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Mixed frequency models: Bayesian approaches to estimation and prediction

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  • Rodriguez, Abel
  • Puggioni, Gavino

Abstract

We describe Bayesian models for economic and financial time series that use regressors sampled at higher frequencies than the outcome of interest. The models are developed within the framework of dynamic linear models, which provides a high level of flexibility and allows direct interpretation of the results. The problem of the collinearity of intraperiod observations is solved using model selection and model averaging approaches. Bayesian approaches to model selection automatically adjust for multiple comparisons, while predictions based on model averaging allow us to account for both model and parameter uncertainty when predicting future observations. A novel aspect of the models presented here is the introduction of new formulations for the prior distribution on the model space that allow us to favor sparse models where the significant coefficients cluster on adjacent lags of the high frequency predictor. We illustrate our approach by predicting the gross national product of the United States using the term structure of interest rates.

Suggested Citation

  • Rodriguez, Abel & Puggioni, Gavino, 2010. "Mixed frequency models: Bayesian approaches to estimation and prediction," International Journal of Forecasting, Elsevier, vol. 26(2), pages 293-311, April.
  • Handle: RePEc:eee:intfor:v:26:y::i:2:p:293-311
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    Cited by:

    1. Frank Schorfheide & Dongho Song, 2015. "Real-Time Forecasting With a Mixed-Frequency VAR," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 366-380, July.
    2. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Realtime nowcasting with a Bayesian mixed frequency model with stochastic volatility," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 837-862, October.
    3. Ferrara, Laurent & Mogliani, Matteo & Sahuc, Jean-Guillaume, 2022. "High-frequency monitoring of growth at risk," International Journal of Forecasting, Elsevier, vol. 38(2), pages 582-595.
    4. Duarte, Cláudia & Rodrigues, Paulo M.M. & Rua, António, 2017. "A mixed frequency approach to the forecasting of private consumption with ATM/POS data," International Journal of Forecasting, Elsevier, vol. 33(1), pages 61-75.
    5. Foroni, Claudia & Marcellino, Massimiliano & Schumacher, Christian, 2011. "U-MIDAS: MIDAS regressions with unrestricted lag polynomials," Discussion Paper Series 1: Economic Studies 2011,35, Deutsche Bundesbank.
    6. Ankargren Sebastian & Unosson Måns & Yang Yukai, 2020. "A Flexible Mixed-Frequency Vector Autoregression with a Steady-State Prior," Journal of Time Series Econometrics, De Gruyter, vol. 12(2), pages 1-41, July.
    7. Marcellino, Massimiliano & Foroni, Claudia & Casarin, Roberto & Ravazzolo, Francesco, 2017. "Uncertainty Through the Lenses of A Mixed-Frequency Bayesian Panel Markov Switching Model," CEPR Discussion Papers 12339, C.E.P.R. Discussion Papers.
    8. Ghysels, Eric, 2016. "Macroeconomics and the reality of mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 294-314.
    9. Bhaghoe, S. & Ooft, G. & Franses, Ph.H.B.F., 2019. "Estimates of quarterly GDP growth using MIDAS regressions," Econometric Institute Research Papers EI2019-29, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    10. Timmermann, Allan & Pettenuzzo, Davide & Valkanov, Rossen, 2014. "A Bayesian MIDAS Approach to Modeling First and Second Moment Dynamics," CEPR Discussion Papers 10160, C.E.P.R. Discussion Papers.
    11. Roberto Casarin & Claudia Foroni & Massimiliano Marcellino & Francesco Ravazzolo, 2016. "Uncertainty Through the Lenses of A Mixed-Frequency Bayesian Panel Markov Switching Model," Working Papers 585, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    12. Pettenuzzo, Davide & Timmermann, Allan & Valkanov, Rossen, 2016. "A MIDAS approach to modeling first and second moment dynamics," Journal of Econometrics, Elsevier, vol. 193(2), pages 315-334.
    13. Ashish Shrestha & Bishal Ghimire & Francisco Gonzalez-Longatt, 2021. "A Bayesian Model to Forecast the Time Series Kinetic Energy Data for a Power System," Energies, MDPI, vol. 14(11), pages 1-15, June.
    14. Cláudia Duarte, 2016. "A Mixed Frequency Approach to Forecast Private Consumption with ATM/POS Data," Working Papers w201601, Banco de Portugal, Economics and Research Department.
    15. Dirk Drechsel & Stefan Neuwirth, 2016. "Taming volatile high frequency data with long lag structure: An optimal filtering approach for forecasting," KOF Working papers 16-407, KOF Swiss Economic Institute, ETH Zurich.
    16. C. Marsilli, 2014. "Variable Selection in Predictive MIDAS Models," Working papers 520, Banque de France.
    17. Marc Francke & Alex Van De Minne, 2022. "Daily appraisal of commercial real estate a new mixed frequency approach," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 50(5), pages 1257-1281, September.

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