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BETA AUTOREGRESSIVE FRACTIONALLY INTEGRATED MOVING AVERAGE MODELS
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.
double bounded time series - long-range dependence - partial likelihood - asymptotic theory - forecast
Séries Temporais e Econometria
Guilherme Pumi, Marcio Valk, Cleber Bisognin, Fábio Mariano Bayer, Taiane Schaedler Prass