Results 1  10
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108
Bayesian analysis of DSGE models
 ECONOMETRICS REVIEW
, 2007
"... This paper reviews Bayesian methods that have been developed in recent years to estimate and evaluate dynamic stochastic general equilibrium (DSGE) models. We consider the estimation of linearized DSGE models, the evaluation of models based on Bayesian model checking, posterior odds comparisons, and ..."
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Cited by 130 (5 self)
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This paper reviews Bayesian methods that have been developed in recent years to estimate and evaluate dynamic stochastic general equilibrium (DSGE) models. We consider the estimation of linearized DSGE models, the evaluation of models based on Bayesian model checking, posterior odds comparisons, and comparisons to vector autoregressions, as well as the nonlinear estimation based on a secondorder accurate model solution. These methods are applied to data generated from correctly specified and misspecified linearized DSGE models, and a DSGE model that was solved with a secondorder perturbation method. (JEL C11, C32, C51, C52)
Estimating macroeconomic models: a likelihood approach
, 2006
"... This paper shows how particle filtering facilitates likelihoodbased inference in dynamic macroeconomic models. The economies can be nonlinear and/or nonnormal. We describe how to use the output from the particle filter to estimate the structural parameters of the model, those characterizing prefer ..."
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Cited by 102 (27 self)
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This paper shows how particle filtering facilitates likelihoodbased inference in dynamic macroeconomic models. The economies can be nonlinear and/or nonnormal. We describe how to use the output from the particle filter to estimate the structural parameters of the model, those characterizing preferences and technology, and to compare different economies. Both tasks can be implemented from either a classical or a Bayesian perspective. We illustrate the technique by estimating a business cycle model with investmentspecific technological change, preference shocks, and stochastic volatility.
Risk Matters: The Real Effects of Volatility Shocks
, 2009
"... This paper shows how changes in the volatility of the real interest rate at which small open emerging economies borrow have a quantitatively important effect on real variables like output, consumption, investment, and hours worked. To motivate our investigation, we document the strong evidence of ti ..."
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Cited by 70 (6 self)
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This paper shows how changes in the volatility of the real interest rate at which small open emerging economies borrow have a quantitatively important effect on real variables like output, consumption, investment, and hours worked. To motivate our investigation, we document the strong evidence of timevarying volatility in the real interest rates faced by a sample of four emerging small open
Estimating dynamic equilibrium economies: linear versus nonlinear likelihood
 Journal of Applied Econometrics
, 2005
"... This paper compares twomethods for undertaking likelihoodbased inference in dynamic equilibrium economies: a Sequential Monte Carlo filter and the Kalman filter. The Sequential Monte Carlo filter exploits the nonlinear structure of the economy and evaluates the likelihood function of the model by s ..."
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Cited by 42 (13 self)
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This paper compares twomethods for undertaking likelihoodbased inference in dynamic equilibrium economies: a Sequential Monte Carlo filter and the Kalman filter. The Sequential Monte Carlo filter exploits the nonlinear structure of the economy and evaluates the likelihood function of the model by simulation methods. The Kalman filter estimates a linearization of the economy around the steady state. We report two main results. First, both for simulated and for real data, the Sequential Monte Carlo filter delivers a substantially better fit of the model to the data as measured by the marginal likelihood. This is true even for a nearly linear case. Second, the differences in terms of point estimates, although relatively small in absolute values, have important effects on the moments of the model. We conclude that the nonlinear filter is a superior procedure for taking models to the data.
HigherOrder Perturbation Solutions to Dynamic, DiscreteTime Rational Expectations Models,” mimeo, Federal Reserve Bank of San Francisco,
, 2005
"... Abstract We present an algorithm and software routines for computing nthorder approximate solutions to dynamic, discretetime rational expectations models around a nonstochastic steady state. We apply these routines to investigate the optimal monetary policy with commitment in an optimizingagent m ..."
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Cited by 37 (8 self)
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Abstract We present an algorithm and software routines for computing nthorder approximate solutions to dynamic, discretetime rational expectations models around a nonstochastic steady state. We apply these routines to investigate the optimal monetary policy with commitment in an optimizingagent model with nominal price rigidities, subject to a fiscal policy that is stochastic, suboptimal, and exogenous to the central bank.
