A new time-varying model for forecasting long-memory series

12/18/2018
by   Luisa Bisaglia, et al.
0

In this work we propose a new class of long-memory models with time-varying fractional parameter. In particular, the dynamics of the long-memory coefficient, d, is specified through a stochastic recurrence equation driven by the score of the predictive likelihood, as suggested by Creal et al. (2013) and Harvey (2013). We demonstrate the validity of the proposed model by a Monte Carlo experiment and an application to two real time series.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/22/2022

Time-Varying Poisson Autoregression

In this paper we propose a new time-varying econometric model, called Ti...
research
11/21/2020

Minimum Hellinger distance estimates for a periodically time-varying long memory parameter

We consider a purely fractionally deferenced process driven by a periodi...
research
03/19/2018

On Time-Varying Amplitude HGARCH Mode

The HGARCH model allows long-memory impact in volatilities. A new HGARCH...
research
08/05/2020

A long memory time series with a periodic degree of fractional differencing

This article develops a periodic version of a time varying parameter fra...
research
03/13/2018

A General Class of Score-Driven Smoothers

Motivated by the observation that score-driven models can be viewed as a...
research
09/13/2020

Time-varying auto-regressive models for count time-series

Count-valued time series data are routinely collected in many applicatio...
research
05/25/2023

The GNAR-edge model: A network autoregressive model for networks with time-varying edge weights

In economic and financial applications, there is often the need for anal...

Please sign up or login with your details

Forgot password? Click here to reset