Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
IDEAS home Printed from https://ideas.repec.org/p/bbk/bbkefp/1804.html
   My bibliography  Save this paper

R2 bounds for predictive models: what univariate properties tell us about multivariate predictability

Author

Listed:
  • Stephen Wright

    (Birkbeck, University of London)

  • James Mitchell

    (Warwick Business School)

  • Donald Robertson

    (University of Cambridge)

Abstract

A longstanding puzzle in macroeconomic forecasting has been that a wide variety of multivariate models have struggled to out-predict univariate models consistently. We seek an explanation for this puzzle in terms of population properties. We derive bounds for the predictive R2 of the true, but unknown, multivariate model from univariate ARMA parameters alone. These bounds can be quite tight, implying little forecasting gain even if we knew the true multivariate model. We illustrate using CPI inflation data and the Eurozone in a specification motivated by a preferred-habitat model to test for monetary policy transmission domestically and internationally. Our findings suggest an impact of monetary policy on variance processes only and provides evidence for an international channel of monetary transmission on both money and capital markets. This is, to our knowledge, the first attempt to use search-engine data in the context of monetary policy.

Suggested Citation

  • Stephen Wright & James Mitchell & Donald Robertson, 2018. "R2 bounds for predictive models: what univariate properties tell us about multivariate predictability," Birkbeck Working Papers in Economics and Finance 1804, Birkbeck, Department of Economics, Mathematics & Statistics.
    • Handle: RePEc:bbk:bbkefp:1804
    as

