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We study how unexpected changes in uncertainty about fiscal policy affect economic activity. First, we estimate tax and spending processes for the United States with time-varying volatility to uncover evidence of time-varying volatility. Second, we estimate a VAR for the US economy using the time-varying volatility found in the previous step. Third, we feed the tax and spending processes into an otherwise standard New Keynesian model. Both in the VAR and in the model, we find that unexpected changes in fiscal volatility shocks can have a sizable adverse effect on economic activity. An endogenous increase in markups is a key mechanism.

We show how changes in the volatility of the real interest rate at which small open emerging economies borrow have an important effect on variables like output, consumption, investment, and hours. We start by documenting the strong evidence of time-varying volatility in the real interest rates faced by four emerging economies: Argentina, Brazil, Ecuador, and Venezuela. We estimate a stochastic volatility process for real interest rates. Then, we feed this process in a standard small open economy business cycle model. We find that an increase in real interest rate volatility triggers a fall in output, consumption, investment, hours, and debt.

We study the causes behind the shift in the level of U.S. GDP following the Great Recession. To this end, we propose a model featuring endogenous productivity à la Romer and a financial friction à la Kiyotaki-Moore. Adverse financial disturbances during the recession and the lack of strong tailwinds post-crisis resulted in a severe contraction and the downward shift in the economy's trend. Had financial conditions remained stable during the crisis, the economy would have grown at its average growth rate. From a historical perspective, the Great Recession was unique because of the size and persistence of adverse shocks, and the lackluster performance of favorable shocks since 2010.

This paper discusses the parallel and efficient computation of macroeconomic models, with an emphasis on solving sovereign default models. Our motivation is twofold. First, we aim to streamline complex numerical models in a parallel computation fashion. Second, we want to unleash the power of graphic processing unit but bypass the steep learning and implementation costs of languages like C++ and Compute Unified Device Architecture (CUDA) in economic research. To this end, we propose a framework for efficient parallel computing with the modern language Julia. The paper offers a detailed analysis of parallel computing, Julia-style acceleration techniques, and coding recommendations. The Julia with CUDA benchmark shows a substantial speedup of over 1000 times compared to standard Julia that runs on a CPU. Our Julia with CUDA’s implementation is twice as fast as that of the C++ Standard Parallel Library. We provide an accompanying GitHub repository with the codes and the benchmarks used in this paper.

This paper explores the effects of using alternative combinations of observables for the estimation of Dynamic Stochastic General Equilibrium (DSGE) models. I find that the estimation of structural parameters describing the Taylor rule and sticky contracts in prices and wages is particularly sensitive to the set of observables. In terms of the model's predictions, the exclusion of some observables may lead to estimated parameters with unexpected outcomes, such as recessions following a positive technology shock. More importantly, two ways to assess different sets of observables are proposed. These measures favor a dataset consisting of seven observables.

A common problem in estimating dynamic stochastic general equilibrium models is that the structural parameters of economic interest are only weakly identified. As a result, classical confidence sets and Bayesian credible sets will not coincide even asymptotically, and the mean, mode, or median of the posterior distribution of the structural parameters can no longer be viewed as a consistent estimator. We propose two methods of constructing confidence intervals for structural model parameters that are asymptotically valid from a frequentist point of view regardless of the strength of identification. One involves inverting a likelihood ratio test statistic, whereas the other involves inverting a Bayes factor statistic. A simulation study shows that both methods have more accurate coverage than alternative methods of inference. An empirical study of the degree of wage and price rigidities in the U.S. economy illustrates that the data may contain useful information about structural model parameters even when these parameters are only weakly identified.

Supply-side policies can play a role infighting a low aggregate demand that traps an economy at the zero lower bound (ZLB) of nominal interest rates. Reductions in mark-ups or future increases in productivity triggered by supply-side policies generate a wealth effect that pulls current consumption and output up. Since the economy is at the ZLB, increases in interest rates do not undo this wealth effect. The paper illustrates this mechanism with a New Keynesian model.

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 changes in monetary policy during Chairman Paul Volcker's tenure at the Federal Reserve, those changes contributed little to the Great Moderation. Instead, changes in the volatility of structural shocks account for most of it. Also, although the authors find that monetary policy was different under Volcker, they do not find much evidence of a big difference in monetary policy among the tenures of Chairmen Arthur Burns, G. William Miller, and Alan Greenspan. The difference in aggregate outcomes across these periods is attributed to the time-varying volatility of shocks. The history for inflation is more nuanced, as a more vigorous stand against it would have reduced inflation in the 1970s, but not completely eliminated it. In addition, they find that volatile shocks (especially those related to aggregate demand) were important contributors to the Great Inflation. Adapted from the source document.

Working Paper No. 15928 This paper compares the role of stochastic volatility versus changes in monetary policy rules in accounting for the time-varying volatility of U.S. aggregate data. Of special interest to us is understanding the sources of the great moderation of business cycle fluctuations that the U.S. economy experienced between 1984 and 2007. To explore this issue, we build a medium-scale dynamic stochastic general equilibrium (DSGE) model with both stochastic volatility and parameter drifting in the Taylor rule and we estimate it non-linearly using U.S. data and Bayesian methods. Methodologically, we show how to confront such a rich model with the data by exploiting the structure of the high-order approximation to the decision rules that characterize the equilibrium of the economy. Our main empirical findings are: 1) even after controlling for stochastic volatility (and there is a fair amount of it), there is overwhelming evidence of changes in monetary policy during the analyzed period; 2) however, these changes in monetary policy mattered little for the great moderation; 3) most of the great performance of the U.S. economy during the 1990s was a result of good shocks; and 4) the response of monetary policy to inflation under Burns, Miller, and Greenspan was similar, while it was much higher under Volcker.