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Recovering stars in macroeconomics

Author

Listed:
  • Daniel Buncic
  • Adrian Pagan
  • Tim Robinson

    (Melbourne Institute: Applied Economic & Social Research, The University of Melbourne)

Abstract

Many key macroeconomic variables such as the NAIRU, potential GDP, and the neutral real rate of interest—which are needed for policy analysis—are latent. Collectively, these latent variables are known as ‘stars’ and are typically estimated using the Kalman filter or smoother from models that can be expressed in State Space form. When these models contain more shocks than observed variables, they are ‘short’, and potentially create issues in recovering the star variable of interest from the observed data. Recovery issues can occur when the model is correctly specified and its parameters are known. In this paper, we summarize the literature on shock recovery and demonstrate its implications for estimating stars in a number of widely used models in policy analysis. The ability of many popular and recent models to recover stars is shown to be limited. We suggest ways this can be addressed.

Suggested Citation

  • Daniel Buncic & Adrian Pagan & Tim Robinson, 2023. "Recovering stars in macroeconomics," Melbourne Institute Working Paper Series wp2023n12, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    • Handle: RePEc:iae:iaewps:wp2023n12
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    References listed on IDEAS

    as
    1. Xianglong Liu & Adrian R. Pagan & Tim Robinson, 2018. "Critically Assessing Estimated DSGE Models: A Case Study of a Multi‐sector Model," The Economic Record, The Economic Society of Australia, vol. 94(307), pages 349-371, December.
    2. Daniel Buncic, 2021. "On a Standard Method for Measuring the Natural Rate of Interest," Papers 2103.16452, arXiv.org, revised Apr 2022.
    3. Robert B. Barsky & Eric R. Sims, 2012. "Information, Animal Spirits, and the Meaning of Innovations in Consumer Confidence," American Economic Review, American Economic Association, vol. 102(4), pages 1343-1377, June.
    4. Ali Alichi & Olivier Bizimana & Mr. Douglas Laxton & Kadir Tanyeri & Hou Wang & Jiaxiong Yao & Fan Zhang, 2017. "Multivariate Filter Estimation of Potential Output for the United States," IMF Working Papers 2017/106, International Monetary Fund.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Kalman filter and smoother; State Space models; shock recovery; short systems; natural rate of interest; macroeconomic policy; Beveridge-Nelson decomposition;
    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
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

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