IDEAS home Printed from https://ideas.repec.org/p/bdr/borrec/1184.html
   My bibliography  Save this paper

Unraveling the Exogenous Forces Behind Analysts’ Macroeconomic Forecasts

Author

Listed:
  • Marcela De Castro-Valderrama
  • Santiago Forero-Alvarado
  • Nicolás Moreno-Arias
  • Sara Naranjo-Saldarriaga

Abstract

Modern macroeconomics focuses on the identification of the primitive exogenous forces generating business cycles. This is at odds with macroeconomic forecasts collected through surveys, which are about endogenous variables. To address this divorce, our paper uses a semi-structural general equilibrium model as a multivariate filter to infer the shocks behind economic analysts’ forecasts and thus, unravel their implicit macroeconomic stories. By interpreting all analysts’ forecasts through the same lenses, it is possible to understand the differences between projected endogenous variables as differences in the types and magnitudes of shocks. It also allows to explain market’s uncertainty about the future in terms of analysts’ disagreement about these shocks. The usefulness of the approach is illustrated by adapting the canonical SOE semi-structural model in Carabenciov et al. (2008a) to Colombia and then using it to filter forecasts of its Central Bank’s Monthly Expectations Survey during the COVID-19 crisis. **** RESUMEN: La macroeconomía actualmente se centra en la identificación de las fuerzas exógenas primitivas que generan los ciclos económicos reales. En contraste, las encuestas macroeconómicas recogen pronósticos sobre variables endógenas. Con el fin de reconciliar este divorcio, este trabajo usa un modelo semi-estructural de equilibrio general como un filtro multivariado para inferir los choques que estarían detrás de los pronósticos de los analistas de mercado y, por ende, desvelar sus historias macroeconómicas implícitas. Al interpretar los pronósticos de todos los analistas a través de los mismos lentes, es posible entender las diferencias entre las variables endógenas proyectadas a partir de las diferencias en los tipos y magnitudes de los choques implícitos en ellas. Del mismo modo, la incertidumbre del mercado respecto al futuro de la economía puede ser explicada en términos del desacuerdo de los analistas frente a estos choques. La utilidad de este enfoque es ilustrada mediante un caso de estudio, en el cual se adapta a Colombia el modelo semi-estructural canónico de Carabenciov et al. (2008a) para una economía pequeña y abierta, y se utiliza luego para filtrar los pronósticos registrados en la Encuesta Mensual de Expectativas del Banco de la República durante la crisis de la COVID-19.

Suggested Citation

  • Marcela De Castro-Valderrama & Santiago Forero-Alvarado & Nicolás Moreno-Arias & Sara Naranjo-Saldarriaga, 2021. "Unraveling the Exogenous Forces Behind Analysts’ Macroeconomic Forecasts," Borradores de Economia 1184, Banco de la Republica de Colombia.
  • Handle: RePEc:bdr:borrec:1184
    DOI: 10.32468/be.1184
    as

    Download full text from publisher

    File URL: https://doi.org/10.32468/be.1184
    Download Restriction: no

    File URL: https://libkey.io/10.32468/be.1184?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
    ---><---

