Stochastic volatility models with ARMA innovations: An application to G7 inflation forecasts

Bo Zhang, Joshua Chan and Jamie Cross

International Journal of Forecasting, 2020, vol. 36, issue 4, 1318-1328

Abstract: We introduce a new class of stochastic volatility models with autoregressive moving average (ARMA) innovations. The conditional mean process has a flexible form that can accommodate both a state space representation and a conventional dynamic regression. The ARMA component introduces serial dependence, which results in standard Kalman filter techniques not being directly applicable. To overcome this hurdle, we develop an efficient posterior simulator that builds on recently developed precision-based algorithms. We assess the usefulness of these new models in an inflation forecasting exercise across all G7 economies. We find that the new models generally provide competitive point and density forecasts compared to standard benchmarks, and are especially useful for Canada, France, Italy, and the U.S.

Keywords: Autoregressive moving average errors; Stochastic volatility; Inflation forecast; State space models; Unobserved components model (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (11)

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Working Paper: Stochastic volatility models with ARMA innovations: An application to G7 inflation forecasts (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:36:y:2020:i:4:p:1318-1328

DOI: 10.1016/j.ijforecast.2020.01.004

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