Estimation with Aggregate Shocks

TL;DR

This paper develops an econometric framework that combines cross-sectional and time-series data to accurately estimate models affected by aggregate shocks, addressing limitations of previous methods that ignored such shocks.

Contribution

It introduces a novel approach that explicitly models agents' inference under aggregate uncertainty, providing formulas for inference and demonstrating the importance of combined data sources.

Findings

The proposed method improves parameter estimation accuracy.

Ignoring aggregate shocks leads to biased inference.

Monte Carlo simulations validate the effectiveness of the framework.

Abstract

Aggregate shocks affect most households' and firms' decisions. Using three stylized models we show that inference based on cross-sectional data alone generally fails to correctly account for decision making of rational agents facing aggregate uncertainty. We propose an econometric framework that overcomes these problems by explicitly parameterizing the agents' inference problem relative to aggregate shocks. Our framework and examples illustrate that the cross-sectional and time-series aspects of the model are often interdependent. Therefore, estimation of model parameters in the presence of aggregate shocks requires the combined use of cross-sectional and time series data. We provide easy-to-use formulas for test statistics and confidence intervals that account for the interaction between the cross-sectional and time-series variation. Lastly, we perform Monte Carlo simulations that highlight the properties of the proposed method and the risks of not properly accounting for the presence of aggregate shocks.