Session 4: Financial/Methods - Estimating the Quantile Elasticity of Intertemporal Substitution with Instrumental Variables Quantile Regression
Presentation Type
Event
Abstract
estimate the quantile elasticity of intertemporal substitution (QEIS) of consumption using instrumental variables quantile regression. The elasticity of intertemporal substitution represents the willingness of a consumer to substitute future consumption for present consumption. In this paper, agents have a quantile utility preference instead of standard expected utility. This allows for the capture of heterogeneity along the conditional distribution of agents. The QEIS considers structural breaks in the data and is estimated for each regime using linearized Epstein-Zin preferences and by the use of fixed effects, instrumental variables, and quantile regression. The estimator is a feasible estimator based on smoothed sample moments. In order to estimate the model, the Nielsen Consumer Panel dataset is used. This dataset is built from transactional data that follows households in the United States and their grocery purchases from 2004 through 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. To estimate the model, consumption is aggregated weekly, and consumption growth is measured over a four-week time period in order to match four-week Treasury bills. Results give evidence of heterogeneity of the QEIS along the quantiles of the conditional distribution.
Start Date
2-12-2018 11:00 AM
End Date
2-12-2018 12:00 PM
Session 4: Financial/Methods - Estimating the Quantile Elasticity of Intertemporal Substitution with Instrumental Variables Quantile Regression
University Student Union: Pasque Room 255
estimate the quantile elasticity of intertemporal substitution (QEIS) of consumption using instrumental variables quantile regression. The elasticity of intertemporal substitution represents the willingness of a consumer to substitute future consumption for present consumption. In this paper, agents have a quantile utility preference instead of standard expected utility. This allows for the capture of heterogeneity along the conditional distribution of agents. The QEIS considers structural breaks in the data and is estimated for each regime using linearized Epstein-Zin preferences and by the use of fixed effects, instrumental variables, and quantile regression. The estimator is a feasible estimator based on smoothed sample moments. In order to estimate the model, the Nielsen Consumer Panel dataset is used. This dataset is built from transactional data that follows households in the United States and their grocery purchases from 2004 through 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. To estimate the model, consumption is aggregated weekly, and consumption growth is measured over a four-week time period in order to match four-week Treasury bills. Results give evidence of heterogeneity of the QEIS along the quantiles of the conditional distribution.