    References listed on IDEAS

    as
    1. KONISHI Yoko & NISHIYAMA Yoshihiko, 2013. "Decomposition of Supply and Demand Shocks in the Production Function using the Current Survey of Production," Discussion papers 13003, Research Institute of Economy, Trade and Industry (RIETI).
    2. Olivier Coibion & Yuriy Gorodnichenko, 2012. "What Can Survey Forecasts Tell Us about Information Rigidities?," Journal of Political Economy, University of Chicago Press, vol. 120(1), pages 116-159.
    3. Oscar Claveria & Enric Monte & Salvador Torra, 2021. "Frequency domain analysis and filtering of business and consumer survey expectations," International Economics, CEPII research center, issue 166, pages 42-57.
    4. N. Gregory Mankiw & Ricardo Reis, 2002. "Sticky Information versus Sticky Prices: A Proposal to Replace the New Keynesian Phillips Curve," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 117(4), pages 1295-1328.
    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. Andrew J. Patton & Allan Timmermann, 2011. "Predictability of Output Growth and Inflation: A Multi-Horizon Survey Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(3), pages 397-410, July.
    7. Anmol Bhandari & Jaroslav Borovička & Paul Ho, 2016. "Identifying Ambiguity Shocks in Business Cycle Models Using Survey Data," NBER Working Papers 22225, National Bureau of Economic Research, Inc.
    8. Jennifer Castle & David Hendry, 2016. "Policy Analysis, Forediction, and Forecast Failure," Economics Series Working Papers 809, University of Oxford, Department of Economics.
    9. Ms. Luisa Charry & Pranav Gupta & Mr. Vimal V Thakoor, 2014. "Introducing a Semi-Structural Macroeconomic Model for Rwanda," IMF Working Papers 2014/159, International Monetary Fund.
    10. Fuhrer, Jeffrey C, 1988. "On the Information Content of Consumer Survey Expectations," The Review of Economics and Statistics, MIT Press, vol. 70(1), pages 140-144, February.
    11. Francesca Monti, 2010. "Combining Judgment and Models," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(8), pages 1641-1662, December.
    12. Michal Andrle & Mr. Andrew Berg & Rogelio Morales & Mr. Rafael A Portillo & Mr. Jan Vlcek, 2013. "Forecasting and Monetary Policy Analysis in Low-Income Countries: Food and non-Food Inflation in Kenya," IMF Working Papers 2013/061, International Monetary Fund.
    13. Patton, Andrew J. & Timmermann, Allan, 2010. "Why do forecasters disagree? Lessons from the term structure of cross-sectional dispersion," Journal of Monetary Economics, Elsevier, vol. 57(7), pages 803-820, October.
    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. George-Marios Angeletos, 2018. "Frictional Coordination," Journal of the European Economic Association, European Economic Association, vol. 16(3), pages 563-603.
    2. Michael P Clements, 2014. "Assessing the Evidence of Macro- Forecaster Herding: Forecasts of Inflation and Output Growth," ICMA Centre Discussion Papers in Finance icma-dp2014-12, Henley Business School, University of Reading.
    3. Hollmayr, Josef & Kühl, Michael, 2019. "Learning about banks’ net worth and the slow recovery after the financial crisis," Journal of Economic Dynamics and Control, Elsevier, vol. 109(C).
    4. Chini, Emilio Zanetti, 2023. "Can we estimate macroforecasters’ mis-behavior?," Journal of Economic Dynamics and Control, Elsevier, vol. 149(C).
    5. Michael P. Clements, 2014. "US Inflation Expectations and Heterogeneous Loss Functions, 1968–2010," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(1), pages 1-14, January.
    6. Martin Geiger & Johann Scharler, 2021. "How Do People Interpret Macroeconomic Shocks? Evidence from U.S. Survey Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(4), pages 813-843, June.
    7. Paul Hubert, 2014. "FOMC Forecasts as a Focal Point for Private Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(7), pages 1381-1420, October.
    8. Zhao Han & Xiaohan Ma & Ruoyun Mao, 2023. "The Role of Dispersed Information in Inflation and Inflation Expectations," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 48, pages 72-106, April.
    9. Bartosz Maćkowiak & Filip Matějka & Mirko Wiederholt, 2023. "Rational Inattention: A Review," Journal of Economic Literature, American Economic Association, vol. 61(1), pages 226-273, March.
    10. An, Zidong & Zheng, Xinye, 2023. "Diligent forecasters can make accurate predictions despite disagreeing with the consensus," Economic Modelling, Elsevier, vol. 125(C).
    11. Conrad, Christian & Lahiri, Kajal, 2024. "Heterogeneous Expectations among Professional Forecasters," Working Papers 0754, University of Heidelberg, Department of Economics.
    12. George‐Marios Angeletos & Fabrice Collard & Harris Dellas, 2018. "Quantifying Confidence," Econometrica, Econometric Society, vol. 86(5), pages 1689-1726, September.
    13. Robert Rich & Joseph Tracy, 2021. "A Closer Look at the Behavior of Uncertainty and Disagreement: Micro Evidence from the Euro Area," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(1), pages 233-253, February.
    14. 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.
    15. Meade, Nigel & Driver, Ciaran, 2023. "Differing behaviours of forecasters of UK GDP growth," International Journal of Forecasting, Elsevier, vol. 39(2), pages 772-790.
    16. Kim, Insu & Kim, Young Se, 2019. "Inattentive agents and inflation forecast error dynamics: A Bayesian DSGE approach," Journal of Macroeconomics, Elsevier, vol. 62(C).
    17. Raffaella Giacomini & Barbara Rossi, 2015. "Forecasting in Nonstationary Environments: What Works and What Doesn't in Reduced-Form and Structural Models," Annual Review of Economics, Annual Reviews, vol. 7(1), pages 207-229, August.
    18. Tomasz Łyziak & Xuguang Simon Sheng, 2023. "Disagreement in Consumer Inflation Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(8), pages 2215-2241, December.
    19. Benhima, Kenza, 2019. "Booms and busts with dispersed information," Journal of Monetary Economics, Elsevier, vol. 107(C), pages 32-47.
    20. Jean-Paul L’Huillier & Sanjay R Singh & Donghoon Yoo, 2024. "Incorporating Diagnostic Expectations into the New Keynesian Framework," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 91(5), pages 3013-3046.

    More about this item

    Keywords

    Expectativas macroeconómicas; pronósticos profesionales; Modelo semi-structural; Suavizado de Kalman; Expectativas de encuestas; Macroeconomic expectations; Professional forecasters; Semi-structural model; Kalman smoother; Survey expectations.;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - 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:bdr:borrec:1184. 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: Clorith Angélica Bahos Olivera (email available below). General contact details of provider: https://edirc.repec.org/data/brcgvco.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.