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Changes in predictive ability with mixed frequency data

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  • Galvão, Ana Beatriz

Abstract

When assessing the predictive power of financial variables for economic activity, researchers usually aggregate higher-frequency data before estimating a forecasting model that assumes the relationship between the financial variable and the dependent variable to be linear. This paper proposes a model called smooth transition mixed data sampling (STMIDAS) regression, which relaxes both of these assumptions. Simulation exercises indicate that the improvements in forecasting accuracy from the use of mixed data sampling are larger in nonlinear than in linear specifications. When forecasting output growth with financial variables in real time, statistically significant improvements over a linear regression are more likely to arise from forecasting with STMIDAS than with MIDAS regressions.

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  • Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
  • Handle: RePEc:eee:intfor:v:29:y:2013:i:3:p:395-410 DOI: 10.1016/j.ijforecast.2012.10.006
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    Cited by:

    1. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Economics Working Papers ECO2013/02, European University Institute.
    2. Stefan Neuwirth, 2017. "Time-varying mixed frequency forecasting: A real-time experiment," KOF Working papers 17-430, KOF Swiss Economic Institute, ETH Zurich.
    3. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Economics Working Papers ECO2013/02, European University Institute.
    4. Elena Andreou & Andros Kourtellos, 2015. "The State and the Future of Cyprus Macroeconomic Forecasting," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 9(1), pages 73-90, June.
    5. repec:wly:japmet:v:31:y:2016:i:7:p:1276-1290 is not listed on IDEAS
    6. Schumacher, Christian, 2014. "MIDAS regressions with time-varying parameters: An application to corporate bond spreads and GDP in the Euro area," Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100289, Verein für Socialpolitik / German Economic Association.
    7. Michael P. Clements, 2014. "Anticipating Early Data Revisions to US GDP and the Effects of Releases on Equity Markets," ICMA Centre Discussion Papers in Finance icma-dp2014-06, Henley Business School, Reading University.
    8. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2015. "Markov-switching mixed-frequency VAR models," International Journal of Forecasting, Elsevier, pages 692-711.
    9. D'Agostino, Antonello & Cimadomo, Jacopo, 2015. "Combining time-variation and mixed-frequencies: an analysis of government spending multipliers in Italy," Working Paper Series 1856, European Central Bank.

    More about this item

    Keywords

    Smooth transition; MIDAS; Predictive ability; Financial indicators; Economic activity;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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