IDEAS home Printed from https://ideas.repec.org/p/fip/fedfwp/96372.html

Significance Bands for Local Projections

Author

Listed:
  • Atsushi Inoue

  • Òscar Jordà
  • Guido M. Kuersteiner

Abstract

An impulse response function describes the dynamic evolution of an outcome variable following a stimulus or treatment. A common hypothesis of interest is whether the treatment affects the outcome. We show that this hypothesis is best assessed using significance bands rather than relying on commonly displayed confidence bands. Under the null hypothesis, we show that significance bands are trivial to construct with standard statistical software using the LM principle, and should be reported as a matter of routine when displaying impulse responses graphically.

Suggested Citation

  • Atsushi Inoue & Òscar Jordà & Guido M. Kuersteiner, 2023. "Significance Bands for Local Projections," Working Paper Series 2023-15, Federal Reserve Bank of San Francisco.
    • Handle: RePEc:fip:fedfwp:96372
    DOI: 10.24148/wp2023-15
    as

    Download full text from publisher

    File URL: https://www.frbsf.org/wp-content/uploads/sites/4/wp2023-15.pdf
    File Function: Full text - article PDF
    Download Restriction: no
    File URL: https://libkey.io/10.24148/wp2023-15?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Òscar Jordà, 2009. "Simultaneous Confidence Regions for Impulse Responses," The Review of Economics and Statistics, MIT Press, vol. 91(3), pages 629-647, August.
    2. Inoue, Atsushi & Kilian, Lutz, 2013. "Inference on impulse response functions in structural VAR models," Journal of Econometrics, Elsevier, vol. 177(1), pages 1-13.
    3. Inoue, Atsushi & Kilian, Lutz, 2016. "Joint confidence sets for structural impulse responses," Journal of Econometrics, Elsevier, vol. 192(2), pages 421-432.
    4. James H. Stock & Mark W. Watson, 2018. "Identification and Estimation of Dynamic Causal Effects in Macroeconomics Using External Instruments," Economic Journal, Royal Economic Society, vol. 128(610), pages 917-948, May.
    5. Òscar Jordà, 2005. "Estimation and Inference of Impulse Responses by Local Projections," American Economic Review, American Economic Association, vol. 95(1), pages 161-182, March.
    6. Christina D. Romer & David H. Romer, 2004. "A New Measure of Monetary Shocks: Derivation and Implications," American Economic Review, American Economic Association, vol. 94(4), pages 1055-1084, September.
    7. José Luis Montiel Olea & Mikkel Plagborg‐Møller, 2019. "Simultaneous confidence bands: Theory, implementation, and an application to SVARs," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(1), pages 1-17, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Uwe Hassler & Marc-Oliver Pohle & Tanja Zahn, 2025. "Simultaneous Inference Bands for Autocorrelations," Papers 2503.18560, arXiv.org, revised Aug 2025.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Atsushi Inoue & `Oscar Jord`a & Guido M. Kuersteiner, 2023. "Inference for Local Projections," Papers 2306.03073, arXiv.org, revised Aug 2024.
    2. Òscar Jordà & Alan M. Taylor, 2024. "Local Projections," NBER Working Papers 32822, National Bureau of Economic Research, Inc.
    3. Eickmeier, Sandra & Metiu, Norbert & Prieto, Esteban, 2016. "Time-varying volatility, financial intermediation and monetary policy," Discussion Papers 46/2016, Deutsche Bundesbank.
    4. Montiel Olea, José Luis & Nesbit, James, 2021. "(Machine) learning parameter regions," Journal of Econometrics, Elsevier, vol. 222(1), pages 716-744.
    5. Oscar Jorda & Alan Taylor & Sanjay Singh, 2019. "The Long-Run Effects of Monetary Policy," 2019 Meeting Papers 1307, Society for Economic Dynamics.
    6. Lu, Dong & Tang, Huoqing & Zhang, Chengsi, 2023. "China's monetary policy surprises and corporate real investment," China Economic Review, Elsevier, vol. 77(C).
    7. Barnichon, Regis & Mesters, Geert, 2021. "The Phillips multiplier," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 689-705.
    8. Inoue, Atsushi & Kilian, Lutz, 2022. "Joint Bayesian inference about impulse responses in VAR models," Journal of Econometrics, Elsevier, vol. 231(2), pages 457-476.
    9. Mario Alloza & Jesús Gonzalo & Carlos Sanz, 2025. "Dynamic Effects of Persistent Shocks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(4), pages 380-394, June.
    10. Lynda Khalaf & Beatriz Peraza López, 2020. "Simultaneous Indirect Inference, Impulse Responses and ARMA Models," Econometrics, MDPI, vol. 8(2), pages 1-26, April.
    11. Anna Samarina & Anh D.M. Nguyen, 2024. "Does Monetary Policy Affect Income Inequality in the Euro Area?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 56(1), pages 35-80, February.
    12. Simon Freyaldenhoven & Christian Hansen, 2025. "(Visualizing) Plausible Treatment Effect Paths," Working Papers 25-27, Federal Reserve Bank of Philadelphia.
    13. Lütkepohl, Helmut & Staszewska-Bystrova, Anna & Winker, Peter, 2020. "Constructing joint confidence bands for impulse response functions of VAR models – A review," Econometrics and Statistics, Elsevier, vol. 13(C), pages 69-83.
    14. Priit Jeenas, 2023. "Firm balance sheet liquidity, monetary policy shocks, and investment dynamics," Economics Working Papers 1872, Department of Economics and Business, Universitat Pompeu Fabra.
    15. Kilian, Lutz & Inoue, Atsushi, 2020. "The Role of the Prior in Estimating VAR Models with Sign Restrictions," CEPR Discussion Papers 15545, C.E.P.R. Discussion Papers.
    16. James McNeil, 2020. "Estimation of Impulse response functions with term structure local projections," Working Papers daleconwp2020-05, Dalhousie University, Department of Economics.
    17. Ashoka Mody & Milan Nedeljkovic, 2018. "Central Bank Policies and Financial Markets: Lessons from the Euro Crisis," Working Papers 253, Princeton University, Department of Economics, Center for Economic Policy Studies..
    18. Francesco Furlanetto & Antoine Lepetit & Ørjan Robstad & Juan Rubio-Ramírez & Pål Ulvedal, 2025. "Estimating Hysteresis Effects," American Economic Journal: Macroeconomics, American Economic Association, vol. 17(1), pages 35-70, January.
    19. Pablo Aguilar Perez, 2024. "Global Spillovers of US Monetary Policy: New Insights from the Remittance Channel," Working Papers hal-04706954, HAL.
    20. Jörg Breitung & Ralf Brüggemann, 2019. "Projection estimators for structural impulse responses," Working Paper Series of the Department of Economics, University of Konstanz 2019-05, Department of Economics, University of Konstanz.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:fip:fedfwp:96372. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Federal Reserve Bank of San Francisco Research Library (email available below). General contact details of provider: https://edirc.repec.org/data/frbsfus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.