Redistribution and fiscal policy

This paper studies the optimal behavior of a democratic government in its use of fiscal policies to redistribute income. I present a stochastic dynamic general equilibrium model with heterogeneous agents to analyze (1) the differences between the effects on the optimal tax rate of permanent and nonpermanent perturbations and (2) the relationship between initial inequality and both steady-state levy and income distribution. In addition, the optimal fiscal policy for the transition is calculated. The analysis leads me to three main conclusions. First, there are no important differences between how taxes respond to a permanent or nonpermanent perturbation. Second, the initial inequality has a huge effect on both actual levy and actual income distribution. And finally, the Chari, Christiano, and Kehoe (1992) result, i.e., taxes on labor are roughly constant over the business cycle, holds only if the productivity ratio is constant. In addition, the model implies a positive correlation between inequality and tax rate, just as in the basic literature.Taxation ; Income distribution

Inflation persistence: how much can we explain?

Until recently most macroeconomic models in which monetary policy has real effects were based on the assumption that agents in the economy do not use all available information when making a decision. Critics of these models argue that this assumption implies that agents are not rational. ; In response to this criticism, a class of New Keynesian models has recently been proposed. These models combine "old" Keynesian elements with an environment in which agents form their expectations rationally. The simplest version of such models includes only one type of nominal rigidity, either sticky prices or sticky wages-that is, prices or wages that adjust only slowly to market shortages or surpluses. But these simple models have a drawback: They do not seem to be able to reproduce the persistence of inflation observed in the data. ; This article explores whether adding sticky wages to the baseline sticky-price model solves the persistence-of-inflation problem when plausible durations of price and wage contracts are assumed. ; The analysis confirms that the baseline sticky-price model cannot replicate the observed inflation persistence unless an implausible degree of either price stickiness or exogenous nominal interest rate persistence is assumed. The findings also show that a model with both sticky prices and sticky wages can replicate more closely the autocorrelation function of inflation, even with acceptable levels of both price and wage stickiness.Inflation (Finance) ; Econometric models

Estimating Macroeconomic Models: A Likelihood Approach

This paper shows how particle filtering allows us to undertake likelihood-based inference in dynamic macroeconomic models. The models can be nonlinear and/or non-normal. We describe how to use the output from the particle filter to estimate the structural parameters of the model, those characterizing preferences and technology, and to compare different economies. Both tasks can be implemented from either a classical or a Bayesian perspective. We illustrate the technique by estimating a business cycle model with investment-specific technological change, preference shocks, and stochastic volatility.

Estimating Dynamic Equilibrium Economies: Linear versus Nonlinear Likelihood

This paper compares two methods for undertaking likelihood-based inference in dynamic equilibrium economies: a Sequential Monte Carlo filter proposed by Fernández-Villaverde and Rubio-Ramírez (2004) and the Kalman filter. The Sequential Monte Carlo filter exploits the nonlinear structure of the economy and evaluates the likelihood function of the model by simulation methods. The Kalman filter estimates a linearization of the economy around the steady state. We report two main results. First, both for simulated and for real data, the Sequential Monte Carlo filter delivers a substantially better fit of the model to the data as measured by the marginal likelihood. This is true even for a nearly linear case. Second, the differences in terms of point estimates, even if relatively small in absolute values, have important effects on the moments of the model. We conclude that the nonlinear filter is a superior procedure for taking models to the data.Likelihood-Based Inference, Dynamic Equilibrium Economies, Nonlinear Filtering, Kalman Filter, Sequential Monte Carlo

