Abstract not found

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 time-varying volatility in the real interest rates faced by a sample of four emerging small open

This paper employs the one-sector 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.

This paper provides a methodology for computing optimal filtering distributions in discretely observed continuous-time jump-diffusion 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 time-discretization schemes with Monte Carlo methods to compute the optimal filtering distribution. Our approach is very general, applying in multivariate jump-diffusion models with nonlinear characteristics and even non-analytic 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.

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.

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.

I explore the implications for asset prices and macroeconomic dynamics of shocks that improve real investment opportunities and thus affect the representative household’s marginal utility. These investment shocks generate differences in risk premia due to their heterogenous impact on firms: they benefit firms producing investment relative to firms producing consumption goods, and increase the value of growth opportunities relative to the value of existing assets. Using data on asset returns, I find that a positive investment shock leads to high marginal utility states. A general equilibrium model with investment shocks matches key features of macroeconomic quantities and asset prices. 1

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.

for useful comments. 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 or the Federal Reserve System. Finally, we also thank the NSF for …nancial support. This paper shows how changes in the volatility of the real interest rate at which small open emerging economies borrow have a quantitatively important e¤ect on real variables like output, consumption, investment, and hours worked. To motivate our investigation, we document the strong evidence of time varying volatility in the real interest rates faced by a sample of four small emerging open

conference for comments. 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 or the Federal Reserve System. Finally, we also thank the NSF for financial support. This paper studies how stable over time are the so-called “structural parameters” of dynamic stochastic general equilibrium (DSGE) models. To answer this question, we estimate a medium-scale DSGE model with real and nominal rigidities using U.S. data. In our model, we allow for parameter drifting and rational expectations of the agents with respect to this drift. We document that there is strong evidence that parameters change within our sample. We illustrate variations in the parameters describing the monetary policy reaction function and in the parameters characterizing the pricing behavior of firms and households. Moreover, we show how the movements in the pricing parameters are correlated with inflation. Thus, our results cast doubts on the empirical relevance of Calvo models.