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Not Just Another Mixed Frequency Paper

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  • Sergio Afonso Lago Alves
  • Angelo Marsiglia Fasolo

Abstract

This paper presents a new algorithm, based on a two-part Gibbs sampler with FFBS method, to recover the joint distribution of missing observations in a mixed-frequency dataset. The new algorithm relaxes most of the constraints usually presented in the literature, namely: (i) it does not require at least one time series to be observed every period; (ii) it provides an easy way to add linear restrictions based on the state space representation of the VAR; (iii) it does not require regularly-spaced time series at lower frequencies; and (iv) it avoids degeneration problems arising when states, or linear combination of states, are actually observed. In addition, the algorithm is well suited for embedding high-frequency real-time information for improving nowcasts and forecasts of lower frequency time series. We evaluate the properties of the algorithm using simulated data. Moreover, as empirical applications, we simulate monthly Brazilian GDP, comparing our results to the Brazilian IBC-BR, and recover what would historical PNAD-C unemployment rates look like prior to 2012.

Suggested Citation

  • Sergio Afonso Lago Alves & Angelo Marsiglia Fasolo, 2015. "Not Just Another Mixed Frequency Paper," Working Papers Series 400, Central Bank of Brazil, Research Department.
    • Handle: RePEc:bcb:wpaper:400
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    File URL: https://www.bcb.gov.br/content/publicacoes/WorkingPaperSeries/wps400.pdf
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    References listed on IDEAS

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    1. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    2. 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.
    3. Bjørn Eraker & Ching Wai (Jeremy) Chiu & Andrew T. Foerster & Tae Bong Kim & Hernán D. Seoane, 2015. "Bayesian Mixed Frequency VARs," Journal of Financial Econometrics, Oxford University Press, vol. 13(3), pages 698-721.
    4. Olivier Basdevant, 2003. "On applications of state-space modelling in macroeconomics," Reserve Bank of New Zealand Discussion Paper Series DP2003/02, Reserve Bank of New Zealand.
    5. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    6. Litterman, Robert, 1986. "Forecasting with Bayesian vector autoregressions -- Five years of experience : Robert B. Litterman, Journal of Business and Economic Statistics 4 (1986) 25-38," International Journal of Forecasting, Elsevier, vol. 2(4), pages 497-498.
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    Cited by:

    1. Fasolo, Angelo M. & Araujo, Eurilton & Jorge, Marcos Valli & Kornelius, Alexandre & Marinho, Leonardo Sousa Gomes, 2024. "Brazilian macroeconomic dynamics redux: Shocks, frictions, and unemployment in SAMBA model," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 5(2).

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