Estimating Nonlinear Dynamic Equilibrium economies: A Likelihood Approach

This paper presents a framework to undertake likelihood-based inference in nonlinear dynamic equilibrium economies. We develop a Sequential Monte Carlo algorithm that delivers an estimate of the likelihood function of the model using simulation methods. This likelihood can be used for parameter estimation and for model comparison. The algorithm can deal both with nonlinearities of the economy and with the presence of non-normal shocks. We show consistency of the estimate and its good performance in finite simulations. This new algorithm is important because the existing empirical literature that wanted to follow a likelihood approach was limited to the estimation of linear models with Gaussian innovations. We apply our procedure to estimate the structural parameters of the neoclassical growth model.Likelihood-Based Inference, Dynamic Equilibrium Economies, Nonlinear Filtering, Sequential Monte Carlo)

Cointegrated TFP processes and international business cycles

A puzzle in international macroeconomics is that observed real exchange rates are highly volatile. Standard international real business cycle (IRBC) models cannot reproduce this fact. We show that total factor productivity processes for the United States and the rest of the world are characterized by a vector error correction model (VECM) and that adding cointegrated technology shocks to the standard IRBC model helps explaining the observed high real exchange rate volatility. Also, we show that the observed increase of the real exchange rate volatility with respect to output in the past twenty years can be explained by changes in the parameter of the VECM.

Smoothing the shocks of a dynamic stochastic general equilibrium model

In some ways, the recession of 2001 and the recovery that followed it were unique: During the recession, the contraction in measured output was driven almost entirely by a retrenchment in business capital spending while consumer spending and residential investment remained positive. And the recovery was marked by moderate, uneven gross domestic product growth and job market weakness that were historically unusual. These events raise questions about the conventional wisdom on post–World War II business cycles. ; To help answer these questions, the authors use a general equilibrium model with sticky prices and sticky wages as a framework for exploring the effects of structural shocks to the U.S. economy. Using the Kalman filter, the authors estimate the parameters of the model and then back out the unobservable shocks that make the model’s observed variables match the observable data. ; The model shows that during the 1990–91 and 2001 recessions demand shocks turned sharply negative as output growth weakened. However, the model attributes the relatively small decline in output during the 2001 recession to a positive productivity shock. Both the 1990–91 and 2001 recessions exhibited a sudden loosening of monetary policy greater than would be predicted by a Taylor rule. The model does not capture inflation dynamics during these periods and attributes frequent changes in inflation to the markup shock.Business cycles ; Econometric models

MEDEA: A DSGE Model for the Spanish Economy

In this paper, we provide a brief introduction to a new macroeconometric model of the Spanish economy named MEDEA (Modelo de Equilibrio Dinámico de la Economía EspañolA). MEDEA is a dynamic stochastic general equilibrium (DSGE) model that aims to describe the main features of the Spanish economy for policy analysis, counterfactual exercises, and forecasting. MEDEA is built in the tradition of New Keynesian models with real and nominal rigidities, but it also incorporates aspects such as a small open economy framework, an outside monetary authority such as the ECB, and population growth, factors that are important in accounting for aggregate fluctuations in Spain. The model is estimated with Bayesian techniques and data from the last two decades. Beyond describing the properties of the model, we perform different exercises to illustrate the potential of MEDEA, including historical decompositions, long-run and short-run simulations, and counterfactual experiments.DSGE Models, Likelihood Estimation, Bayesian Methods

Convergence Properties of the Likelihood of Computed Dynamic Models

This paper studies the econometrics of computed dynamic models. Since these models generally lack a closed-form solution, economists approximate the policy functions of the agents in the model with numerical methods. But this implies that, instead of the exact likelihood function, the researcher can evaluate only an approximated likelihood associated with the approximated policy function. What are the consequences for inference of the use of approximated likelihoods? First, we show that as the approximated policy function converges to the exact policy, the approximated likelihood also converges to the exact likelihood. Second, we prove that the approximated likelihood converges at the same rate as the approximated policy function. Third, we find that the error in the approximated likelihood gets compounded with the size of the sample. Fourth, we discuss convergence of Bayesian and classical estimates. We complete the paper with three applications to document the quantitative importance of our results.computed dynamic models, likelihood inference, asymptotic properties