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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.

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  • 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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    Cited by:

    1. Gregor Boehl & Cars Hommes, 2021. "Rational vs. Irrational Beliefs in a Complex World," CRC TR 224 Discussion Paper Series crctr224_2021_287, University of Bonn and University of Mannheim, Germany.
    2. S. Bogan Aruoba & Pablo Cuba-Borda & Kenji Higa-Flores & Frank Schorfheide & Sergio Villalvazo, . "Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics.
    3. Hirokuni Iiboshi & Mototsugu Shintani & Kozo Ueda, 2018. "Estimating a nonlinear new Keynesian model with the zero lower bound for Japan," CAMA Working Papers 2018-37, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    4. 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.
    5. Böhl, Gregor & Goy, Gavin & Strobel, Felix, 2020. "A structural investigation of quantitative easing," IMFS Working Paper Series 142, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    6. Daisuke Ikeda & Shangshang Li & Sophocles Mavroeidis & Francesco Zanetti, 2020. "Testing the Effectiveness of Unconventional Monetary Policy in Japan and the United States," IMES Discussion Paper Series 20-E-10, Institute for Monetary and Economic Studies, Bank of Japan.
    7. 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.
    8. S. Bogan Aruoba & Pablo Cuba-Borda & Kenji Higa-Flores & Frank Schorfheide & Sergio Villalvazo, . "Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics.
    9. Böhl, Gregor & Strobel, Felix, 2020. "US business cycle dynamics at the zero lower bound," Discussion Papers 65/2020, Deutsche Bundesbank.
    10. Gregor Boehl & Felix Strobel, 2020. "US Business Cycle Dynamics at the Zero Lower Bound," CRC TR 224 Discussion Paper Series crctr224_2020_192, University of Bonn and University of Mannheim, Germany.
    11. Daisuke Ida & Hirokuni Iiboshi, 2021. "The interaction of forward guidance in a two-country new Keynesian model," Papers 2103.12503, arXiv.org, revised Apr 2021.
    12. Böhl, Gregor & Goy, Gavin & Strobel, Felix, 2021. "A structural investigation of quantitative easing," Discussion Papers 01/2021, Deutsche Bundesbank.
    13. 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.
    14. 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).
    15. 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 (Ispra site).
    16. Gregor Boehl & Gavin Goy & Felix Strobel, 2020. "A Structural Investigation of Quantitative Easing," DNB Working Papers 691, Netherlands Central Bank, Research Department.

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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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