Consumption over the Life Cycle: Facts from Consumer Expenditure Survey Data

This paper uses a seminonparametric model and Consumer Expenditure Survey data to estimate life cycle profiles of consumption, controlling for demographics, cohort and time e.ects. In addition to documenting profiles for total and nondurable consumption, we devote special attention to the age expenditure pattern for consumer durables. We find hump-shaped paths over the life cycle for total, for nondurable and for durable expenditures. Changes in household size account for roughly half of these humps. The other half remains unaccounted for by the standard complete markets life cycle model. Our results imply that households do not smooth consumption over their lifetimes. This is especially true for services from consumer durables. Bootstrap simulations suggest that our empirical estimates are tight and sensitivity analysis indicates that the computed profiles are robust to a large number of different specifications.

Consumption over the Life Cycle: Some Facts from Consumer Expenditure Survey Data

This paper uses a seminonparametric model and Consumer Expenditure Survey data to estimate life cycle profiles of consumption, controlling for demographics, cohort and time effects. In addition to documenting profiles for total and nondurable consumption, we devote special attention to the age expenditure pattern for consumer durables. We find hump-shaped paths over the life cycle for total, for nondurable and for durable expenditures. Changes in household size account for roughly half of these humps. The other half remains unaccounted for by the standard complete markets life cycle model. Our results imply that households do not smooth consumption over their lifetimes. This is especially the case for services from consumer durables. Bootstrap simulations suggest that our empirical estimates are tight and sensitivity analysis indicates that the computed profiles are robust to a large number of different specifications.Consumption, Durables, CEX

Cryptocurrency Competition and the U.S. Monetary System

Advocates of cryptocurrencies such as Bitcoin believe that having currency competition will help achieve the economic objective of price stability. This Issue Brief summarizes research that explores whether competition among privately issued fiat currencies can actually produce price stability. The research finds that in most cases, a system of private monies does not deliver price stability. And even when it does, it always is subject to self-fulfilling inflationary episodes, and it supplies a suboptimal amount of money. Although there is no economic reason to curb the use of cryptocurrencies at the moment, it is important to review key regulatory issues that policymakers need to consider now, before the use of cryptocurrencies becomes even more widespread.https://repository.upenn.edu/pennwhartonppi/1057/thumbnail.jp

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 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 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)

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, their policy functions are approximated by numerical methods. Hence, the researcher can only evaluate an approximated likelihood associated with the approximated policy function rather than the exact likelihood implied by the exact policy function. What are the consequences for inference of the use of approximated likelihoods? First, we find conditions under which, as the approximated policy function converges to the exact policy, the approximated likelihood also converges to the exact likelihood. Second, we show that second order approximation errors in the policy function, which almost always are ignored by researchers, have first order effects on the likelihood function. Third, we discuss convergence of Bayesian and classical estimates. Finally, we propose to use a likelihood ratio test as a diagnostic device for problems derived from the use of approximated likelihoods.

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