Work in Progress

Estimating the Elasticity of Intertemporal Substitution: A Quantile Maximization Approach (with Luciano de Castro and Antonio Galvao)

This paper uses a dynamic consumption-based quantile utility maximization model to estimate the elasticity of intertemporal substitution (EIS) across different levels of risk attitude. In the quantile model, the risk attitude is captured by the quantile and is, therefore, independent of the EIS. This is an advantage with respect to expected utility models, under which risk attitude and EIS are necessarily linked. We derive the quantile Euler equation from the dynamic problem, and use disaggregated consumption data from the Nielsen Consumer Panel together with recently developed smoothed instrumental variables quantile regression for nonlinear models to estimate the corresponding EIS across quantiles. The empirical results document evidence of important heterogeneity of the EIS across different risk attitudes, as well as the presence of negative estimates on part of its distribution. The negative estimates help to shed light and reconcile recent results in the literature documenting strong selective reporting, where negative and insignificant EIS estimates are too often discarded. We also estimate the model for different economic scenarios — expansion, contraction, and quantitative easing. The results show evidence of large heterogeneity in the EIS both across economic scenarios and risk attitudes.

Job Market Paper

Estimating the Elasticity of Intertemporal Substitution with Disaggregated Consumption Data

This paper estimates the elasticity of intertemporal substitution (EIS) of consumption using the Nielsen Consumer Panel. The Nielsen Consumer Panel is built from transactional data that follows households in the United States and their grocery purchases from 2004 to 2014. Because of the transactional nature of the dataset, there is a low source of measurement error in consumption, and aggregation bias can be minimized. Due to changes in the economy during this timeframe, the data is examined for structural breaks. The data suggests evidence for two structural changes in the U.S. economy leading to three regimes. The first regime, 2004 to 2006, was a period of economic expansion, while the second regime, 2007 and 2008, was a period of recession. Lastly, during the third regime, 2009 to 2014, the economy exhibited quantitative easing. The EIS is estimated for each regime using linearized Epstein-Zin preferences and by the use of fixed effects and instrumental variables. The data exhibits no evidence of weak instruments. In order to estimate the EIS, consumption is aggregated weekly, and consumption growth is measured over a four-week time period in order to match four-week Treasury bills. This study adds to the literature by examining individual EIS during different periods of economic activity. With a more complete dataset that has less measurement error and aggregation bias than the existing literature, this study gives evidence of a small and negative EIS during a period of expansion, a small and positive EIS during a period of recession, and a large and positive EIS during quantitative easing.