We build an open-economy dynamic stochastic general equilibrium (DSGE) model that allows us to: (i) derive a time series for labor informality in Brazil spanning the period 2004–2018, whose evolution is consistent with the behavior of the main series provided by Pesquisa Nacional por Amostra de Domicílios (PNAD); (ii) run dynamic simulations showing that, in the presence of a large informal labor market (around 50% of the total labor force), expenditure-cutting measures lead, at worst, to mild short-run recessions in the formal sector and are likely to foster public debt sustainability. Likewise, adjustments through some kinds of distortionary taxation, mainly the corporate tax, and to a lesser extent, the consumption tax, also seem to improve both public debt dynamics and fiscal collection without a significant cost in terms of output. Thus, in countries with large informal economies experiencing fiscal woes, expenditure-based consolidations, as well as some sorts of tax-based adjustments, should be relied upon.

In this paper, I investigate the conditional contributions of the ins and outs of unemployment from both empirical and theoretical perspectives. Based on a New Keynesian Dynamic Stochastic General Equilibrium (DSGE) theoretical framework, I estimate a sign restriction Vector Auto Regressive (VAR) model using French data. To identify the origins of unemployment dynamics in terms of worker transition rates, I simulate two shocks: one on the supply side (technology) and the other on the demand side (monetary). The VAR model reveals that the contributions of transition rates in explaining unemployment differ across shocks. After a technology shock, unemployment fluctuations are mainly explained by the job finding process, while the contributions of the two margins are more balanced for the monetary shock. The theoretical model is not able to reproduce the underlying mechanisms inducing unemployment. In particular, the conditional contributions of the job separation margin are overestimated each time. For instance, after a technology shock, 60% of unemployment changes are generated by this margin, while the data suggest a contribution of 28%. This paper strongly indicates that, in its standard formulation, a search and matching DSGE model featuring endogenous job separations is not able to replicate the dominating influence of the outflow process.

This paper tests the ability of New Keynesian models to match the data regarding a key channel for monetary transmission: the dynamic interactions between macroeconomic variables and their corresponding expectations. We exploit survey expectations data and adopt a dynamic stochastic general equilibrium (DSGE)-VAR approach to assess the extent and sources of model misspecification. The results point to serious misspecification in the expectations-formation side of the DSGE model. The rational expectations hypothesis is primarily responsible for the model's failure to capture the co-movements between observed macroeconomic expectations and realizations. Alternative models of expectations formation help partially reconcile the New Keynesian model with the data.

Many algorithms that provide approximate solutions for dynamic stochastic general equilibrium (DSGE) models employ the QZ factorization because it allows a flexible formulation of the model and exempts the researcher from identifying equations that give raise to infinite eigenvalues. We show, by means of an example, that the policy functions obtained by this approach may differ from both the solution of a properly reduced system and the solution obtained from solving the system of nonlinear equations that arises from applying the implicit function theorem to the model's equilibrium conditions. As a consequence, simulation results may depend on the specific algorithm used and on the numerical values of parameters that are theoretically irrelevant. The sources of this inaccuracy are ill-conditioned matrices as they emerge, e.g., in models with strong habits. Researchers should be aware of those strange effects, and we propose several ways to handle them.

We compare model forecast error statistics with forecast error statistics of professional forecasts. We look at a standard sticky-prices–wages model, concluding that it delivers too strong a theoretical forecastability of the variables under scrutiny, at odds with the data (professional forecasts). We argue that the lack of compatibility between the model and professional forecasts results from trying to fit inflation (which is probably nonstationary) to a model that assumes inflation is stationary. A modified version of the model, one with a varying inflation target, delivers a better fit in terms of forecastability.

Revisions of U.S. macroeconomic data are persistent, correlated with real-time data, and with high variability (around 80% of U.S. real-time data volatility). This paper adapts a DSGE-style model to accommodate both real-time and revised data from the U.S. economy. The results show a lesser role of both habit formation and price indexation than in the standard model. In the simulations, revision shocks to both output and inflation are expansionary because the Fed reacts by cutting interest rates. Consumption revisions, in contrast, are countercyclical, consumption mirrors the observed reduction in real-time consumption. In the variance decomposition, data revisions explain 9.3% of output changes.

