What Do We Know About the Effects of Fiscal Policy Shocks? A Comparative Analysis

The empirical literature studying the effects of fiscal policy shocks using VAR models differs among two important dimensions: the identification scheme and the VAR specification. Not surprisingly the results obtained are often diverse. The aim of this paper is to test whether differences in the results can be explained by different VAR specifications and/or alternative identification strategies. To this end, we estimate a common reduced-form VAR model to which we apply the different identification approaches proposed in the literature. We find that, after controlling for specification issues, the recursive approach and the Blanchard-Perotti approach yield very similar results, while the fiscal dummy variable approach yields significantly different results.Fiscal Policy Shocks, VAR analysis

Computing DSGE Models with Recursive Preferences

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). Models with these preferences 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 using four different approaches: second and third-order perturbation, Chebyshev polynomials, and value function iteration. We document the performance of the methods in terms of computing time, implementation complexity, and accuracy. Our main finding is that a third-order perturbation is competitive in terms of accuracy with Chebyshev polynomials and value function iteration, while being an order of magnitude faster to run. Therefore, we conclude that perturbation methods are an attractive approach for computing this class of problems.DSGE Models, Recursive Preferences, Perturbation

Computing DSGE Models with Recursive Preferences

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). Models with these preferences 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 using four different approaches: second- and third-order perturbation, Chebyshev polynomials, and value function iteration. We document the performance of the methods in terms of computing time, implementation complexity, and accuracy. Our main finding is that a third-order perturbation is competitive in terms of accuracy with Chebyshev polynomials and value function iteration, while being an order of magnitude faster to run. Therefore, we conclude that perturbation methods are an attractive approach for computing this class of problems.

Essays on Empirical Macroeconomics

This thesis consists of four essays in empirical macroeconomics. What Are the Effects of Fiscal Policy Shocks? A VAR-Based Comparative Analysis The literature using structural vector autoregressions (SVARs) to assess the effects of fiscal policy shocks strongly disagrees on the qualitative and quantitative response of key macroeconomic variables. We find that controlling for differences in specification of the reduced-form model, all identification approaches used in the literature yield similar results regarding the effects of government spending shocks, but diverging results regarding the effects of tax shocks. The Analytics of SVARs. A Unified Framework to Measure Fiscal Multipliers Does fiscal policy stimulate output? SVARs have been used to address this question, but no stylized facts have emerged. I show that different priors about the output elasticities of tax revenue and government expenditures implied by the identification schemes generate a large dispersion in the estimates of tax and spending multipliers. I estimate fiscal multipliers consistent with prior distributions of the elasticities computed by a variety of empirical strategies. I document that in the U.S. spending multipliers are larger than the tax multipliers. Computing DSGE Models with Recursive Preferences and Stochastic Volatility This paper compares solution methods for computing the equilibrium of dynamic stochastic general equilibrium models with recursive preferences and stochastic volatility. The main finding is that a third-order perturbation is competitive in terms of accuracy with Chebyshev polynomials and value function iteration, while being an order of magnitude faster to run. Business Cycle Accounting and Misspecified DSGE Models This paper investigates how insights from the literature on business cycle accounting can be used to trace out the implications of missing channels in a baseline estimated dynamic stochastic general equilibrium model used for forecast and policy analysis

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 function iteration. We document the performance of the methods in terms of computing time, implementation complexity, and accuracy. Our main finding is that perturbations are competitive in terms of accuracy with Chebyshev polynomials and value function iteration while being several orders of magnitude faster to run. Therefore, we conclude that perturbation methods are an attractive approach for computing this class of problems. (Copyright: Elsevier)DSGE models; Recursive preferences; Perturbation