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

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  • Dimitrakopoulos, Stefanos
  • Dey, Dipak K.

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.

Suggested Citation

  • Dimitrakopoulos, Stefanos & Dey, Dipak K., 2017. "Discrete-response state space models with conditional heteroscedasticity: An application to forecasting the federal funds rate target," Economics Letters, Elsevier, vol. 154(C), pages 20-23.
    • Handle: RePEc:eee:ecolet:v:154:y:2017:i:c:p:20-23
    DOI: 10.1016/j.econlet.2017.02.012
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    References listed on IDEAS

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    Cited by:

    1. Dimitrakopoulos, Stefanos & Tsionas, Mike, 2019. "Ordinal-response GARCH models for transaction data: A forecasting exercise," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1273-1287.
    2. Dimitrakopoulos, Stefanos & Tsionas, Mike G. & Aknouche, Abdelhakim, 2020. "Ordinal-response models for irregularly spaced transactions: A forecasting exercise," MPRA Paper 103250, University Library of Munich, Germany, revised 01 Oct 2020.

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    More about this item

    Keywords

    Conditional heteroscedasticity; Markov chain Monte Carlo; Discrete responses; State-space model;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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