Beta Autoregressive Fractionally Integrated Moving Average Models

TL;DR

This paper introduces beta autoregressive fractionally integrated moving average models for bounded continuous data, incorporating regressors and long-range dependence, with theoretical properties, testing, diagnostics, and empirical validation.

Contribution

It presents a novel class of models combining beta ARFIMA with long-range dependence and develops estimation, testing, and forecasting methods.

Findings

Estimator is consistent and asymptotically normal.

Simulation shows good finite sample performance.

Empirical application demonstrates model usefulness.

Abstract

In this work we introduce the class of beta autoregressive fractionally integrated moving average models for continuous random variables taking values in the continuous unit interval $(0,1)$. The proposed model accommodates a set of regressors and a long-range dependent time series structure. We derive the partial likelihood estimator for the parameters of the proposed model, obtain the associated score vector and Fisher information matrix. We also prove the consistency and asymptotic normality of the estimator under mild conditions. Hypotheses testing, diagnostic tools and forecasting are also proposed. A Monte Carlo simulation is considered to evaluate the finite sample performance of the partial likelihood estimators and to study some of the proposed tests. An empirical application is also presented and discussed.