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Estimating Impulse Response Functions When the Shock Series Is Observed

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  • Chi-Young Choi
  • Alexander Chudik

Abstract

We compare the finite sample performance of a variety of consistent approaches to estimating Impulse Response Functions (IRFs) in a linear setup when the shock of interest is observed. Although there is no uniformly superior approach, iterated approaches turn out to perform well in terms of root mean-squared error (RMSE) in diverse environments and sample sizes. For smaller sample sizes, parsimonious specifications are preferred over full specifications with all ?relevant? variables.

Suggested Citation

  • Chi-Young Choi & Alexander Chudik, 2019. "Estimating Impulse Response Functions When the Shock Series Is Observed," Globalization Institute Working Papers 353, Federal Reserve Bank of Dallas.
    • Handle: RePEc:fip:feddgw:353
    DOI: 10.24149/gwp353
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    Cited by:

    1. Pierre L. Siklos, 2020. "Looking into the Rear-View Mirror: Lessons from Japan for the Eurozone and the U.S?," IMES Discussion Paper Series 20-E-02, Institute for Monetary and Economic Studies, Bank of Japan.
    2. Luciana Juvenal, 2020. "Terms-of-Trade Shocks are Not all Alike," IMF Working Papers 2020/280, International Monetary Fund.
    3. Bruns, Martin & Lütkepohl, Helmut, 2022. "Comparison of local projection estimators for proxy vector autoregressions," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    4. Alexander Chudik & Georgios Georgiadis, 2022. "Estimation of Impulse Response Functions When Shocks Are Observed at a Higher Frequency Than Outcome Variables," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 965-979, June.
    5. Dake Li & Mikkel Plagborg-Møller & Christian K. Wolf, 2021. "Local Projections vs. VARs: Lessons From Thousands of DGPs," Working Papers 2021-55, Princeton University. Economics Department..
    6. Chi-Young Choi & Alexander Chudik, 2023. "Mean Group Distributed Lag Estimation of Impulse Response Functions in Large Panels," Globalization Institute Working Papers 423, Federal Reserve Bank of Dallas.
    7. Guerino Ardizzi & Andrea Nobili & Giorgia Rocco, 2020. "A game changer in payment habits: evidence from daily data during a pandemic," Questioni di Economia e Finanza (Occasional Papers) 591, Bank of Italy, Economic Research and International Relations Area.
    8. Cepni, Oguzhan & Gupta, Rangan & Karahan, Cenk C. & Lucey, Brian, 2022. "Oil price shocks and yield curve dynamics in emerging markets," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 613-623.
    9. Mikkel Plagborg‐Møller & Christian K. Wolf, 2021. "Local Projections and VARs Estimate the Same Impulse Responses," Econometrica, Econometric Society, vol. 89(2), pages 955-980, March.
    10. Yang, Jialin & Ge, Ying-En & Li, Kevin X., 2022. "Measuring volatility spillover effects in dry bulk shipping market," Transport Policy, Elsevier, vol. 125(C), pages 37-47.
    11. Ruth Badru, 2020. "Distribution and Gender Effects on the Path of Economic Growth: Comparative Evidence for Developed, Semi-Industrialized, and Low-Income Agricultural Economies," Economics Working Paper Archive wp_959, Levy Economics Institute.

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    More about this item

    Keywords

    Observed shock; impulse response functions; Monte Carlo experiments; Finite sample performance;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General

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