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The Zero Lower Bound and Estimation Accuracy

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Abstract
During the Great Recession, many central banks lowered their policy rate to its zero lower bound (ZLB), creating a kink in the policy rule and calling into question linear estimation methods. There are two promising alternatives: estimate a fully nonlinear model that accounts for precautionary savings effects of the ZLB or a piecewise linear model that is much faster but ignores the precautionary savings effects. Repeated estimation with artificial datasets reveals some advantages of the nonlinear model, but they are not large enough to justify the longer estimation time, regardless of the ZLB duration in the data. Misspecification of the estimated models has a much larger impact on accuracy. It biases the parameter estimates and creates significant differences between the predictions of the models and the data generating process.

Suggested Citation

  • Tyler Atkinson & Alexander W. Richter & Nathaniel A. Throckmorton, 2018. "The Zero Lower Bound and Estimation Accuracy," Working Papers 1804, Federal Reserve Bank of Dallas.
    • Handle: RePEc:fip:feddwp:1804
    DOI: 10.24149/wp1804r1
    Note: A previous version of this paper circulated with the title, "The Accuracy of Linear and Nonlinear Estimation in the Presence of the Zero Lower Bound."
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    7. Böhl, Gregor & Strobel, Felix, 2020. "US business cycle dynamics at the zero lower bound," IMFS Working Paper Series 143, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    8. Damioli, Giacomo & Gregori, Wildmer Daniel, 2021. "Diplomatic relations and cross-border investments in the European Union," Working Papers 2021-02, Joint Research Centre, European Commission.
    9. Eric Jondeau & Grégory Levieuge & Jean-Guillaume Sahuc & Gauthier Vermandel, 2023. "Environmental Subsidies to Mitigate Net-Zero Transition Costs," Working papers 910, Banque de France.
    10. Eric Jondeau & Grégory Levieuge & Jean-Guillaume Sahuc & Gauthier Vermandel, 2022. "Environmental Subsidies to Mitigate Transition risk," EconomiX Working Papers 2022-21, University of Paris Nanterre, EconomiX.
    11. Ida, Daisuke & Iiboshi, Hirokuni, 2021. "The interaction of forward guidance in a two-country new Keynesian model," MPRA Paper 106752, University Library of Munich, Germany.
    12. Hirokuni Iiboshi & Mototsugu Shintani & Kozo Ueda, 2022. "Estimating a Nonlinear New Keynesian Model with the Zero Lower Bound for Japan," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(6), pages 1637-1671, September.
    13. Pablo Cuba‐Borda & Luca Guerrieri & Matteo Iacoviello & Molin Zhong, 2019. "Likelihood evaluation of models with occasionally binding constraints," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1073-1085, November.
    14. Higgins, C. Richard, 2023. "Risk and Uncertainty: The Role of Financial Frictions," Economic Modelling, Elsevier, vol. 119(C).
    15. Yoichiro Tamanyu, 2020. "The Role of Nonlinearity in Indeterminate Models: An Application to Expectations-Driven Liquidity Traps," Keio-IES Discussion Paper Series 2020-023, Institute for Economics Studies, Keio University.
    16. Giovannini, Massimo & Pfeiffer, Philipp & Ratto, Marco, 2021. "Efficient and robust inference of models with occasionally binding constraints," Working Papers 2021-03, Joint Research Centre, European Commission.
    17. Ivashchenko, Sergey & Mutschler, Willi, 2020. "The effect of observables, functional specifications, model features and shocks on identification in linearized DSGE models," Economic Modelling, Elsevier, vol. 88(C), pages 280-292.
    18. Boehl, Gregor, 2022. "Efficient solution and computation of models with occasionally binding constraints," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    19. Daisuke Ida & Hirokuni Iiboshi, 2021. "The international forward guidance transmission under a global liquidity trap," Papers 2103.12503, arXiv.org, revised Aug 2024.
    20. Calo, Silvia & Gregori, Wildmer Daniel & Petracco Giudici, Marco & Rancan, Michela, 2021. "Has the Comprehensive Assessment made the European financial system more resilient?," Working Papers 2021-08, Joint Research Centre, European Commission.
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    More about this item

    Keywords

    Bayesian Estimation; Projection Methods; Particle Filter; OccBin; Inversion Filter;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • 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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects

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