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Measurement Errors and Monetary Policy: Then and Now

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Abstract

Should policymakers and applied macroeconomists worry about the difference between real-time and final data? We tackle this question by using a VAR with time-varying parameters and stochastic volatility to show that the distinctionbetween real-time data and final data matters for the impact of monetary policy shocks: The impact on final data is substantially and systematically different (in particular, larger in magnitude for different measures of real activity) from theimpact on real-time data. These differences have persisted over the last 40 years and should be taken into account when conducting or studying monetary policy.

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  • Pooyan Amir-Ahmadi & Christian Matthes & Mu-Chun Wang, 2015. "Measurement Errors and Monetary Policy: Then and Now," Working Paper 15-13, Federal Reserve Bank of Richmond.
    • Handle: RePEc:fip:fedrwp:15-13
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    Cited by:

    1. Soojin Jo & Rodrigo Sekkel, 2019. "Macroeconomic Uncertainty Through the Lens of Professional Forecasters," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(3), pages 436-446, July.
    2. Christiane Baumeister & James D. Hamilton, 2019. "Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks," American Economic Review, American Economic Association, vol. 109(5), pages 1873-1910, May.
    3. Baumeister, Christiane & Hamilton, James D., 2020. "Drawing conclusions from structural vector autoregressions identified on the basis of sign restrictions," Journal of International Money and Finance, Elsevier, vol. 109(C).
    4. Aymeric Ortmans, 2020. "Evolving Monetary Policy in the Aftermath of the Great Recession," Documents de recherche 20-01, Centre d'Études des Politiques Économiques (EPEE), Université d'Evry Val d'Essonne.
    5. C. Glocker & G. Sestieri & P. Towbin, 2017. "Time-varying fiscal spending multipliers in the UK," Working papers 643, Banque de France.
    6. Glocker, Christian & Sestieri, Giulia & Towbin, Pascal, 2019. "Time-varying government spending multipliers in the UK," Journal of Macroeconomics, Elsevier, vol. 60(C), pages 180-197.
    7. Baumeister, Christiane & Hamilton, James D., 2021. "Reprint: Drawing conclusions from structural vector autoregressions identified on the basis of sign restrictions," Journal of International Money and Finance, Elsevier, vol. 114(C).
    8. Baumeister, Christiane & Hamilton, James, 2017. "Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Deman," CEPR Discussion Papers 12532, C.E.P.R. Discussion Papers.

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    More about this item

    Keywords

    real-time data; time-varying parameters; stochastic volatility; impulse responses;
    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
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

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