How to Maximize the Likelihood Function for a DSGE Model

Andreasen, Martin M.

doi:10.1007/s10614-009-9182-6

Cited by 32 publications

(20 citation statements)

References 22 publications

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“…; subject to the budget and resource constraint in (2) and 3, and the dynamics of the capital stock in (4). 15 We …nd the …rst-order conditions of the household with respect to consumption, labor, capital, and investment:…”

Section: Augmented Equilibrium Conditionsmentioning

confidence: 99%

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The Term Structure of Interest Rates in a DSGE Model with Recursive Preferences

et al. 2011

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a b s t r a c tA dynamic stochastic general equilibrium (DSGE) model in which households have Epstein and Zin recursive preferences is solved with perturbation. The parameters governing preferences and technology are estimated by maximum likelihood using macroeconomic data and the term structure of interest rates. The estimates imply a large risk aversion, an elasticity of intertemporal substitution higher than one, and substantial adjustment costs. Furthermore, the paper identifies the tensions within the model by estimating it on subsets of these data. The analysis concludes by pointing out potential extensions that may improve the model's fit.

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Section: Augmented Equilibrium Conditionsmentioning

confidence: 99%

“…This is particularly easy to see if we concentrate on the consumption process that drives the stochastic discount factor under recursive preferences. Except in a few papers, 2 researchers interested in asset pricing have studied economies in which consumption follows an exogenous process. This is a potentially important shortcoming.…”

Section: Introductionmentioning

confidence: 99%

The Term Structure of Interest Rates in a DSGE Model with Recursive Preferences

et al. 2011

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“…We instead use the covariance matrix adaption evolutionary strategy (CMA-ES) to obtain the maximum-likelihood estimates of . This optimization algorithm is designed to cope with objective functions that are non-linear, non-convex, rugged, and multimodal (Hansen, 2011;Andreasen, 2010).…”

Section: The Empirical Modelmentioning

confidence: 99%

Modeling changes in US monetary policy with a time-varying nonlinear Taylor rule

Nguyen

Pavlidis

Peel

2018

Studies in Nonlinear Dynamics &Amp; Econometrics

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The monetary economics literature has highlighted four issues that are important in evaluating US monetary policy since the late 1960s: (i) time variation in policy parameters, (ii) asymmetric preferences, (iii) real-time nature of data, and (iv) heteroskedasticity. In this paper, we exploit advances in sequential monte carlo methods to estimate a time-varying nonlinear Taylor rule that addresses these four issues simultaneously. Our findings suggest that US monetary policy has experienced substantial changes in terms of both the response to inflation and to real economic activity, as well as changes in preferences. These changes cannot be captured adequately by a single structural break at the late 1970s, as has been commonly assumed in the literature, and play a non-trivial role in economic performance.

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“…For small models, local optimization routines such as the Newton–Raphson method and its various extensions may be used with different starting values. For larger models, global optimization routines such as simulated annealing and evolutionary algorithms may be more effective (see Andreasen, ). In comparison, the estimated log‐likelihood function in most particle filters does not display smoothness in θ due to the resampling step and this makes the optimization very challenging…”

Section: Quasi Maximum Likelihood Estimationmentioning

confidence: 99%

Non‐linear Dsge Models and the Central Difference Kalman Filter

Andreasen

2012

J of Applied Econometrics

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SUMMARY This paper introduces a quasi maximum likelihood approach based on the central difference Kalman filter to estimate non‐linear dynamic stochastic general equilibrium (DSGE) models with potentially non‐Gaussian shocks. We argue that this estimator can be expected to be consistent and asymptotically normal for DSGE models solved up to third order. These properties are verified in a Monte Carlo study for a DSGE model solved to second and third order with structural shocks that are Gaussian, Laplace distributed, or display stochastic volatility. Copyright © 2012 John Wiley & Sons, Ltd.

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How to Maximize the Likelihood Function for a DSGE Model

Abstract: CMA-ES optimization routine, Multimodel objective function, Nelder–Mead simplex routine, Non-convex search space, Resampling, Simulated Annealing, C61, C88, E30,

Cited by 32 publications

References 22 publications

The Term Structure of Interest Rates in a DSGE Model with Recursive Preferences

The Term Structure of Interest Rates in a DSGE Model with Recursive Preferences

Modeling changes in US monetary policy with a time-varying nonlinear Taylor rule

Non‐linear Dsge Models and the Central Difference Kalman Filter

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