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Recovering Stars in Macroeconomics

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  • Daniel Buncic
  • Adrian Pagan
  • Tim Robinson

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," CAMA Working Papers 2023-43, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    • Handle: RePEc:een:camaaa:2023-43
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    References listed on IDEAS

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    1. Daniel Buncic, 2021. "On a Standard Method for Measuring the Natural Rate of Interest," Papers 2103.16452, arXiv.org, revised Apr 2022.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. McDonald, John & Darroch, John, 1983. "Consistent estimation of equations with composite moving average disturbance terms," Journal of Econometrics, Elsevier, vol. 23(2), pages 253-267, October.
    7. 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.
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    Cited by:

    1. Buncic, Daniel, 2024. "Econometric issues in the estimation of the natural rate of interest," Economic Modelling, Elsevier, vol. 132(C).

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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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