    Download full text from publisher

    File URL: https://eprints.bbk.ac.uk/id/eprint/26851
    File Function: First version, 2018
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Joshua C. C. Chan, 2018. "Specification tests for time-varying parameter models with stochastic volatility," Econometric Reviews, Taylor & Francis Journals, vol. 37(8), pages 807-823, September.
    2. Joshua C. C. Chan & Gary Koop & Simon M. Potter, 2013. "A New Model of Trend Inflation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 94-106, January.
    3. James H. Stock & Mark W. Watson, 2007. "Erratum to "Why Has U.S. Inflation Become Harder to Forecast?"," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
    4. Beveridge, Stephen & Nelson, Charles R., 1981. "A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the `business cycle'," Journal of Monetary Economics, Elsevier, vol. 7(2), pages 151-174.
    5. 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.
    6. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    7. Lars Peter Hansen & Thomas J. Sargent, 2013. "Recursive Models of Dynamic Linear Economies," Economics Books, Princeton University Press, edition 1, number 10141.
    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. Ana Beatriz Galvão & James Mitchell, 2019. "Measuring Data Uncertainty: An Application using the Bank of England's "Fan Charts" for Historical GDP Growth," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2019-08, Economic Statistics Centre of Excellence (ESCoE).
    2. Mihaela-Eugenia VASILACHE, 2018. "Forecasting the Trend of Art Market," Computational Methods in Social Sciences (CMSS), "Nicolae Titulescu" University of Bucharest, Faculty of Economic Sciences, vol. 6(1), pages 82-93, June.
    3. repec:wrk:wrkemf:35 is not listed on IDEAS
    4. Zhang, Bo & Chan, Joshua C.C. & Cross, Jamie L., 2020. "Stochastic volatility models with ARMA innovations: An application to G7 inflation forecasts," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1318-1328.
    5. repec:wrk:wrkemf:24 is not listed on IDEAS
    6. Tommaso Proietti, 2021. "Predictability, real time estimation, and the formulation of unobserved components models," Econometric Reviews, Taylor & Francis Journals, vol. 40(5), pages 433-454, April.
    7. Julien Champagne & Guillaume Poulin‐Bellisle & Rodrigo Sekkel, 2020. "Introducing the Bank of Canada staff economic projections database," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(1), pages 114-129, 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. Delle Monache, Davide & Petrella, Ivan, 2017. "Adaptive models and heavy tails with an application to inflation forecasting," International Journal of Forecasting, Elsevier, vol. 33(2), pages 482-501.
    2. McNeil, James, 2023. "Monetary policy and the term structure of inflation expectations with information frictions," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    3. Luis Uzeda, 2022. "State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models," Advances in Econometrics, in: Essays in Honour of Fabio Canova, volume 44, pages 25-53, Emerald Group Publishing Limited.
    4. Behera, Harendra Kumar & Patra, Michael Debabrata, 2022. "Measuring trend inflation in India," Journal of Asian Economics, Elsevier, vol. 80(C).
    5. Alex, Dony, 2021. "Anchoring of inflation expectations in large emerging economies," The Journal of Economic Asymmetries, Elsevier, vol. 23(C).
    6. Joshua C.C. Chan & Rodney W. Strachan, 2023. "Bayesian State Space Models In Macroeconometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 58-75, February.
    7. Francesco Bianchi & Giovanni Nicolo & Dongho Song, 2023. "Inflation and Real Activity over the Business Cycle," Finance and Economics Discussion Series 2023-038, Board of Governors of the Federal Reserve System (U.S.).
    8. De Schryder, Selien & Peersman, Gert & Wauters, Joris, 2020. "Wage indexation and the monetary policy regime," Journal of Macroeconomics, Elsevier, vol. 63(C).
    9. Patrick C. Higgins, 2014. "GDPNow: A Model for GDP \"Nowcasting\"," FRB Atlanta Working Paper 2014-7, Federal Reserve Bank of Atlanta.
    10. Yunjong Eo & Luis Uzeda & Benjamin Wong, 2023. "Understanding trend inflation through the lens of the goods and services sectors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(5), pages 751-766, August.
    11. James M. Nason & Gregor W. Smith, 2021. "Measuring the slowly evolving trend in US inflation with professional forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(1), pages 1-17, January.
    12. Qazi Haque, 2022. "Monetary Policy, Inflation Target, and the Great Moderation: An Empirical Investigation," International Journal of Central Banking, International Journal of Central Banking, vol. 18(4), pages 1-52, October.
    13. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    14. Thomas Hasenzagl & Filippo Pellegrino & Lucrezia Reichlin & Giovanni Ricco, 2022. "A Model of the Fed's View on Inflation," The Review of Economics and Statistics, MIT Press, vol. 104(4), pages 686-704, October.
    15. Chan, Joshua C.C., 2013. "Moving average stochastic volatility models with application to inflation forecast," Journal of Econometrics, Elsevier, vol. 176(2), pages 162-172.
    16. Ben Zeev, Nadav, 2018. "What can we learn about news shocks from the late 1990s and early 2000s boom-bust period?," Journal of Economic Dynamics and Control, Elsevier, vol. 87(C), pages 94-105.
    17. Christian Kascha & Francesco Ravazzolo, 2010. "Combining inflation density forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 231-250.
    18. Roberto Duncan & Enrique Martínez‐García, 2023. "Forecasting inflation in open economies: What can a NOEM model do?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 481-513, April.
    19. Choi, Chi-Young & O'Sullivan, Róisín, 2013. "Heterogeneous response of disaggregate inflation to monetary policy regime change: The role of price stickiness," Journal of Economic Dynamics and Control, Elsevier, vol. 37(9), pages 1814-1832.
    20. Bradley, Michael D. & Jansen, Dennis W. & Sinclair, Tara M., 2015. "How Well Does “Core” Inflation Capture Permanent Price Changes?," Macroeconomic Dynamics, Cambridge University Press, vol. 19(4), pages 791-815, June.

    More about this item

    Keywords

    attention; internet search; Google; monetary policy; ECB; FED; international financial markets; macro-finance; sovereign bonds; international finance; bond markets; preferred habitat models.;
    All these keywords.

    JEL classification:

    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

    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:bbk:bbkefp:1804. 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: the person in charge (email available below). General contact details of provider: https://www.bbk.ac.uk/departments/ems/ .

    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.