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Proxy SVARs: Asymptotic Theory, Bootstrap Inference, and the Effects of Income Tax Changes in the United States

Author

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  • Carsen Jentsch
  • Kurt Graden Lunsford

Abstract

Proxy structural vector autoregressions (SVARs) identify structural shocks in vector autoregressions (VARs) with external proxy variables that are correlated with the structural shocks of interest but uncorrelated with other structural shocks. We provide asymptotic theory for proxy SVARs when the VAR innovations and proxy variables are jointly ?-mixing. We also prove the asymptotic validity of a residual-based moving block bootstrap (MBB) for inference on statistics that depend jointly on estimators for the VAR coefficients and for covariances of the VAR innovations and proxy variables. These statistics include structural impulse response functions (IRFs). Conversely, wild bootstraps are invalid, even when innovations and proxy variables are either independent and identically distributed or martingale difference sequences, and simulations show that their coverage rates for IRFs can be badly mis-sized. Using the MBB to re-estimate confidence intervals for the IRFs in Mertens and Ravn (2013), we show that inferences cannot be made about the effects of tax changes on output, labor, or investment.

Suggested Citation

  • Carsen Jentsch & Kurt Graden Lunsford, 2016. "Proxy SVARs: Asymptotic Theory, Bootstrap Inference, and the Effects of Income Tax Changes in the United States," Working Papers (Old Series) 1619, Federal Reserve Bank of Cleveland.
    • Handle: RePEc:fip:fedcwp:1619
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    Cited by:

    1. Giovanni Angelini & Luca Fanelli, 2019. "Exogenous uncertainty and the identification of structural vector autoregressions with external instruments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(6), pages 951-971, September.
    2. Karel Mertens & Morten O. Ravn, 2018. "The Dynamic Effects of Personal and Corporate Income Tax Changes in the United States: Reply to Jentsch and Lunsford," Working Papers 1805, Federal Reserve Bank of Dallas.
    3. Daniel A. Dias & João B. Duarte, 2019. "Monetary policy, housing rents, and inflation dynamics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(5), pages 673-687, August.
    4. Katarzyna Budnik & Gerhard Rünstler, 2023. "Identifying structural VARs from sparse narrative instruments: Dynamic effects of US macroprudential policies," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 186-201, March.
    5. Aeimit Lakdawala, 2019. "Decomposing the effects of monetary policy using an external instruments SVAR," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(6), pages 934-950, September.
    6. Kiyotaka Nakashima & Masahiko Shibamoto & Koji Takahashi, 2024. "Identifying Quantitative and Qualitative Monetary Policy Shocks," International Journal of Central Banking, International Journal of Central Banking, vol. 20(3), pages 1-61, July.
    7. Gerald Carlino & Thorsten Drautzburg, 2020. "The role of startups for local labor markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(6), pages 751-775, September.
    8. 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..
    9. Kyungmin Kim, 2017. "Identification of Monetary Policy Shocks with External Instrument SVAR," Finance and Economics Discussion Series 2017-113, Board of Governors of the Federal Reserve System (U.S.).
    10. Herrera, Ana María & Rangaraju, Sandeep Kumar, 2019. "The quantitative effects of tax foresight: Not all states are equal," Journal of Economic Dynamics and Control, Elsevier, vol. 107(C), pages 1-1.
    11. Carsten Jentsch & Kurt G. Lunsford, 2022. "Asymptotically Valid Bootstrap Inference for Proxy SVARs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1876-1891, October.
    12. Mathias Klein & Ludger Linnemann, 2019. "Tax and Spending Shocks in the Open Economy: Are the Deficits Twins?," Discussion Papers of DIW Berlin 1821, DIW Berlin, German Institute for Economic Research.
    13. Daniel Gründler & Eric Mayer & Johann Scharler, 2023. "Monetary Policy Announcements, Information Shocks, and Exchange Rate Dynamics," Open Economies Review, Springer, vol. 34(2), pages 341-369, April.
    14. Thore Schlaak & Malte Rieth & Maximilian Podstawski, 2023. "Monetary policy, external instruments, and heteroskedasticity," Quantitative Economics, Econometric Society, vol. 14(1), pages 161-200, January.
    15. Klein, Mathias & Linnemann, Ludger, 2019. "Tax and spending shocks in the open economy: are the deficits twins?," Working Paper Series 377, Sveriges Riksbank (Central Bank of Sweden).
    16. Laséen, Stefan, 2020. "Monetary Policy Surprises, Central Bank Information Shocks, and Economic Activity in a Small Open Economy," Working Paper Series 396, Sveriges Riksbank (Central Bank of Sweden).
    17. Breitenlechner, Max & Georgiadis, Georgios & Schumann, Ben, 2022. "What goes around comes around: How large are spillbacks from US monetary policy?," Journal of Monetary Economics, Elsevier, vol. 131(C), pages 45-60.
    18. Andrew Keinsley & Shu Wu, 2020. "Marginal Income Tax Rates, Economic Growth, and Primary Fiscal Deficits," Public Finance Review, , vol. 48(5), pages 676-705, September.
    19. Montiel Olea, José L. & Stock, James H. & Watson, Mark W., 2021. "Inference in Structural Vector Autoregressions identified with an external instrument," Journal of Econometrics, Elsevier, vol. 225(1), pages 74-87.
    20. Eminidou, Snezana & Zachariadis, Marios, 2022. "Firms’ expectations and monetary policy shocks in the euro area," Journal of International Money and Finance, Elsevier, vol. 122(C).
    21. Pascal Paul, 2020. "The Time-Varying Effect of Monetary Policy on Asset Prices," The Review of Economics and Statistics, MIT Press, vol. 102(4), pages 690-704, October.
    22. Liu, Dandan & Wang, Qiaoyu & Yan, Karen Xueqing, 2022. "Oil supply news shock and Chinese economy," China Economic Review, Elsevier, vol. 73(C).
    23. G. Angelini & L. Fanelli, 2018. "Identification and estimation issues in Structural Vector Autoregressions with external instruments," Working Papers wp1122, Dipartimento Scienze Economiche, Universita' di Bologna.

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    Keywords

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    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E62 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Fiscal Policy; Modern Monetary Theory
    • H24 - Public Economics - - Taxation, Subsidies, and Revenue - - - Personal Income and Other Nonbusiness Taxes and Subsidies
    • H25 - Public Economics - - Taxation, Subsidies, and Revenue - - - Business Taxes and Subsidies
    • H3 - Public Economics - - Fiscal Policies and Behavior of Economic Agents
    • H31 - Public Economics - - Fiscal Policies and Behavior of Economic Agents - - - Household

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