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News Shocks under Financial Frictions: A comment on Görtz et al. (2022)

Author

Listed:
  • Ash, Thomas
  • Nikolaishvili, Giorgi
  • Struby, Ethan

Abstract

Görtz et al. (2022) estimate the effects of innovations to future total factor productivity (TFP) on financial markets. In a Bayesian vector autoregression, they identify a TFP news shock as one that explains the largest share of 40- quarter ahead forecast error variance (FEV) of TFP. Their estimated impulse responses functions show that a positive news shock significantly decreases credit market spreads and increases credit market supply. They also find that a shock that explains the maximum of the FEV of the "excess bond premium" (EBP) (Gilchrist and Zakrajsek 2012) causes similar responses. These results are consistent with an estimated DSGE model with financial frictions. We estimate the main IRFs of the study using the original data and a frequentist estimation approach. We obtain similar point estimates for the dynamic responses to TFP news and EBP max-share shocks. We also update their macroeconomic and financial time series, as some of the data has been revised substantially since their original estimate. We use the updated data to re-estimate the above-mentioned IRFs, and we find that the results are robust to this change in the data. Finally, we investigate the computational reproducibility of their DSGE results, and find that their provided code (consistent with warnings in their README file) does not execute in the most recent version of Dynare or Matlab. Using the version indicated in their replication files, we encounter issues estimating the posterior mode.

Suggested Citation

  • Ash, Thomas & Nikolaishvili, Giorgi & Struby, Ethan, 2023. "News Shocks under Financial Frictions: A comment on Görtz et al. (2022)," I4R Discussion Paper Series 51, The Institute for Replication (I4R).
    • Handle: RePEc:zbw:i4rdps:51
    as

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    References listed on IDEAS

    as
    1. André Kurmann & Eric Sims, 2021. "Revisions in Utilization-Adjusted TFP and Robust Identification of News Shocks," The Review of Economics and Statistics, MIT Press, vol. 103(2), pages 216-235, May.
    2. Neville Francis & Michael T. Owyang & Jennifer E. Roush & Riccardo DiCecio, 2014. "A Flexible Finite-Horizon Alternative to Long-Run Restrictions with an Application to Technology Shocks," The Review of Economics and Statistics, MIT Press, vol. 96(4), pages 638-647, October.
    3. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015. "Prior Selection for Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 436-451, May.
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    More about this item

    Keywords

    Replication; News Shocks; Financial Frictions; Excess Bond Premium;
    All these keywords.

    JEL classification:

    • E12 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Keynes; Keynesian; Post-Keynesian; Modern Monetary Theory
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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