Kyle Macaskill
Semi-Parametric Time-Series Models: Computation and Simulation
The project will make developments to extending the semi-parametric time-series modelling framework proposed in Fung & Huang (2015) to handle INGARCH (Integer-valued Generalized Autoregressive Conditional Heteroscedastic) models. This will primarily focus on INGARCH(1,1) models on count time-series data. Firstly, extensive background reading will be performed before tackling computation, estimation and simulation of semi-parametric time-series models from various distributions. The project will then move on to applying the semi-parametric model to common examples and applications in INGARCH time-series literature, comparing with parametric estimations given by various authors. Finally, the project will explore relevant software to handle a general INGARCH(p,q) models within the semi-parametric framework.
Kyle Macaskill
The University of Queensland
Kyle has completed his third year of a dual degree of a Bachelor of Mathematics (Statistics) / Computer Science (Machine Learning) at the University of Queensland. Kyle has a passion for mathematical statistics, mathematical analysis and probability theory. In particular, he has taken a strong interest in applications of mathematics in finance, sports and health, as well as combining skills in both mathematics and programming to solve real-world problems. The AMSI Vacation Research Scholarship will provide Kyle with an excellent experience to develop his communication skills and apply concepts for his future academic studies.
You may be interested in
Joshua Crawford
Inverse Scattering in the Recovery of a Single Concave and a Finite Union of Disjoint Convex Obstacles
