IDEAS home Printed from https://ideas.repec.org/a/wly/japmet/v34y2019i2p247-267.html
   My bibliography  Save this article

Bootstrap inference for impulse response functions in factor‐augmented vector autoregressions

Author

Listed:
  • Yohei Yamamoto

Abstract

In this study, we consider residual‐based bootstrap methods to construct the confidence interval for structural impulse response functions in factor‐augmented vector autoregressions. In particular, we compare the bootstrap with factor estimation (Procedure A) with the bootstrap without factor estimation (Procedure B). Both procedures are asymptotically valid under the condition T/N→0, where N and T are the cross‐sectional dimension and the time dimension, respectively. However, Procedure A is also valid even when T/N→c with 0 ≤ c

Suggested Citation

  • Yohei Yamamoto, 2019. "Bootstrap inference for impulse response functions in factor‐augmented vector autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(2), pages 247-267, March.
    • Handle: RePEc:wly:japmet:v:34:y:2019:i:2:p:247-267
    DOI: 10.1002/jae.2659
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/jae.2659
    Download Restriction: no
    File URL: https://libkey.io/10.1002/jae.2659?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. Blanchard, Olivier Jean & Quah, Danny, 1989. "The Dynamic Effects of Aggregate Demand and Supply Disturbances," American Economic Review, American Economic Association, vol. 79(4), pages 655-673, September.
    2. Uhlig, Harald, 2005. "What are the effects of monetary policy on output? Results from an agnostic identification procedure," Journal of Monetary Economics, Elsevier, vol. 52(2), pages 381-419, March.
    3. YAMAMOTO, Yohei & 山本, 庸平, 2015. "Asymptotic Inference for Common Factor Models in the Presence of Jumps," Discussion Papers 2015-05, Graduate School of Economics, Hitotsubashi University.
    4. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    5. Juan F. Rubio-Ramírez & Daniel F. Waggoner & Tao Zha, 2010. "Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 77(2), pages 665-696.
    6. Jean Boivin & Marc P. Giannoni & Dalibor Stevanović, 2020. "Dynamic Effects of Credit Shocks in a Data-Rich Environment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 272-284, April.
    7. Domenico Giannone & Lucrezia Reichlin & Luca Sala, 2005. "Monetary Policy in Real Time," NBER Chapters, in: NBER Macroeconomics Annual 2004, Volume 19, pages 161-224, National Bureau of Economic Research, Inc.
    8. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(1), pages 387-422.
    9. Bénédicte Vidaillet & V. d'Estaintot & P. Abécassis, 2005. "Introduction," Post-Print hal-00287137, HAL.
    10. Gonçalves, Sílvia & Perron, Benoit, 2014. "Bootstrapping factor-augmented regression models," Journal of Econometrics, Elsevier, vol. 182(1), pages 156-173.
    11. Ang, Andrew & Piazzesi, Monika, 2003. "A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables," Journal of Monetary Economics, Elsevier, vol. 50(4), pages 745-787, May.
    12. Sydney C. Ludvigson & Serena Ng, 2009. "Macro Factors in Bond Risk Premia," The Review of Financial Studies, Society for Financial Studies, vol. 22(12), pages 5027-5067, December.
    13. Marc P. Giannoni & Jean Boivin, 2005. "DSGE Models in a Data-Rich Environment," Computing in Economics and Finance 2005 431, Society for Computational Economics.
    14. Moench, Emanuel, 2008. "Forecasting the yield curve in a data-rich environment: A no-arbitrage factor-augmented VAR approach," Journal of Econometrics, Elsevier, vol. 146(1), pages 26-43, September.
    15. Jean Boivin & Marc P. Giannoni & Ilian Mihov, 2009. "Sticky Prices and Monetary Policy: Evidence from Disaggregated US Data," American Economic Review, American Economic Association, vol. 99(1), pages 350-384, March.
    16. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
    17. Mototsugu Shintani & Zi-Yi Guo, 2018. "Improving the finite sample performance of autoregression estimators in dynamic factor models: A bootstrap approach," Econometric Reviews, Taylor & Francis Journals, vol. 37(4), pages 360-379, April.
    18. Gilchrist, Simon & Yankov, Vladimir & Zakrajsek, Egon, 2009. "Credit market shocks and economic fluctuations: Evidence from corporate bond and stock markets," Journal of Monetary Economics, Elsevier, vol. 56(4), pages 471-493, May.
    19. Acconcia, Antonio & Simonelli, Saverio, 2008. "Interpreting aggregate fluctuations looking at sectors," Journal of Economic Dynamics and Control, Elsevier, vol. 32(9), pages 3009-3031, September.
    20. Shintani, Mototsugu & Guo, Zi-Yi, 2011. "Finite Sample Performance of Principal Components Estimators for Dynamic Factor Models: Asymptotic vs. Bootstrap Approximations," EconStor Preprints 167627, ZBW - Leibniz Information Centre for Economics.
    21. Lutz Kilian, 1999. "Finite-Sample Properties of Percentile and Percentile-t Bootstrap Confidence Intervals for Impulse Responses," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 652-660, November.
