Analysis of Numerical Errors

Peralta-Alva, Adrian; Santos, Manuel S.

doi:10.1016/b978-0-444-52980-0.00009-8

Cited by 4 publications

(2 citation statements)

References 64 publications

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“…This work contrasts with the analysis provided in(Judd et al, 2017a;Peralta-Alva and Santos, 2014;Santos and Peralta-alva, 2005;Santos, 2000). Their approaches identify Euler Equation Errors, but use statistical techniques to estimate the impact of these errors on solution accuracy.…”

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confidence: 97%

Reliably Computing Nonlinear Dynamic Stochastic Model Solutions: An Algorithm with Error Formulas

Anderson

2018

FEDS

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This paper provides a new technique for representing discrete time nonlinear dynamic stochastic time invariant maps. Using this new series representation, the paper shows how to augment a solution strategy based on projection methods with an additional set of constraints thereby enhancing its reliability. The paper also provides general formulas for evaluating the accuracy of proposed solutions. The technique can readily accommodate models with occasionally binding constraints and regime switching. The algorithm uses Smolyak polynomial function approximation in a way which makes it possible to exploit a high degree of parallelism.

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confidence: 97%

Reliably Computing Nonlinear Dynamic Stochastic Model Solutions: An Algorithm with Error Formulas

Anderson

2018

FEDS

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“…This is related to the accumulation of errors studied in the approximation of DSGE models (see e.g Peralta-Alva & Santos, 2014)…”

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confidence: 99%

A Sieve-SMM Estimator for Dynamic Models

Forneron¹

2019

Preprint

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This paper proposes a Sieve Simulated Method of Moments (Sieve-SMM) estimator for the parameters and the distribution of the shocks in nonlinear dynamic models where the likelihood and the moments are not tractable. An important concern with SMM, which matches sample with simulated moments, is that a parametric distribution is required but economic quantities that depend on this distribution, such as welfare and asset-prices, can be sensitive to misspecification. The Sieve-SMM estimator addresses this issue by flexibly approximating the distribution of the shocks with a Gaussian and tails mixture sieve. The asymptotic framework provides consistency, rate of convergence and asymptotic normality results, extending existing sieve estimation theory to a new framework with more general dynamics and latent variables. Monte-Carlo simulations illustrate the finite sample properties of the estimator. Two empirical applications highlight the importance of the distribution of the shocks for estimates and counterfactuals.

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Forecasting with Second-Order Approximations and Markov-Switching DSGE Models

et al. 2019

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This paper considers the out-of-sample forecasting performance of first-and secondorder perturbation approximations for DSGE models that incorporate Markov-switching behaviour in the policy reaction function and the volatility of shocks. The results suggest that second-order approximations provide an improved forecasting performance in models that do not allow for regime-switching, while for the MS-DSGE models, a first-order approximation would appear to provide better out-of-sample properties. In addition, we find that over short-horizons, the MS-DSGE models provide superior forecasting results when compared to those models that do not allow for regime-switching (at both perturbation orders).

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Analysis of Numerical Errors

Cited by 4 publications

References 64 publications

Reliably Computing Nonlinear Dynamic Stochastic Model Solutions: An Algorithm with Error Formulas

Reliably Computing Nonlinear Dynamic Stochastic Model Solutions: An Algorithm with Error Formulas

A Sieve-SMM Estimator for Dynamic Models

Forecasting with Second-Order Approximations and Markov-Switching DSGE Models

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