This paper assesses the empirical relevance of financial frictions in the Euro Area (EA) and the United States (US). It provides a comprehensive set of comparisons between two models: (i) a Smets and Wouters (SW) [F. Smets and R. Wouters, Shocks and frictions in US business cycles: A Bayesian DSGE approach, American Economic Review 97(3), 586–606 (2007)] model with financial frictions originating in nonfinancial firms and (ii) a SW model with frictions originating in financial intermediaries. The introduction of financial frictions in either way improves the models' fit compared to a standard SW model, and the empirical comparisons reveal that the latter model outperforms the former both in the euro area and in the United States. Two main factors explain this result: first, the magnitude of the financial accelerator effect, and second, the role of the investment-specific technology shock in affecting financial variables.

We model agents' endogenous updating of information sets over time under changing macroeconomic conditions. Building on sticky information models, the degree of inattentiveness is endogenized by allowing agents to choose between a costly full-information predictor and a costless sticky-information predictor. This is modeled as a choice between discrete alternatives under rational inattention. Recursive simulation shows that the dynamic equilibrium paths of aggregate variables are highly persistent and match the moments of U.S. data better than a model with fixed sticky information or with sticky prices, especially with regard to higher moments and the degree of persistence. Predictors are chosen in line with the predictions from rational inattention models, as the aggregate degree of attentiveness increases with rising variance of the forecast variable. Moreover, the model can generate hump-shaped impulse responses of inflation to a monetary policy shock if the degree of inattentiveness is sufficiently high.

Term premia are shown to provide crucial information for discriminating among alternative sources of change in the economy, namely shifts in the variance of structural shocks and in monetary policy. These sources have been identified as competing explanations for time-varying features of major industrial economies during the 1980s and 1990s. Although hardly distinguishable through the lens of standard dynamic stochastic general equilibrium (DSGE) models, lower nonpolicy shock variances and “tighter” monetary policy regimes imply higher and lower term premia, respectively. As a result, moving to tighter monetary policy alone cannot explain the improved U.S. macroeconomic stability in the 1980s and 1990s: term premia would have shifted downward, a fact inconsistent with the evidence of higher premia from early 80s onward. Conversely, favorable shifts in nonpolicy innovation variance imply movements in term premia that are at least qualitatively consistent with historical patterns.

The performance of unit root tests on simulated series is compared, using the business-cycle model of Chang et al. [Journal of Money, Credit and Banking 39(6), 1357–1373 (2007)] as a data-generating process. Overall, Monte Carlo simulations show that the efficient unit root tests of Ng and Perron (NP) [Econometrica 69(6), 1519–1554 (2001)] are more powerful than the standard unit root tests. These efficient tests are frequently able (i) to reject the unit-root hypothesis on simulated series, using the best specification of the business-cycle model found by Chang et al., in which hours worked are stationary with adjustment costs, and (ii) to reduce the gap between the theoretical impulse response functions and those estimated with a Structural VAR model. The results of Monte Carlo simulations show that the hump-shaped behavior of data can explain the divergence between unit root tests.

Recent empirical findings suggest that macroeconomic variables are seldom normally distributed. For example, the distributions of aggregate output growth-rate time series of many OECD countries are well approximated by symmetric exponential-power (EP) densities with Laplace fat tails. In this work, we assess whether real business cycle (RBC) and standard medium-scale New Keynesian (NK) models are able to replicate this statistical regularity. We simulate both models, drawing Gaussian- vs Laplace-distributed shocks, and we explore the statistical properties of simulated time series. Our results cast doubts on whether RBC and NK models are able to provide a satisfactory representation of the transmission mechanisms linking exogenous shocks to macroeconomic dynamics.