    22. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
    23. Jushan Bai & Serena Ng, 2006. "Confidence Intervals for Diffusion Index Forecasts and Inference for Factor-Augmented Regressions," Econometrica, Econometric Society, vol. 74(4), pages 1133-1150, July.
    24. Bai, Jushan & Ng, Serena, 2013. "Principal components estimation and identification of static factors," Journal of Econometrics, Elsevier, vol. 176(1), pages 18-29.
    25. Lutz Kilian, 1998. "Small-Sample Confidence Intervals For Impulse Response Functions," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 218-230, May.
    26. Thomas J. Sargent & Christopher A. Sims, 1977. "Business cycle modeling without pretending to have too much a priori economic theory," Working Papers 55, Federal Reserve Bank of Minneapolis.
    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. Bicu, A.C. & Lieb, L.M., 2015. "Cross-border effects of fiscal policy in the Eurozone," Research Memorandum 019, Maastricht University, Graduate School of Business and Economics (GSBE).
    2. Mototsugu Shintani & Zi-Yi Guo, 2018. "Improving the finite sample performance of autoregression estimators in dynamic factor models: A bootstrap approach," Econometric Reviews, Taylor & Francis Journals, vol. 37(4), pages 360-379, April.
    3. Karaki, Mohamad B. & Rangaraju, Sandeep Kumar, 2023. "The confidence channel of U.S. financial uncertainty: Evidence from industry-level data," Economic Modelling, Elsevier, vol. 129(C).
    4. Zongwu Cai & Xiyuan Liu, 2021. "Solving the Price Puzzle Via A Functional Coefficient Factor-Augmented VAR Model," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202106, University of Kansas, Department of Economics, revised Jan 2021.
    5. 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.
    6. Gonçalves, Sílvia & Perron, Benoit, 2014. "Bootstrapping factor-augmented regression models," Journal of Econometrics, Elsevier, vol. 182(1), pages 156-173.
    7. Martin Bruns, 2019. "Proxy VAR models in a data-rich environment," University of East Anglia School of Economics Working Paper Series 2019-03, School of Economics, University of East Anglia, Norwich, UK..
    8. Yohei Yamamoto & Naoko Hara, 2022. "Identifying factor‐augmented vector autoregression models via changes in shock variances," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 722-745, June.
    9. Dominik Bertsche, 2019. "The effects of oil supply shocks on the macroeconomy: a Proxy-FAVAR approachThe effects of oil supply shocks on the macroeconomy: a Proxy-FAVAR approach," Working Paper Series of the Department of Economics, University of Konstanz 2019-06, Department of Economics, University of Konstanz.
    10. Herrera, Ana María & Karaki, Mohamad B. & Rangaraju, Sandeep Kumar, 2017. "Where do jobs go when oil prices drop?," Energy Economics, Elsevier, vol. 64(C), pages 469-482.
    11. Gent Bajraj & Jorge Lorca & Juan M. Wlasiuk, 2022. "On Foreign Drivers of EMEs Fluctuations," Working Papers Central Bank of Chile 951, Central Bank of Chile.
    12. repec:cte:wsrepe:23974 is not listed on IDEAS
    13. Franz Ramsauer & Aleksey Min & Michael Lingauer, 2019. "Estimation of FAVAR Models for Incomplete Data with a Kalman Filter for Factors with Observable Components," Econometrics, MDPI, vol. 7(3), pages 1-43, July.
    14. Krampe, J. & Paparoditis, E. & Trenkler, C., 2023. "Structural inference in sparse high-dimensional vector autoregressions," Journal of Econometrics, Elsevier, vol. 234(1), pages 276-300.
    15. Javier Maldonado & Esther Ruiz, 2021. "Accurate Confidence Regions for Principal Components Factors," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(6), pages 1432-1453, December.
    16. Anindya Banerjee & Victor Bystrov & Paul Mizen, 2017. "Structural Factor Analysis of Interest Rate Pass Through In Four Large Euro Area Economies," Working Papers in Economics 17/07, University of Canterbury, Department of Economics and Finance.
    17. Yoshiki Nakajima & Naoya Sueishi, 2022. "Forecasting the Japanese macroeconomy using high-dimensional data," The Japanese Economic Review, Springer, vol. 73(2), pages 299-324, April.
    18. Jushan Bai & Kunpeng Li & Lina Lu, 2016. "Estimation and Inference of FAVAR Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 620-641, October.
    19. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    20. Bruns, Martin, 2021. "Proxy Vector Autoregressions in a Data-rich Environment," Journal of Economic Dynamics and Control, Elsevier, vol. 123(C).
    21. Shintani, Mototsugu & Guo, Zi-Yi, 2011. "Finite Sample Performance of Principal Components Estimators for Dynamic Factor Models: Asymptotic vs. Bootstrap Approximations," EconStor Preprints 167627, ZBW - Leibniz Information Centre for Economics.
    22. Antoine A. Djogbenou, 2024. "Identifying oil price shocks with global, developed, and emerging latent real economy activity factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 128-149, January.