The Econometrics of DSGE Models
, 2009
"... In this paper, I review the literature on the formulation and estimation of dynamic stochastic general equilibrium (DSGE) models with a special emphasis on Bayesian methods. First, I discuss the evolution of DSGE models over the last couple of decades. Second, I explain why the profession has decide ..."
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Cited by 31 (1 self)
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In this paper, I review the literature on the formulation and estimation of dynamic stochastic general equilibrium (DSGE) models with a special emphasis on Bayesian methods. First, I discuss the evolution of DSGE models over the last couple of decades. Second, I explain why the profession has decided to estimate these models using Bayesian methods. Third, I brie‡y introduce some of the techniques required to compute and estimate these models. Fourth, I illustrate the techniques under consideration by estimating a benchmark DSGE model with real and nominal rigidities. I conclude by o¤ering some pointers for future research.
Computing DSGE Models with Recursive Preferences
, 2009
"... This paper compares different solution methods for computing the equilibrium of dynamic stochastic general equilibrium (DSGE) models with recursive preferences such as those in Epstein and Zin (1989 and 1991). Models with these preferences have recently become popular, but we know little about the b ..."
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Cited by 31 (1 self)
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This paper compares different solution methods for computing the equilibrium of dynamic stochastic general equilibrium (DSGE) models with recursive preferences such as those in Epstein and Zin (1989 and 1991). Models with these preferences have recently become popular, but we know little about the best ways to implement them numerically. To fill this gap, we solve the stochastic neoclassical growth model with recursive preferences using four different approaches: second and thirdorder perturbation, Chebyshev polynomials, and value function iteration. We document the performance of the methods in terms of computing time, implementation complexity, and accuracy. Our main finding is that a thirdorder perturbation is competitive in terms of accuracy with Chebyshev polynomials and value function iteration, while being an order of magnitude faster to run. Therefore, we conclude that perturbation methods are an attractive approach for computing this class of problems.
Finite State MarkovChain Approximations to Highly Persistent Processes
, 2009
"... This paper reexamines the Rouwenhorst method of approximating firstorder autoregressive processes. This method is appealing because it can match the conditional and unconditional mean, the conditional and unconditional variance and the firstorder autocorrelation of any AR(1) process. This paper p ..."
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Cited by 29 (0 self)
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This paper reexamines the Rouwenhorst method of approximating firstorder autoregressive processes. This method is appealing because it can match the conditional and unconditional mean, the conditional and unconditional variance and the firstorder autocorrelation of any AR(1) process. This paper provides the first formal proof of this and other results. When comparing to five other methods, the Rouwenhorst method has the best performance in approximating the business cycle moments generated by the stochastic growth model. In addition, when the Rouwenhorst method is used, moments computed directly off the stationary distribution are as accurate as those obtained using Monte Carlo simulations.
Fortune or Virtue: TimeVariant Volatilities Versus Parameter Drifting in U.S. Data ∗
, 2010
"... participants at several seminars for useful comments, and Béla Személy for invaluable research assistance. Beyond the usual disclaimer, we must note that any views expressed herein are those of the authors and not necessarily those of the Federal Reserve Bank of Atlanta, the Federal Reserve Bank of ..."
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Cited by 29 (7 self)
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participants at several seminars for useful comments, and Béla Személy for invaluable research assistance. Beyond the usual disclaimer, we must note that any views expressed herein are those of the authors and not necessarily those of the Federal Reserve Bank of Atlanta, the Federal Reserve Bank of Philadelphia, or the Federal Reserve System. Finally, we also thank the NSF for financial support.
Risk Aversion and the Labor Margin in Dynamic Equilibrium Models”Federal Reserve Bank of San Francisco working paper
, 2010
"... The household’s labor margin has a substantial effect on risk aversion, and hence asset prices, in dynamic equilibrium models even when utility is additively separable between consumption and labor. This paper derives simple, closedform expressions for risk aversion that take into account the house ..."
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Cited by 23 (3 self)
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The household’s labor margin has a substantial effect on risk aversion, and hence asset prices, in dynamic equilibrium models even when utility is additively separable between consumption and labor. This paper derives simple, closedform expressions for risk aversion that take into account the household’s labor margin. Ignoring this margin can wildly overstate the household’s true aversion to risk. Risk premia on assets priced with the stochastic discount factor increase essentially linearly with risk aversion, so measuring risk aversion correctly is crucial for asset pricing in the model. Closedform expressions for risk aversion in models with generalized recursive preferences and internal and external habits are also derived.