IDEAS home Printed from https://ideas.repec.org/p/qmw/qmwecw/891.html
   My bibliography  Save this paper

Time-Varying Local Projections

Author

Listed:
  • Germano Ruisi

    (Queen Mary University of London)

Abstract

In recent years local projections have become a more and more popular methodology for the estimation of impulse responses. Besides being relatively easy to implement, the main strength of this approach relative to the traditional VAR one is that there is no need to impose any specific assumption on the dynamics of the data. This paper models local projections in a time-varying framework and provides a Gibbs sampler routine to estimate them. A simulation study shows how the performance of the algorithm is satisfactory while the usefulness of the model developed here is shown through an application to fiscal policy shocks.

Suggested Citation

  • Germano Ruisi, 2019. "Time-Varying Local Projections," Working Papers 891, Queen Mary University of London, School of Economics and Finance.
  • Handle: RePEc:qmw:qmwecw:891
    as

    Download full text from publisher

    File URL: https://www.qmul.ac.uk/sef/media/econ/research/workingpapers/2019/wp891.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. Silvia Miranda-Agrippino & Giovanni Ricco, 2015. "The Transmission of Monetary Policy Shocks," Discussion Papers 1711, Centre for Macroeconomics (CFM), revised Feb 2017.
    2. Gergely Ganics & Atsushi Inoue & Barbara Rossi, 2018. "Confidence intervals for bias and size distortion in IV and local projections — IV models," Working Papers 1841, Banco de España;Working Papers Homepage.
    3. Christina D. Romer & David H. Romer, 2010. "The Macroeconomic Effects of Tax Changes: Estimates Based on a New Measure of Fiscal Shocks," American Economic Review, American Economic Association, vol. 100(3), pages 763-801, June.
    4. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    5. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," Review of Economic Studies, Oxford University Press, vol. 72(3), pages 821-852.
    6. 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.
    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. Philippe Goulet Coulombe, 2020. "Time-Varying Parameters as Ridge Regressions," Papers 2009.00401, arXiv.org.

    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. Ramey, V.A., 2016. "Macroeconomic Shocks and Their Propagation," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.),Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 71-162, Elsevier.
    2. Lusompa, Amaze, 2019. "Local Projections, Autocorrelation, and Efficiency," MPRA Paper 99856, University Library of Munich, Germany, revised 11 Apr 2020.
    3. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2018. "Bayesian Vector Autoregressions," The Warwick Economics Research Paper Series (TWERPS) 1159, University of Warwick, Department of Economics.
    4. Pereira Manuel Coutinho & Lopes Artur Silva, 2014. "Time-varying fiscal policy in the US," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(2), pages 1-28, April.
    5. Mandalinci, Zeyyad, 2017. "Forecasting inflation in emerging markets: An evaluation of alternative models," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1082-1104.
    6. Andrea Boitani & Salvatore Perdichizzi, 2018. "Public Expenditure Multipliers in recessions. Evidence from the Eurozone," DISCE - Working Papers del Dipartimento di Economia e Finanza def068, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    7. Fabian Krüger & Todd E. Clark & Francesco Ravazzolo, 2017. "Using Entropic Tilting to Combine BVAR Forecasts With External Nowcasts," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 470-485, July.
    8. Gergely Ganics & Atsushi Inoue & Barbara Rossi, 2018. "Confidence intervals for bias and size distortion in IV and local projections — IV models," Working Papers 1841, Banco de España;Working Papers Homepage.
    9. Caldara, Dario & Fuentes-Albero, Cristina & Gilchrist, Simon & Zakrajšek, Egon, 2016. "The macroeconomic impact of financial and uncertainty shocks," European Economic Review, Elsevier, vol. 88(C), pages 185-207.
    10. Dovern, Jonas & Manner, Hans, 2016. "Robust Evaluation of Multivariate Density Forecasts," VfS Annual Conference 2016 (Augsburg): Demographic Change 145547, Verein für Socialpolitik / German Economic Association.
    11. Belongia, Michael T. & Ireland, Peter N., 2016. "The evolution of U.S. monetary policy: 2000–2007," Journal of Economic Dynamics and Control, Elsevier, vol. 73(C), pages 78-93.
    12. Karlsson, Sune, 2013. "Forecasting with Bayesian Vector Autoregression," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 791-897, Elsevier.
    13. Barbara Rossi, 2018. "Identifying and estimating the effects of unconventional monetary policy in the data: How to do It and what have we learned?," Economics Working Papers 1641, Department of Economics and Business, Universitat Pompeu Fabra.
    14. Gergely Ganics & Florens Odendahl, 2019. "Bayesian VAR Forecasts, Survey Information and Structural Change in the Euro Area," Working papers 733, Banque de France.
    15. Ashesh Rambachan & Neil Shephard, 2019. "Econometric analysis of potential outcomes time series: instruments, shocks, linearity and the causal response function," Papers 1903.01637, arXiv.org, revised Feb 2020.
    16. Niall O’Sullivan & Sheng Zhu & Jason Foran, 2019. "Sentiment versus liquidity pricing effects in the cross-section of UK stock returns," Journal of Asset Management, Palgrave Macmillan, vol. 20(4), pages 317-329, July.
    17. Kanazawa, Nobuyuki, 2020. "Radial basis functions neural networks for nonlinear time series analysis and time-varying effects of supply shocks," Journal of Macroeconomics, Elsevier, vol. 64(C).
    18. Albuquerque, Bruno & Iseringhausen, Martin & Opitz, Frederic, 2020. "Monetary policy and US housing expansions: The case of time-varying supply elasticities," Economics Letters, Elsevier, vol. 195(C).
    19. Stelios Bekiros & Alessia Paccagnini, 2013. "On the predictability of time-varying VAR and DSGE models," Empirical Economics, Springer, vol. 45(1), pages 635-664, August.
    20. Christophe André & Petre Caraiani & Adrian Cantemir Čalin & Rangan Gupta, 2018. "Can Monetary Policy Lean against Housing Bubbles?," Working Papers 201877, University of Pretoria, Department of Economics.

    More about this item

    Keywords

    Time-Varying Coefficients; Local Projections;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: 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
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:qmw:qmwecw:891. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Nicholas Owen) The email address of this maintainer does not seem to be valid anymore. Please ask Nicholas Owen to update the entry or send us the correct email address. General contact details of provider: http://edirc.repec.org/data/deqmwuk.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.