Forecasting Observables with Particle Filters: Any Filter Will Do!

Patrick Leung, Catherine Forbes, Gael Martin () and Brendan McCabe

No 22/19, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: We investigate the impact of filter choice on forecast accuracy in state space models. The filters are used both to estimate the posterior distribution of the parameters, via a particle marginal Metropolis-Hastings (PMMH) algorithm, and to produce draws from the filtered distribution of the final state. Multiple filters are entertained, including two new data-driven methods. Simulation exercises are used to document the performance of each PMMH algorithm, in terms of computation time and the efficiency of the chain. We then produce the forecast distributions for the one-stepahead value of the observed variable, using a fixed number of particles and Markov chain draws. Despite distinct differences in efficiency, the filters yield virtually identical forecasting accuracy, with this result holding under both correct and incorrect specification of the model. This invariance of forecast performance to the specification of the filter also characterizes an empirical analysis of S&P500 daily returns.

Keywords: Bayesian prediction; particle MCMC; non-Gaussian time series; state space models; unbiased likelihood estimation; sequential Monte Carlo. (search for similar items in EconPapers)
JEL-codes: C11 C22 C58 (search for similar items in EconPapers)
Pages: 34
Date: 2019
New Economics Papers: this item is included in nep-cmp, nep-ecm, nep-ets, nep-for and nep-ore
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