Year: 2018
Type: article
Abstract: Consumption Euler equations are important tools in empirical macroeconomics. When estimated on micro data, they are typically linearized, so standard IV or GMM methods can be employed to deal with the measurement error that is endemic to survey data. However, linearization, in turn, may induce serious approximation bias. We numerically solve and simulate six different life-cycle models, and then use the simulated data as the basis for a series of Monte Carlo experiments in which we evaluate the performance of linearized Euler equation estimation. We sample from the simulated data in ways that mimic realistic data structures. The linearized Euler equation leads to biased estimates of the EIS, but that bias is modest when there is a sufficient time dimension to the data, and sufficient variation in interest rates. However, a sufficient time dimension can only realistically be achieved with a synthetic cohort. Estimates from synthetic cohorts of sufficient length, while often exhibiting small mean bias, are quite imprecise. We also show that in all data structures, estimates are less precise in impatient models.
Source: Macroeconomic Dynamics
Authors: Sule Alan, Kadir Atalay, Thomas F. Crossley
Language: English
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,550
Awards: Centre for the Microeconomic Analysis of Public Policy, G6250724980, The Research Centre for Micro-Social Change (MiSoC): Understanding individual and family behaviours in a new era of uncertainty and change, Family Responses to Economic Shocks and Policy Reforms over the Life Cycle, G8877997352
OpenAlex ID: W1514297678