Dario Caldara

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.

The analytics of SVARs: a unified framework to measure fiscal multipliers

Do tax cuts and spending increases stimulate output? Studies that identify fiscal shocks using structural vector autoregressions (SVAR) have reached different conclusions. In this article, we show analytically that this lack of consensus reflects different assumptions on the fiscal rules that—by relating tax and spending policies to macroeconomic conditions—determine the identification of fiscal shocks and the associated fiscal multipliers. We then propose a new identification strategy based on a proxy SVAR that uses non-fiscal instruments to directly estimate the parameters of the fiscal rules. We find that spending increases stimulate output more than tax cuts..

The Macroeconomic Impact of Financial and Uncertainty Shocks

The extraordinary events surrounding the Great Recession have cast a considerable doubt on the traditional sources of macroeconomic instability. In their place, economists have singled out financial and uncertainty shocks as potentially important drivers of economic fluctuations. Empirically distinguishing between these two types of shocks, however, is difficult because increases in economic uncertainty are strongly associated with a widening of credit spreads, an indication of a tightening in financial conditions. This paper uses the penalty function approach within the SVAR framework to examine the interaction between financial conditions and economic uncertainty and to trace out the impact of these two types of shocks on the economy. The results indicate that (1) financial shocks have a significant adverse effect on economic outcomes and that such shocks were an important source of cyclical fluctuations since the mid-1980s; (2) uncertainty shocks, especially those implied by uncertainty proxies that do not rely on financial asset prices, are also an important source of macroeconomic disturbances; and (3) uncertainty shocks have an especially negative economic impact in situations where they elicit a concomitant tightening of financial conditions. Evidence suggests that the Great Recession was likely an acute manifestation of the toxic interaction between uncertainty and financial shocks.

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.

The Systematic Component of Monetary Policy in SVARs: An Agnostic Identification Procedure

Following Leeper, Sims, and Zha (1996), we identify monetary policy shocks in SVARs by restricting the systematic component of monetary policy. In particular, we impose sign and zero restrictions only on the monetary policy equation. Since we do not restrict the response of output to a monetary policy shock, we are agnostic in Uhligs (2005) sense. But, in contrast to Uhlig (2005), our results support the conventional view that a monetary policy shock leads to a decline in output. Hence, our results show that the contractionary effects of monetary policy shocks do not hinge on questionable exclusion restrictions.

Measuring Geopolitical Risk

We present a monthly indicator of geopolitical risk based on a tally of newspaper articles covering geopolitical tensions, and examine its evolution and effects since 1985. The geopolitical risk (GPR) index spikes around the Gulf War, after 9/11, during the 2003 Iraq invasion, during the 2014 Russia-Ukraine crisis, and after the Paris terrorist attacks. High geopolitical risk leads to a decline in real activity, lower stock returns, and movements in capital flows away from emerging economies and towards advanced economies. When we decompose the index into threats and acts components, the adverse effects of geopolitical risk are mostly driven by the threat of adverse geopolitical events. Extending our index back to 1900, geopolitical risk rose dramatically during the World War I and World War II, was elevated in the early 1980s, and has drifted upward since the beginning of the 21st century.

Oil Price Elasticities and Oil Price Fluctuations

We study the identification of oil shocks in a structural vector autoregressive (SVAR) model of the oil market. First, we show that the cross-equation restrictions of a SVAR impose a nonlinear relation between the short-run price elasticities of oil supply and oil demand. This relation implies that seemingly plausible restrictions on oil supply elasticity may map into implausible values of the oil demand elasticity, and vice versa. Second, we propose an identification scheme that restricts these elasticities by minimizing the distance between the elasticities allowed by the SVAR and target values that we construct from a survey of relevant studies. Third, we use the identified SVAR to analyze sources and consequences of movements in oil prices. We find that (1) oil supply shocks and global demand shocks explain 50 and 35 percent of oil price fluctuations, respectively; (2) a drop in oil prices driven by supply shocks boosts economic activity in advanced economies, whereas it depresses economic activity in emerging economies; and (3) the selection of oil market elasticities is essential for understanding the source of oil price movements and to measuring the multipliers of oil prices on economic activity.

Practical Tools for Policy Analysis in DSGE Models with Missing Channels

In this paper we analyze the propagation of shocks originating in sectors that are not present in a baseline dynamic stochastic general equilibrium (DSGE) model. Specifically, we proxy the missing sector through a small set of factors, that feed into the structural shocks of the DSGE model to create correlated disturbances. We estimate the factor structure by matching impulse responses of the augmented DSGE model to those generated by an auxiliary model. We apply this methodology to track the effects of oil shocks and housing demand shocks in models without energy and housing sectors.

Monetary Policy, Real Activity, and Credit Spreads: Evidence from Bayesian Proxy Svars

This paper studies the interaction between monetary policy, financial markets, and the real economy. We develop a Bayesian framework to estimate proxy structural vector autoregressions (SVARs) in which monetary policy shocks are identified by exploiting the information contained in high frequency data. For the Great Moderation period, we find that monetary policy shocks are key drivers of fluctuations in industrial output and corporate credit spreads, explaining about 20 percent of the volatility of these variables. Central to this result is a systematic component of monetary policy characterized by a direct and economically significant reaction to changes in credit spreads. We show that the failure to account for this endogenous reaction induces an attenuation bias in the response of all variables to monetary shocks.

The Analytics of SVARs: A Unified Framework to Measure Fiscal Multipliers

Do tax cuts and spending increases stimulate output? Studies that identify fiscal shocks using structural vector autoregressions (SVAR) have reached different conclusions. In this article, we show analytically that this lack of consensus reflects different assumptions on the fiscal rules that—by relating tax and spending policies to macroeconomic conditions—determine the identification of fiscal shocks and the associated fiscal multipliers. We then propose a new identification strategy based on a proxy SVAR that uses non-fiscal instruments to directly estimate the parameters of the fiscal rules. We find that spending increases stimulate output more than tax cuts..