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Working Paper
Bayesian semiparametric stochastic volatility modeling
This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general return distributions. Instead of specifying a particular distribution for the return innovation, we use nonparametric Bayesian methods to flexibly model the skewness and kurtosis of the distribution while continuing to model the dynamics of volatility with a parametric structure. Our semiparametric Bayesian approach provides a full characterization of parametric and distributional uncertainty. We present a Markov chain Monte Carlo sampling approach to estimation with ...
Report
Investment shocks and business cycles
Shocks to the marginal efficiency of investment are the most important drivers of business cycle fluctuations in U.S. output and hours. Moreover, like a textbook demand shock, these disturbances drive prices higher in expansions. We reach these conclusions by estimating a dynamic stochastic general equilibrium (DSGE) model with several shocks and frictions. We also find that neutral technology shocks are not negligible, but their share in the variance of output is only around 25 percent and even lower for hours. Labor supply shocks explain a large fraction of the variation of hours at very ...
Report
Inflation dynamics in a small open-economy model under inflation targeting: some evidence from Chile
This paper estimates a small open-economy dynamic stochastic general equilibrium (DSGE) model, specified along the lines of Gal and Monacelli (2005) and Lubik and Schorfheide (2007), using Chilean data for the full inflation-targeting period of 1999 to 2007. We study the specification of the policy rule followed by the Central Bank of Chile, the dynamic response of inflation to domestic and external shocks, and the change in these dynamics under different policy parameters. We use the DSGE-VAR methodology from our earlier work (2007) to assess the robustness of the conclusion to the presence ...
Journal Article
Policy analysis using DSGE models: an introduction
Many central banks have come to rely on dynamic stochastic general equilibrium, or DSGE, models to inform their economic outlook and to help formulate their policy strategies. But while their use is familiar to policymakers and academics, these models are typically not well known outside these circles. This article introduces the basic structure, logic, and application of the DSGE framework to a broader public by providing an example of its use in monetary policy analysis. The authors present and estimate a simple New Keynesian DSGE model, highlighting the core features that this basic ...
Report
DSGE model-based forecasting
Dynamic stochastic general equilibrium (DSGE) models use modern macroeconomic theory to explain and predict comovements of aggregate time series over the business cycle and to perform policy analysis. We explain how to use DSGE models for all three purposes?forecasting, story telling, and policy experiments?and review their forecasting record. We also provide our own real-time assessment of the forecasting performance of the Smets and Wouters (2007) model data up to 2011, compare it with Blue Chip and Greenbook forecasts, and show how it changes as we augment the standard set of observables ...
Report
The term structure of inflation expectations
We present estimates of the term structure of inflation expectations, derived from an affine model of real and nominal yield curves. The model features stochastic covariation of inflation with the real pricing kernel, enabling us to extract a time-varying inflation risk premium. We fit the model not only to yields, but also to the yields' variance-covariance matrix, thus increasing identification power. We find that model-implied inflation expectations can differ substantially from break-even inflation rates when market volatility is high. Our model's ability to be updated weekly makes it ...
Working Paper
Solving stochastic money-in-the-utility-function models
This paper analyzes the necessary and sufficient conditions for solving money-in-the-utility-function models when contemporaneous asset returns are uncertain. A unique solution to such models is shown to exist under certain measurability conditions. Stochastic Euler equations, whose existence is normally assumed in these models, are then formally derived. The regularity conditions are weak, and economically innocuous. The results apply to the broad range of discrete-time monetary and financial models that are special cases of the model used in this paper. The method is also applicable to ...
Journal Article
How good is what you've got? DSGE-VAR as a toolkit for evaluating DSGE models
In the constant search for better models to help guide policy decisions, central banks have begun to use and develop dynamic stochastic general equilibrium (DSGE) models. Although such models were until recently considered theoretically sound but overly restrictive, newly developed methods have proved successful in specifying DSGE models that fit the macroeconomic data well. ; Policy institutions that use DSGE models in policymaking need a reliable method for evaluating the models? effectiveness. This article reviews a procedure recently proposed by the authors and their colleagues. The ...
Working Paper
Documentation of the Research and Statistics Division’s estimated DSGE model of the U.S. economy: 2006 version
This paper provides documentation for the large-scale estimated DSGE model of the U.S. economy used in Edge, Kiley, and Laforte (2007). The model represents part of an ongoing research project (the Federal Reserve Board's Estimated, Dynamic, Optimization-based--FRB/EDO--model project) in the Macroeconomic and Quantitative Studies section of the Federal Reserve Board aimed at developing a DSGE model that can be used to address practical policy questions and the model documented here is the version that was current at the end of 2006. The paper discusses the model's specification, estimated ...
Working Paper
Dynamic estimation of volatility risk premia and investor risk aversion from option-implied and realized volatilities
This paper proposes a method for constructing a volatility risk premium, or investor risk aversion, index. The method is intuitive and simple to implement, relying on the sample moments of the recently popularized model-free realized and option-implied volatility measures. A small-scale Monte Carlo experiment suggests that the procedure works well in practice. Implementing the procedure with actual S&P 500 option-implied volatilities and high-frequency five-minute-based realized volatilities results in significant temporal dependencies in the estimated stochastic volatility risk premium, ...