Christian Matthes

Optimal Disinflation Under Learning

We model transitional dynamics that emerge after the adoption of a new monetary-policy rule. We assume that private agents learn about the new policy via Bayesian updating, and we study how learning affects the nature of the transition and choice of a new rule. The model endogenously generates time-varying volatility during the transition. Managing this volatility is the central banks main challenge. The optimal policy depends on subtle features of the private sectors prior. Nevertheless, two robust conclusions emerge from our examples. First, the central bank can adjust target inflation freely without triggering high volatility. Second, none of our examples rationalizes a gradual reduction in inflation. On the contrary, inflation falls sharply at impact, overshoots the new target, and converges from below.

Learning About Fiscal Policy and the Effects of Policy Uncertainty

The recent crisis in the United States has often been associated with substantial amounts of policy uncertainty. In this paper we ask how uncertainty about fiscal policy affects the impact of fiscal policy changes on the economy when the government tries to counteract a deep recession. The agents in our model act as econometricians by estimating the policy rules for the different fiscal policy instruments, which include distortionary tax rates. Comparing the outcomes in our model to those under full-information rational expectations, we find that assuming that agents are not instantaneously aware of the new fiscal policy regime (or policy rule) in place leads to substantially more volatility in the short run and persistent differences in average outcomes.

Approximating Time Varying Structural Models with Time Invariant Structures

The paper studies how parameter variation affects the decision rules of a DSGE model and structural inference. We provide diagnostics to detect parameter variations and to ascertain whether they are exogenous or endogenous. Identification and inferential distortions when a constant parameter model is incorrectly assumed are examined. Likelihood and VAR-based estimates of the structural dynamics when parameter variations are neglected are compared. Time variations in the financial frictions of Gertler and Karadis (2010) model are studied.

Drifts and volatilities under measurement error: Assessing monetary policy shocks over the last century

How much have the dynamics of U.S. time series changed over the last century? Has the evolution of the Federal Reserve as an institution over the 100 years altered the transmission of monetary policy shocks? To tackle these questions, we build a multivariate time series model with time‐varying parameters and stochastic volatility that features measurement errors in observables. We find substantial changes in the structure of the economy. There is also large variation in the impact of monetary policy shocks, but the majority of this variation is driven by changes in exogenous volatility.

Choosing Prior Hyperparameters: With Applications to Time-Varying Parameter Models

Time-varying parameter models with stochastic volatility are widely used to study macroeconomic and financial data. These models are almost exclusively estimated using Bayesian methods. A common practice is to focus on prior distributions that themselves depend on relatively few hyperparameters such as the scaling factor for the prior covariance matrix of the residuals governing time variation in the parameters. The choice of these hyperparameters is crucial because their influence is sizeable for standard sample sizes. In this article, we treat the hyperparameters as part of a hierarchical model and propose a fast, tractable, easy-to-implement, and fully Bayesian approach to estimate those hyperparameters jointly with all other parameters in the model. We show via Monte Carlo simulations that, in this class of models, our approach can drastically improve on using fixed hyperparameters previously proposed in the literature. Supplementary materials for this article are available online.

Understanding the Size of the Government Spending Multiplier: It's in the Sign

Despite intense scrutiny, estimates of the government spending multiplier remain highly uncertain, with values ranging from 0.5 to 2. While an increase in government spending is generally assumed to have the same (mirror-image) effect as a decrease in government spending, we show that relaxing this assumption is important to understand the effects of fiscal policy. Regardless of whether we identify government spending shocks from (i) a narrative approach, or (ii) a timing restriction, we find that the contractionary multiplier --the multiplier associated with a negative shock to government spending-- is above 1, while the expansionary multiplier --the multiplier associated with a positive shock-- is substantially below 1. The multiplier is largest in recessions, as found in previous studies, but only because the contractionary multiplier is largest in recessions. The expansionary multiplier is always below 1 and not larger in recessions. We argue that our results help understand the wide range of multiplier estimates found in the literature.

Time-Varying Parameter Vector Autoregressions: Specification, Estimation, and an Application

Time-varying parameter vector autoregressions (TVP-VARs) have become a popular tool to study the dynamics of macroeconomic time series. In this article, we discuss the specification and estimation of this class of models with a focus on implementability. We provide a step-by-step guide for researchers interested in utilizing this methodology in their own research. Specifically, we discuss how to use Bayesian Gibbs-sampling techniques to easily conduct inference.

Tales of Transition Paths: Policy Uncertainty and Random Walks

What happens when fiscal and/or monetary policy changes systematically? We construct a DSGE model in which agents have to estimate fiscal and monetary policy rules and assess how uncertainty surrounding the conduct of policymakers influences transition paths after policy changes. We find that policy changes of the magnitude often considered in the literature can lead private agents to hold substantially different views about the nature of equilibrium than would be predicted by a full information analysis. In particular, random walk-like behavior can be observed for a large number of periods in equilibrium, even though the models we use admit stationary dynamics under full-information rational expectations.