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Estimation of impulse response functions when shocks are observed at a higher frequency than outcome variables

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  • Chudik, Alexander
  • Georgiadis, Georgios

Abstract

This paper proposes mixed-frequency distributed-lag (MFDL) estimators of impulse response functions (IRFs) in a setup where (i) the shock of interest is observed, (ii) the impact variable of interest is observed at a lower frequency (as a temporally aggregated or sequentially sampled variable), (iii) the data generating process (DGP) is given by a VAR model at the frequency of the shock, and (iv) the full set of relevant endogenous variables entering the DGP is unknown or unobserved. Consistency and asymptotic normality of the proposed MFDL estimators is established, and their small-sample performance is documented by a set of Monte Carlo experiments. The proposed approach is then applied to estimate the daily pass-through of changes in crude oil prices observed at the daily frequency to U.S. gasoline consumer prices observed at the weekly frequency. We find that the pass-through is fast, with about 23% of the crude oil price changes passed through to retail gasoline prices within five working days, representing about 42% of the long-run pass-through. JEL Classification: C22

Suggested Citation

  • Chudik, Alexander & Georgiadis, Georgios, 2019. "Estimation of impulse response functions when shocks are observed at a higher frequency than outcome variables," Working Paper Series 2307, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:20192307
    Note: 2435756
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    References listed on IDEAS

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    1. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    2. Davidson, James, 1994. "Stochastic Limit Theory: An Introduction for Econometricians," OUP Catalogue, Oxford University Press, number 9780198774037, November.
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    6. Choi, Chi-Young & Chudik, Alexander, 2019. "Estimating impulse response functions when the shock series is observed," Economics Letters, Elsevier, vol. 180(C), pages 71-75.
    7. Ghysels, Eric, 2016. "Macroeconomics and the reality of mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 294-314.
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    9. Bacchiocchi, Emanuele & Bastianin, Andrea & Missale, Alessandro & Rossi, Eduardo, 2016. "Structural analysis with mixed frequencies: monetary policy, uncertainty and gross capital flows," Working Papers 2016-04, Joint Research Centre, European Commission.
    10. Lutz Kilian, 2008. "Exogenous Oil Supply Shocks: How Big Are They and How Much Do They Matter for the U.S. Economy?," The Review of Economics and Statistics, MIT Press, vol. 90(2), pages 216-240, May.
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    Cited by:

    1. Piergiorgio Alessandri & Andrea Gazzani & Alejandro Vicondoa, 2021. "The real effects of financial uncertainty shocks: A daily identification approach," Working Papers 61, Red Nacional de Investigadores en Economía (RedNIE).
    2. Lutz Kilian & Xiaoqing Zhou, 2020. "The Econometrics of Oil Market VAR Models," CESifo Working Paper Series 8153, CESifo.
    3. Alejandro Vicondoa & Andrea Gazzani, 2020. "Bridge Proxy-SVAR: Estimating the Macroeconomic Effects of Shocks Identified at High-Frequency," Documentos de Trabajo 533, Instituto de Economia. Pontificia Universidad Católica de Chile..
    4. Shioji, Etsuro, 2021. "Pass-through of oil supply shocks to domestic gasoline prices: evidence from daily data," Energy Economics, Elsevier, vol. 98(C).
    5. Gareth Anderson & Ambrogio Cesa-Bianchi, 2020. "Crossing the Credit Channel: Credit Spreads and Firm Heterogeneity," Discussion Papers 2005, Centre for Macroeconomics (CFM).

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    More about this item

    Keywords

    estimation and inference; impulse response functions; mixed frequencies; temporal aggregation; VAR models;
    All these keywords.

    JEL classification:

    • 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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