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Dr Miguel Gonzalez Belmonte

Formerly at Lancaster University

Miguel Gonzalez Belmonte

Research overview

My research interests lie in application of sequential Monte Carlo and MCMC to analyse time series data by observation-driven and state-space models

Career Details

I came across the Kalman filter and structural time series modelling by STAMP software before I started my M.Sc in Econometrics at York.

My Ph.D in Statistics at Warwick allowed me to explore the application of particle filters to analyse financial time series adopting not necessarily linear and Gaussian state-space models. Static parameter estimation of Cox model, stochastic volatility and stochastic conditional duration was tackled with smooth particle filter and particle MCMC.

My postdoc in Strathclyde evolved around modelling and forecasting EU inflation. I examined shrinkage via Lasso methods and aplication of MCMC to Markov switching models.

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