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Time aggregation of mixed causal–noncausal models

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  • Telg, Sean

Abstract

We study systematic and flow aggregation of mixed causal-noncausal autoregressive models. We show that aggregation preserves noncausality and generates a moving average component. Monte Carlo simulations demonstrate that backward- and forward-looking behavior can be identified empirically for sufficiently large samples.

Suggested Citation

  • Telg, Sean, 2024. "Time aggregation of mixed causal–noncausal models," Economics Letters, Elsevier, vol. 244(C).
    • Handle: RePEc:eee:ecolet:v:244:y:2024:i:c:s0165176524005032
    DOI: 10.1016/j.econlet.2024.112019
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    References listed on IDEAS

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    1. Andrea Silvestrini & David Veredas, 2008. "Temporal Aggregation Of Univariate And Multivariate Time Series Models: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 22(3), pages 458-497, July.
    2. Lanne Markku & Saikkonen Pentti, 2011. "Noncausal Autoregressions for Economic Time Series," Journal of Time Series Econometrics, De Gruyter, vol. 3(3), pages 1-32, October.
    3. Fries, Sébastien & Zakoian, Jean-Michel, 2019. "Mixed Causal-Noncausal Ar Processes And The Modelling Of Explosive Bubbles," Econometric Theory, Cambridge University Press, vol. 35(6), pages 1234-1270, December.
    4. Lanne, Markku & Luoto, Jani & Saikkonen, Pentti, 2012. "Optimal forecasting of noncausal autoregressive time series," International Journal of Forecasting, Elsevier, vol. 28(3), pages 623-631.
    5. Lanne, Markku & Luoto, Jani, 2012. "Has US inflation really become harder to forecast?," Economics Letters, Elsevier, vol. 115(3), pages 383-386.
    6. Gourieroux, C. & Jasiak, J. & Monfort, A., 2020. "Stationary bubble equilibria in rational expectation models," Journal of Econometrics, Elsevier, vol. 218(2), pages 714-735.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Systematic and flow aggregation; MAR models; Noncausality; Non-gaussianity;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • 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

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