Discrete-response state space models with conditional heteroscedasticity: An application to forecasting the federal funds rate target

Stefanos Dimitrakopoulos and Dipak K. Dey

Economics Letters, 2017, vol. 154, issue C, 20-23

Abstract: We propose a state space mixed model with stochastic volatility for ordinal-response time series data. For parameter estimation, we design an efficient Markov chain Monte Carlo algorithm. We illustrate our method with an empirical study on the federal funds rate target. The proposed model provides better forecasts than alternative specifications.

Keywords: Conditional heteroscedasticity; Markov chain Monte Carlo; Discrete responses; State-space model (search for similar items in EconPapers)
JEL-codes: C11 C15 C22 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:154:y:2017:i:c:p:20-23

DOI: 10.1016/j.econlet.2017.02.012

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