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Working Paper
Estimating Hysteresis Effects
In this paper, we identify demand shocks that can have a permanent effect on output through hysteresis effects. We call these shocks permanent demand shocks. They are found to be quantitatively important in the United States, in particular when the sample includes the Great Recession. Recessions driven by permanent demand shocks lead to a permanent decline in employment and investment, although output per worker is largely unaffected. We find strong evidence that hysteresis transmits through a rise in long-term unemployment and a decline in labor force participation and disproportionately ...
Working Paper
Markov-switching structural vector autoregressions: theory and application
This paper develops a new and easily implementable necessary and sufficient condition for the exact identification of a Markov-switching structural vector autoregression (SVAR) model. The theorem applies to models with both linear and some nonlinear restrictions on the structural parameters. We also derive efficient MCMC algorithms to implement sign and long-run restrictions in Markov-switching SVARs. Using our methods, four well-known identification schemes are used to study whether monetary policy has changed in the euro area since the introduction of the European Monetary Union. We find ...
Working Paper
Estimating dynamic equilibrium economies: linear versus nonlinear likelihood
This paper compares two methods for undertaking likelihood-based inference in dynamic equilibrium economies: a sequential Monte Carlo filter proposed by Fernndez-Villaverde and Rubio-Ramrez (2004) 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. The authors report two main results. First, both for simulated and for real data, the sequential Monte Carlo filter delivers a ...
Working Paper
Dividend Momentum and Stock Return Predictability: A Bayesian Approach
A long tradition in macro finance studies the joint dynamics of aggregate stock returns and dividends using vector autoregressions (VARs), imposing the cross-equation restrictions implied by the Campbell-Shiller (CS) identity to sharpen inference. We take a Bayesian perspective and develop methods to draw from any posterior distribution of a VAR that encodes a priori skepticism about large amounts of return predictability while imposing the CS restrictions. In doing so, we show how a common empirical practice of omitting dividend growth from the system amounts to imposing the extra ...
Working Paper
Computing DSGE models with recursive preferences and stochastic volatility
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) and stochastic volatility. Models with these two features 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 and stochastic volatility using four different approaches: second- and third-order perturbation, Chebyshev polynomials, and value ...
Working Paper
Nominal versus real wage rigidities: A Bayesian approach
This paper explores the capability of a dynamic stochastic general equilibrium model with staggered price setting and real wage rigidities to fit the data with reasonable average durations of price and wage contracts. The authors implement a Bayesian approach for parameter estimation and for model comparison with other models that only incorporate nominal rigidities. Their main results can be summarized as follows: First, the authors find that, on average, prices are fixed for three quarters, nominal wages are fixed for five quarters, and half of the wage setters follow a real wage indexing ...
Journal Article
Reading the recent monetary history of the United States, 1959-2007
In this paper the authors report the results of the estimation of a rich dynamic stochastic general equilibrium (DSGE) model of the U.S. economy with both stochastic volatility and parameter drifting in the Taylor rule. They use the results of this estimation to examine the recent monetary history of the United States and to interpret, through this lens, the sources of the rise and fall of the Great Inflation from the late 1960s to the early 1980s and of the Great Moderation of business cycle fluctuations between 1984 and 2007. Their main findings are that, while there is strong evidence of ...
Working Paper
Nonlinear adventures at the zero lower bound
Motivated by the recent experience of the U.S. and the Eurozone, the authors describe the quantitative properties of a New Keynesian model with a zero lower bound (ZLB) on nominal interest rates, explicitly accounting for the nonlinearities that the bound brings. Besides showing how such a model can be efficiently computed, the authors found that the behavior of the economy is substantially affected by the presence of the ZLB. In particular, the authors document 1) the unconditional and conditional probabilities of hitting the ZLB; 2) the unconditional and conditional probabilty distributions ...
Working Paper
Investment-specific technology shocks and international business cycles: an empirical assessment
In this paper, we first introduce investment-specific technology (IST) shocks into an otherwise standard international real business cycle model and show that a thoughtful calibration of them along the lines of Raffo (2009) successfully addresses several of the existing puzzles in the literature. In particular, we obtain a negative correlation of relative consumption and the terms of trade (Backus-Smith puzzle), as well as a more volatile real exchange rate, and cross-country output correlations that are higher than consumption correlations (price and quantity puzzles). Then we use data from ...
Working Paper
Macroeconomic Forecasting and Variable Ordering in Multivariate Stochastic Volatility Models
We document five novel empirical findings on the well-known potential ordering drawback associated with the time-varying parameter vector autoregression with stochastic volatility developed by Cogley and Sargent (2005) and Primiceri (2005), CSP-SV. First, the ordering does not affect point prediction. Second, the standard deviation of the predictive densities implied by different orderings can differ substantially. Third, the average length of the prediction intervals is also sensitive to the ordering. Fourth, the best ordering for one variable in terms of log-predictive scores does not ...