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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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    7. 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.
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    Citations

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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, 2023. "Oil Price Shocks and Inflation," Working Papers 2312, Federal Reserve Bank of Dallas.
    3. Lutz Kilian & Xiaoqing Zhou, 2023. "The Econometrics of Oil Market VAR Models," Advances in Econometrics, in: Essays in Honor of Joon Y. Park: Econometric Methodology in Empirical Applications, volume 45, pages 65-95, Emerald Group Publishing Limited.
    4. 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..
    5. Shioji, Etsuro, 2021. "Pass-through of oil supply shocks to domestic gasoline prices: evidence from daily data," Energy Economics, Elsevier, vol. 98(C).
    6. Alessandri, Piergiorgio & Gazzani, Andrea & Vicondoa, Alejandro, 2023. "Are the effects of uncertainty shocks big or small?," European Economic Review, Elsevier, vol. 158(C).
    7. Gareth Anderson & Ambrogio Cesa-Bianchi, 2020. "Crossing the Credit Channel: Credit Spreads and Firm Heterogeneity," Discussion Papers 2005, Centre for Macroeconomics (CFM).
    8. Patozi, A., 2023. "Green Transmission: Monetary Policy in the Age of ESG," Cambridge Working Papers in Economics 2311, Faculty of Economics, University of Cambridge.

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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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