IDEAS home Printed from https://ideas.repec.org/p/bge/wpaper/1474.html

Policy evaluation with Sufficient Macro Statistics -a primer

Author

Listed:
  • Régis Barnichon
  • Geert Mesters

Abstract

Impulse responses and forecasts are central concepts for policy makers. In addition, they are also sufficient statistics to solve many important macroeconomic problems, from policy counterfactuals to policy evaluation, and they offer a promising alternative to the standard structural modeling approach. In this review paper, we discuss and extend recent progress on the use of these sufficient macro statistics for policy evaluation. We illustrate the methods by evaluating the performance of the ECB over 1999-2023.

Suggested Citation

  • Régis Barnichon & Geert Mesters, 2025. "Policy evaluation with Sufficient Macro Statistics -a primer," Working Papers 1474, Barcelona School of Economics.
  • Handle: RePEc:bge:wpaper:1474
    as

    Download full text from publisher

    File URL: https://bw.bse.esgallapre3.com/wp-content/uploads/2025/02/1474-1.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Agnès Bénassy-Quéré & Benoît Coeuré & Pierre Jacquet & Jean Pisani-Ferry, 2018. "Economic Policy: Theory and Practice. Second edition," Post-Print halshs-01883894, HAL.
    2. Graham Elliott & Allan Timmermann, 2016. "Forecasting in Economics and Finance," Annual Review of Economics, Annual Reviews, vol. 8(1), pages 81-110, October.
    3. Martin Beraja, 2023. "A Semistructural Methodology for Policy Counterfactuals," Journal of Political Economy, University of Chicago Press, vol. 131(1), pages 190-201.
    4. 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.
    5. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575, January.
    6. Barro, Robert J & Gordon, David B, 1983. "A Positive Theory of Monetary Policy in a Natural Rate Model," Journal of Political Economy, University of Chicago Press, vol. 91(4), pages 589-610, August.
    7. Graham Elliott & Allan Timmermann, 2016. "Economic Forecasting," Economics Books, Princeton University Press, edition 1, number 10740, December.
    8. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    9. Atsushi Inoue & Barbara Rossi, 2021. "A new approach to measuring economic policy shocks, with an application to conventional and unconventional monetary policy," Quantitative Economics, Econometric Society, vol. 12(4), pages 1085-1138, November.
    10. Dhaene, Geert & Barten, Anton P., 1989. "When it all began : The 1936 Tinbergen model revisited," Economic Modelling, Elsevier, vol. 6(2), pages 203-219, April.
    11. Odendahl, Florens & Pagliari, Maria Sole & Penalver, Adrian & Rossi, Barbara & Sestieri, Giulia, 2024. "Euro area monetary policy effects. Does the shape of the yield curve matter?," Journal of Monetary Economics, Elsevier, vol. 147(S).
    12. Isaiah Andrews & Anna Mikusheva, 2015. "Maximum likelihood inference in weakly identified dynamic stochastic general equilibrium models," Quantitative Economics, Econometric Society, vol. 6(1), pages 123-152, March.
    Full references (including those not matched with items on IDEAS)

    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. Bernardino Adão & Sandra Gomes & Laura Alpizar, 2025. "On how to assess the impact of monetary policy," Economic Bulletin and Financial Stability Report Articles and Banco de Portugal Economic Studies, Banco de Portugal, Economics and Research Department.
    2. Alisdair McKay & Christian K. Wolf, 2023. "What Can Time‐Series Regressions Tell Us About Policy Counterfactuals?," Econometrica, Econometric Society, vol. 91(5), pages 1695-1725, September.
    3. Drautzburg, Thorsten & Wright, Jonathan H., 2023. "Refining set-identification in VARs through independence," Journal of Econometrics, Elsevier, vol. 235(2), pages 1827-1847.
    4. Herwartz, Helmut & Rohloff, Hannes & Wang, Shu, 2022. "Proxy SVAR identification of monetary policy shocks - Monte Carlo evidence and insights for the US," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
    5. Arefeva, Alina & Arefyev, Nikolay, 2025. "Playing by the Taylor rules or sticking to Friedman’s policy: A new approach to monetary policy identification," Economic Modelling, Elsevier, vol. 143(C).
    6. Arai, Natsuki, 2020. "Investigating the inefficiency of the CBO’s budgetary projections," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1290-1300.
    7. Thomas Drechsel, 2023. "Earnings-Based Borrowing Constraints and Macroeconomic Fluctuations," American Economic Journal: Macroeconomics, American Economic Association, vol. 15(2), pages 1-34, April.
    8. Herwartz, Helmut & Rohloff, Hannes & Wang, Shu, 2020. "Proxy SVAR identification of monetary policy shocks: MonteCarlo evidence and insights for the US," University of Göttingen Working Papers in Economics 404, University of Goettingen, Department of Economics.
    9. Alessio Volpicella, 2022. "SVARs Identification Through Bounds on the Forecast Error Variance," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1291-1301, June.
    10. Endong Wang, 2024. "Local projections identify the same policy counterfactuals as empirical and structural models," Papers 2409.09577, arXiv.org, revised Feb 2026.
    11. Cordoni, Francesco & Dorémus, Nicolas & Moneta, Alessio, 2024. "Identification of vector autoregressive models with nonlinear contemporaneous structure," Journal of Economic Dynamics and Control, Elsevier, vol. 162(C).
    12. Mikkel Plagborg‐Møller & Christian K. Wolf, 2021. "Local Projections and VARs Estimate the Same Impulse Responses," Econometrica, Econometric Society, vol. 89(2), pages 955-980, March.
    13. Yuriy Gorodnichenko & Byoungchan Lee, 2017. "A Note on Variance Decomposition with Local Projections," NBER Working Papers 23998, National Bureau of Economic Research, Inc.
    14. Hernández, Juan R., 2025. "Covered interest parity: A forecasting approach to estimate the neutral band," Economic Modelling, Elsevier, vol. 148(C).
    15. Britta Gehrke & Fang Yao, 2016. "Persistence and volatility of real exchange rates: the role of supply shocks revisited," Reserve Bank of New Zealand Discussion Paper Series DP2016/02, Reserve Bank of New Zealand.
    16. Régis Barnichon & Geert Mesters, 2020. "Optimal policy perturbations," Economics Working Papers 1716, Department of Economics and Business, Universitat Pompeu Fabra.
    17. Gahn, Santiago José, 2021. "On the adjustment of capacity utilisation to aggregate demand: Revisiting an old Sraffian critique to the Neo-Kaleckian model," Structural Change and Economic Dynamics, Elsevier, vol. 58(C), pages 325-360.
    18. Alain Guay, 2020. "Identification of Structural Vector Autoregressions Through Higher Unconditional Moments," Working Papers 20-19, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    19. Herwartz, Helmut & Lange, Alexander & Maxand, Simone, 2019. "Statistical identification in SVARs - Monte Carlo experiments and a comparative assessment of the role of economic uncertainties for the US business cycle," University of Göttingen Working Papers in Economics 375, University of Goettingen, Department of Economics.
    20. Demetrescu, Matei & Salish, Nazarii, 2024. "(Structural) VAR models with ignored changes in mean and volatility," International Journal of Forecasting, Elsevier, vol. 40(2), pages 840-854.

    More about this item

    Keywords

    ;
    ;
    ;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric 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
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • N10 - Economic History - - Macroeconomics and Monetary Economics; Industrial Structure; Growth; Fluctuations - - - General, International, or Comparative

    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:bge:wpaper:1474. 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: Bruno Guallar (email available below). General contact details of provider: https://edirc.repec.org/data/bargses.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.