A Bayesian approach to dynamic macroeconomics

DeJong, David N.; Ingram, Beth F.; Whiteman, Charles H.

doi:10.1016/s0304-4076(00)00019-1

Cited by 148 publications

(90 citation statements)

References 20 publications

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“…There is a single consumption good in the economy, and two assets: 2 These results contrast with those presented in an earlier version of this paper, in which we studied the role of temptation in accounting for the volatility puzzle in isolation. There we estimated a quantitatively signi…cant temptation e¤ect (equivalent to the utility loss associated with a 5.25% reduction in steady state consumption), and found that DSC preferences contributed modestly towards accounting for the volatility puzzle.…”

Section: The Modelcontrasting

confidence: 82%

“…Estimation is accomplished using the full-information Bayesian procedure developed by DeJong, Ingram and Whiteman (2000). Brie ‡y, this works as follows.…”

Section: Estimationmentioning

confidence: 99%

“…Second, relative to results obtained using the CRRA benchmark, we …nd that the adoption of self-control preferences makes only a marginal contribution towards a resolution of the three asset-pricing puzzles. 2 There is a growing body of work that focuses on the potential role played by temptation and other self-control problems in in ‡uencing asset-pricing behavior. One study closely related to ours is that of Krusell, Kuruscu and Smith (2002), who explore the ability of a model featuring a small subset of agents who face a temptation to save to account for the equity premium puzzle.…”

Section: Introductionmentioning

confidence: 99%

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Do self-control preferences help explain the puzzling behavior of asset prices?

DeJong

Ripoll

2007

Journal of Monetary Economics

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In the context of a simple asset-pricing environment, we study the ability of self-control preferences to account for the stock-price volatility, risk-free-rate and equity-premium puzzles. Using a full-information estimation procedure, we estimate the presence of a quantitatively small selfcontrol e¤ect in the data. Moreover, with results obtained using CRRA preferences serving as a benchmark, we …nd that the adoption of self-control preferences makes only a marginal contribution towards a resolution of these puzzles.

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Section: The Modelcontrasting

confidence: 82%

“…Estimation is accomplished using the full-information Bayesian procedure developed by DeJong, Ingram and Whiteman (2000). Brie ‡y, this works as follows.…”

Section: Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Do self-control preferences help explain the puzzling behavior of asset prices?

DeJong

Ripoll

2007

Journal of Monetary Economics

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“…This model or simple variations thereof are frequently used when developing and illustrating alternative solution and estimation procedures (for examples of the later see, e.g. RugeMurcia (2007), Ireland (2001Ireland ( , 2004 and Dejong et al (2000b)). …”

Section: Basic Rbc Modelmentioning

confidence: 99%

“…To implement this, first recall the VAR(1) measurement error setup in equation (14). 13 By simulating smoothed states α t and η t using the posterior means of the model parameters, we arrive at data predictions from the RBC and the error blocks of the model.…”

Section: Rbc Model Versus Error System Fitmentioning

confidence: 99%

Technology shocks and aggregate fluctuations in an estimated hybrid RBC model

Malley

Woitek

2010

Journal of Economic Dynamics and Control

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This paper contributes to the on-going empirical debate regarding the role of the RBC model and in particular of technology shocks in explaining aggregate fluctuations. To this end we estimate the model's posterior density using Markov-Chain Monte-Carlo (MCMC) methods. Within this framework we extend Ireland's (2001Ireland's ( , 2004 hybrid estimation approach to allow for a vector autoregressive moving average (VARMA) process to describe the movements and co-movements of the model's errors not explained by the basic RBC model. The results of marginal likelihood ratio tests reveal that the more general model of the errors significantly improves the model's fit relative to the VAR and AR alternatives. Moreover, despite setting the RBC model a more difficult task under the VARMA specification, our analysis, based on forecast error and spectral decompositions, suggests that the RBC model is still capable of explaining a significant fraction of the observed variation in macroeconomic aggregates in the post-war U.S. economy.

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Sequential Monte Carlo Sampling for Dsge Models

Herbst

Schorfheide

2014

J of Applied Econometrics

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SUMMARYWe develop a sequential Monte Carlo (SMC) algorithm for estimating Bayesian dynamic stochastic general equilibrium (DSGE) models; wherein a particle approximation to the posterior is built iteratively through tempering the likelihood. Using two empirical illustrations consisting of the Smets and Wouters model and a larger news shock model we show that the SMC algorithm is better suited for multimodal and irregular posterior distributions than the widely used random walk Metropolis–Hastings algorithm. We find that a more diffuse prior for the Smets and Wouters model improves its marginal data density and that a slight modification of the prior for the news shock model leads to drastic changes in the posterior inference about the importance of news shocks for fluctuations in hours worked. Unlike standard Markov chain Monte Carlo (MCMC) techniques; the SMC algorithm is well suited for parallel computing. Copyright © 2014 John Wiley & Sons, Ltd.

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A Bayesian approach to dynamic macroeconomics

Cited by 148 publications

References 20 publications

Do self-control preferences help explain the puzzling behavior of asset prices?

Do self-control preferences help explain the puzzling behavior of asset prices?

Technology shocks and aggregate fluctuations in an estimated hybrid RBC model

Sequential Monte Carlo Sampling for Dsge Models

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