Non‐linear Dsge Models and the Central Difference Kalman Filter

Andreasen, Martin M.

doi:10.1002/jae.2282

Cited by 39 publications

(17 citation statements)

References 39 publications

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“…This paper is complementary to Andreasen (2012) and Ivashchenko (2014) who also develop deterministic filters for second-order approximated DSGE models, and show that those filters can outperform particle filters. These authors too assume linear updating rules.…”

Section: Introductionmentioning

confidence: 70%

“…Ivashchenko (2014) also applies two other deterministic filters to second-order approximated DSGE models: a Central Difference Kalman filter (Norgaard et al 2000) and an Unscented Kalman filter (Julier and Uhlmann 2004); these filters are based on different deterministic numerical integration schemes for computing one-step-ahead conditional moments (no analytical closed-form expressions). Andreasen (2012) estimates a DSGE model using a Central Difference Kalman filter.…”

Section: Model Format and Second-order Solutionmentioning

confidence: 99%

“…The linear updating formula (11) would be an exact algorithm for computing the conditional expecta-7 Linear updating rules are likewise assumed by Andreasen (2012) and Ivashchenko (2014) who also develop deterministic filters for second-order approximated DSGE models (see above). 8 It is assumed that the inverse of V Z t+1,t + ψ (covariance matrix of prediction errors of observables) exists.…”

Section: Observation Equationmentioning

confidence: 99%

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Tractable Latent State Filtering for Non-Linear DSGE Models Using a Second-Order Approximation and Pruning

Kollmann

2014

Comput Econ

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This paper develops a novel approach for estimating latent state variables of Dynamic Stochastic General Equilibrium (DSGE) models that are solved using a second-order accurate approximation. I apply the Kalman filter to a state-space representation of the second-order solution based on the 'pruning' scheme of Kim et al. (J Econ Dyn Control 32:3397-3414, 2008). By contrast to particle filters, no stochastic simulations are needed for the deterministic filter here; the present method is thus much faster; in terms of estimation accuracy for latent states it is competitive with the standard particle filter. Use of the pruning scheme distinguishes the filter here from the deterministic Quadratic Kalman filter presented by Ivashchenko (Comput Econ, 43:71-82, 2014). The filter here performs well even in models with big shocks and high curvature. R. Kollmann (B)

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Section: Introductionmentioning

confidence: 70%

Section: Model Format and Second-order Solutionmentioning

confidence: 99%

Section: Observation Equationmentioning

confidence: 99%

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Tractable Latent State Filtering for Non-Linear DSGE Models Using a Second-Order Approximation and Pruning

Kollmann

2014

Comput Econ

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“…This returns us to the familiar linear-Gaussian world, however the approximation is only valid in a local neighborhood of the point that it was approximated around. Comparisons of the EKF with Sigma Point Filters indicate that the EKF generally does quite poorly, and for this reason we will not discuss the EKF further (see Andreasen, 2013).…”

Section: State Updatementioning

confidence: 99%

“…Andreasen (2013) shows with a simple DSGE model solved using both a second and third order perturbation method that the Divided Difference Filter provides more accurate results than the Particle Filter using 500,000 particles. Kollmann (2015) is also able to beat a Particle Filter that uses 500,000 particles with a deterministic Kalman Filter adapted for second order approximations in pruned state space (the KalmanQ Filter).…”

Section: Introductionmentioning

confidence: 99%

Sigma Point Filters for Dynamic Nonlinear Regime Switching Models

Binning¹,

Maih²

2015

SSRN Journal

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In this paper we take three well known Sigma Point Filters, namely the Unscented Kalman Filter, the Divided Difference Filter, and the Cubature Kalman Filter, and extend them to allow for a very general class of dynamic nonlinear regime switching models. Using both a Monte Carlo study and real data, we investigate the properties of our proposed filters by using a regime switching DSGE model solved using nonlinear methods. We find that the proposed filters perform well. They are both fast and reasonably accurate, and as a result they will provide practitioners with a convenient alternative to Sequential Monte Carlo methods. We also investigate the concept of observability and its implications in the context of the nonlinear filters developed and propose some heuristics. Finally, we provide in the RISE toolbox, the codes implementing these three novel filters.

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Likelihood evaluation of models with occasionally binding constraints

Cuba‐Borda

Guerrieri

Iacoviello

et al. 2019

J of Applied Econometrics

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Applied researchers interested in estimating key parameters of DSGE models face an array of choices regarding numerical solution and estimation methods. We focus on the likelihood evaluation of models with occasionally binding constraints. We document how solution approximation errors and likelihood misspecification, related to the treatment of measurement errors, can interact and compound each other.

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Non‐linear Dsge Models and the Central Difference Kalman Filter

Cited by 39 publications

References 39 publications

Tractable Latent State Filtering for Non-Linear DSGE Models Using a Second-Order Approximation and Pruning

Tractable Latent State Filtering for Non-Linear DSGE Models Using a Second-Order Approximation and Pruning

Sigma Point Filters for Dynamic Nonlinear Regime Switching Models

Likelihood evaluation of models with occasionally binding constraints

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