    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. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    2. Jushan Bai & Kunpeng Li & Lina Lu, 2016. "Estimation and Inference of FAVAR Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 620-641, October.
    3. Matteo Barigozzi & Antonio M. Conti & Matteo Luciani, 2014. "Do Euro Area Countries Respond Asymmetrically to the Common Monetary Policy?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(5), pages 693-714, October.
    4. Matteo Luciani, 2015. "Monetary Policy and the Housing Market: A Structural Factor Analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(2), pages 199-218, March.
    5. Masud Alam, 2024. "Output, employment, and price effects of U.S. narrative tax changes: a factor-augmented vector autoregression approach," Empirical Economics, Springer, vol. 67(4), pages 1421-1471, October.
    6. Han, Xu, 2015. "Tests for overidentifying restrictions in Factor-Augmented VAR models," Journal of Econometrics, Elsevier, vol. 184(2), pages 394-419.
    7. repec:cte:wsrepe:23974 is not listed on IDEAS
    8. Forni, Mario & Hallin, Marc & Lippi, Marco & Zaffaroni, Paolo, 2015. "Dynamic factor models with infinite-dimensional factor spaces: One-sided representations," Journal of Econometrics, Elsevier, vol. 185(2), pages 359-371.
    9. Kemal Bagzibagli, 2014. "Monetary transmission mechanism and time variation in the Euro area," Empirical Economics, Springer, vol. 47(3), pages 781-823, November.
    10. Han, Xu, 2018. "Estimation and inference of dynamic structural factor models with over-identifying restrictions," Journal of Econometrics, Elsevier, vol. 202(2), pages 125-147.
    11. Jean Boivin & Marc P. Giannoni & Dalibor Stevanović, 2020. "Dynamic Effects of Credit Shocks in a Data-Rich Environment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 272-284, April.
    12. Yohei Yamamoto & Naoko Hara, 2022. "Identifying factor‐augmented vector autoregression models via changes in shock variances," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 722-745, June.
    13. Jin, Xisong & Nadal De Simone, Francisco, 2020. "Monetary policy and systemic risk-taking in the Euro area investment fund industry: A structural factor-augmented vector autoregression analysis," Journal of Financial Stability, Elsevier, vol. 49(C).
    14. Pang, Iris Ai Jao, 2010. "Were Fed’s active monetary policy actions necessary?," MPRA Paper 32496, University Library of Munich, Germany.
    15. Mario Forni & Marc Hallin & Marco Lippi & Paolo Zaffaroni, 2011. "One-Sided Representations of Generalized Dynamic Factor Models," DSS Empirical Economics and Econometrics Working Papers Series 2011/5, Centre for Empirical Economics and Econometrics, Department of Statistics, "Sapienza" University of Rome.
    16. Forni, Mario & Gambetti, Luca, 2010. "The dynamic effects of monetary policy: A structural factor model approach," Journal of Monetary Economics, Elsevier, vol. 57(2), pages 203-216, March.
    17. Forni, Mario & Giannone, Domenico & Lippi, Marco & Reichlin, Lucrezia, 2009. "Opening The Black Box: Structural Factor Models With Large Cross Sections," Econometric Theory, Cambridge University Press, vol. 25(5), pages 1319-1347, October.
    18. Dimitris Korobilis, 2013. "Assessing the Transmission of Monetary Policy Using Time-varying Parameter Dynamic Factor Models-super-," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(2), pages 157-179, April.
    19. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
    20. Javier Maldonado & Esther Ruiz, 2021. "Accurate Confidence Regions for Principal Components Factors," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(6), pages 1432-1453, December.
    21. Bai, Jushan & Wang, Peng, 2014. "Identification theory for high dimensional static and dynamic factor models," Journal of Econometrics, Elsevier, vol. 178(2), pages 794-804.

    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

    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:wly:japmet:v:34:y:2019:i:2:p:247-267. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/0883-7252/ .

    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.