Results 1  10
of
50
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 26 (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
Nonparametric Identification of Dynamic Models with Unobserved State Variables
, 2008
"... We consider the identification of a Markov process {Wt, X ∗ t} when only {Wt}, a subset of the variables, are observed. In structural dynamic models, Wt includes the sequences of choice variables and observed state variables of an optimizing agent, while X ∗ t denotes the sequence of serially correl ..."
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Cited by 21 (7 self)
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We consider the identification of a Markov process {Wt, X ∗ t} when only {Wt}, a subset of the variables, are observed. In structural dynamic models, Wt includes the sequences of choice variables and observed state variables of an optimizing agent, while X ∗ t denotes the sequence of serially correlated unobserved state variables. The to depend Markov setting allows the distribution of the unobserved state variable X ∗ t on Wt−1 and X ∗ t−1. In the nonstationary case, we show that the Markov transition density fWt,X ∗ t Wt−1,X ∗ is identified from the observation of five periods of data t−1 Wt+1, Wt, Wt−1, Wt−2, Wt−3 under reasonable assumptions. In the stationary case, only four observations Wt+1, Wt, Wt−1, Wt−2 are required. Identification of fWt,X ∗ t Wt−1,X ∗ t−1 is a crucial input in methodologies for estimating Markovian dynamic models based on the “conditionalchoiceprobability (CCP)” approach pioneered by Hotz and Miller.
Optimal filtering of jump diffusions: extracting latent states from asset prices”, Working Paper, http://wwwstat.wharton.upenn.edu/ stroud/pubs.html
, 2006
"... This paper provides a methodology for computing optimal filtering distributions in discretely observed continuoustime jumpdiffusion models. Although it has received little attention, the filtering distribution is useful for estimating latent states, forecasting volatility and returns, computing mo ..."
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Cited by 20 (5 self)
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This paper provides a methodology for computing optimal filtering distributions in discretely observed continuoustime jumpdiffusion models. Although it has received little attention, the filtering distribution is useful for estimating latent states, forecasting volatility and returns, computing model diagnostics such as likelihood ratios, and parameter estimation. Our approach combines timediscretization schemes with Monte Carlo methods to compute the optimal filtering distribution. Our approach is very general, applying in multivariate jumpdiffusion models with nonlinear characteristics and even nonanalytic observation equations, such as those that arise when option prices are available. We provide a detailed analysis of the performance of the filter, and analyze four applications: disentangling jumps from stochastic volatility, forecasting realized volatility, likelihood based model comparison, and filtering using both option prices and underlying returns. 2 1
Methods to Estimate Dynamic Stochastic General Equilibrium Models
 Journal of Economic Dynamics and Control
, 2007
"... This paper employs the onesector Real Business Cycle model as a testing ground for four di®erent procedures to estimate Dynamic Stochastic General Equilibrium (DSGE) models. The procedures are: 1) Maximum Likelihood (with and without measurement errors and incorporating priors), 2) Generalized Meth ..."
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Cited by 20 (1 self)
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This paper employs the onesector Real Business Cycle model as a testing ground for four di®erent procedures to estimate Dynamic Stochastic General Equilibrium (DSGE) models. The procedures are: 1) Maximum Likelihood (with and without measurement errors and incorporating priors), 2) Generalized Method of Moments, 3) Simulated Method of Moments, and 4) the Extended Method of Simulated Moments proposed by Smith (1993). Monte Carlo analysis shows that although all procedures deliver reasonably good estimates, there are substantial di®erences in statistical and computational e±ciency in the small samples currently available to estimate DSGE models. The implications of the singularity of DSGE models for each estimation procedure are fully discussed.
The Term Structure of Interest Rates in a DSGE Model with Recursive Preferences
, 2010
"... We solve a dynamic stochastic general equilibrium (DSGE) model in which the representative household has Epstein and Zin recursive preferences. The parameters governing preferences and technology are estimated by means of maximum likelihood using macroeconomic data and asset prices, with a particul ..."
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Cited by 16 (1 self)
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We solve a dynamic stochastic general equilibrium (DSGE) model in which the representative household has Epstein and Zin recursive preferences. The parameters governing preferences and technology are estimated by means of maximum likelihood using macroeconomic data and asset prices, with a particular focus on the term structure of interest rates. We estimate a large risk aversion, an elasticity of intertemporal substitution higher than one, and substantial adjustment costs. Furthermore, we identify the tensions within the model by estimating it on subsets of these data. We conclude by pointing out potential extensions that might improve the model’s fit.
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 13 (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.
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 12 (4 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.
Learning and TimeVarying Macroeconomic Volatility”, mimeo
, 2007
"... Abstract. This paper presents a DSGE model in which agents ’ learning about the economy can endogenously generate timevarying macroeconomic volatility. Economic agents use simple models to form expectations and need to learn the relevant parameters. Their gain coefficient is endogenous and is adjus ..."
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Cited by 11 (1 self)
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Abstract. This paper presents a DSGE model in which agents ’ learning about the economy can endogenously generate timevarying macroeconomic volatility. Economic agents use simple models to form expectations and need to learn the relevant parameters. Their gain coefficient is endogenous and is adjusted according to past forecast errors. The model is estimated using likelihoodbased Bayesian methods. The endogenous gain is jointly estimated with the structural parameters of the system. The estimation results show that private agents appear to have often switched to constantgain learning, with a high constant gain, during most of the 1970s and until the early 1980s, while reverting to a decreasing gain later on. As a result, the model can generate a pattern of volatility, which is increasing in the 1970s and falling in the second half of the sample, with a decline that can roughly match the magnitude of the “Great Moderation”. The paper also documents how a failure to incorporate learning into the estimation may lead econometricians to spuriously find timevarying volatility in the exogenous shocks, even when these have constant variance by construction.
Solving DSGE Models with Perturbation Methods and a Change of Variables
 Journal of Economic Dynamics and Control
, 2006
"... This paper explores the application of the changes of variables technique to solve the stochastic neoclassical growth model. We use the method of Judd (2003) to change variables in the computed policy functions that characterize the behavior of the economy. We report how the optimal change of variab ..."
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Cited by 11 (4 self)
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This paper explores the application of the changes of variables technique to solve the stochastic neoclassical growth model. We use the method of Judd (2003) to change variables in the computed policy functions that characterize the behavior of the economy. We report how the optimal change of variables reduces the average absolute Euler equation errors of the solution of the model by a factor of three. We also demonstrate how changes of variables correct for variations in the volatility of the economy even if we work with first order policy functions and how we can keep a linear representation of the laws of motion of the model if we use a nearly optimal transformation. We discuss how to apply our results to estimate dynamic equilibrium economies.