Optimal discretionary monetary policy in a micro-founded model with a zero lower bound on nominal interest rate

Ngo, Phuong V.

doi:10.1016/j.jedc.2014.05.010

Cited by 24 publications

(23 citation statements)

References 43 publications

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“…Next, given the estimated parameters, we solve the model in a fully nonlinear and stochastic setting with the ZLB and apply a nonlinear filter to a full sample to extract the sequence of the natural interest rate. The literature (e.g., Boneva, Braun, and Waki, 2016;Fernández-Villaverde, Gordon, Guerrón-Quintana, and Rubio-Ramírez, 2015;Gavin, Keen, Richter, and Throckmorton, 2015;Gust, Herbst, López-Salido, and Smith, 2017;Nakata, 2016Nakata, , 2017Ngo, 2014;and Richter and Throckmorton, 2016a) has emphasized the importance of considering nonlinearity in assessing the quantitative implications of New Keynesian models that include the ZLB. The natural rate estimated in the present paper takes account of this important feature.…”

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confidence: 99%

The Natural Rate of Interest in a Nonlinear DSGE Model

Hirose

Sunakawa

2017

SSRN Journal

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This paper investigates how and to what extent nonlinearities, including the zero lower bound on the nominal interest rate, affect the estimate of the natural rate of interest in a dynamic stochastic general equilibrium model with sticky prices and wages. The estimated natural rate of interest in a nonlinear model is substantially different from that in its linear counterpart because of a contractionary effect of the zero lower bound. Price and wage dispersion, from which a linear model abstracts, play a minor role in identifying the natural rate.

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confidence: 99%

The Natural Rate of Interest in a Nonlinear DSGE Model

Hirose

Sunakawa

2017

SSRN Journal

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“…However, with the increased probability of hitting the ZLB or the longer duration of ZLB spells, the parameter biases become large and substantially affect the dynamic properties of the model. Moreover, they demonstrate that the estimation of the model omitting the ZLB leads to biased estimates of the structural shocks, even though the estimated 27 See, for instance, Eggertsson and Woodford (2003), Jung et al (2005), Adam andBilli (2006, 2007) , Gavin et al (2015), Nakata (2016Nakata ( , 2017 and Ngo (2014). 28 The model is solved using the algorithm employed by Erceg and Lind e (2014) and Bodenstein et al (2013).…”

Section: Necessity For Nonlinear Estimationmentioning

confidence: 99%

Review of Solution and Estimation Methods for Nonlinear Dynamic Stochastic General Equilibrium Models with the Zero Lower Bound

Hirose

Sunakawa

2019

Jpn Econ Rev

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We review solution and estimation methods for nonlinear dynamic stochastic general equilibrium models and their application, with a special focus on the zero lower bound on the nominal interest rate. In a fully nonlinear setting, both the solution and estimation methods involve iterative procedures, and their computational expense grows rapidly with an increase in the dimensionality of state variables and parameters. We describe how the procedures deal with the dimensionality problem.

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“…2 See Fernández-Villaverde et al (2016) for a detailed overview of the various solution and estimation methods. 3 Several earlier papers study the effects of the ZLB constraint in calibrated nonlinear models using solution methods similar to the one for NL-PF (Fernández-Villaverde et al (2015); Gavin et al (2015); Keen et al (2017); Mertens and Ravn (2014); Nakata (2017); Nakov (2008); Ngo (2014); Richter and Throckmorton (2015); Wolman (2005)).…”

Section: Introductionmentioning

confidence: 99%

The zero lower bound and estimation accuracy

Atkinson

Richter

Throckmorton

2020

Journal of Monetary Economics

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This paper evaluates the accuracy of linear and nonlinear estimation methods for dynamic stochastic general equilibrium models. We generate a large sample of artificial datasets using a global solution to a nonlinear New Keynesian model with an occasionally binding zero lower bound (ZLB) constraint on the nominal interest rate. For each dataset, we estimate the nonlinear model-solved globally, accounting for the ZLB-and the linear analogue of the nonlinear model-solved locally, ignoring the ZLB-with a Metropolis-Hastings algorithm where the likelihood function is evaluated with a Kalman filter, unscented Kalman filter, or particle filter. In datasets that resemble the U.S. experience, the nonlinear model estimated with a particle filter is more accurate and has a higher marginal data density than the linear model estimated with a Kalman filter, as long as the measurement error variances in the particle filter are not too big.

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Optimal discretionary monetary policy in a micro-founded model with a zero lower bound on nominal interest rate

Cited by 24 publications

References 43 publications

The Natural Rate of Interest in a Nonlinear DSGE Model

The Natural Rate of Interest in a Nonlinear DSGE Model

Review of Solution and Estimation Methods for Nonlinear Dynamic Stochastic General Equilibrium Models with the Zero Lower Bound

The zero lower bound and estimation accuracy

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