap corrections is much more reliable than the usual inferences. We also presented an empirical application.