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What to expect when you’re calibrating: measuring the effect of calibration on the estimation of macroeconomic models

DSGE
calibration
information content
transparency

Nikolay Iskrev, “What to expect when you’re calibrating: measuring the effect of calibration on the estimation of macroeconomic models,” Journal of Economic Dynamic and Control 99 (2019): 54-81, doi: 10.1016/j.jedc.2018.12.002

Author
Affiliation

Nikolay Iskrev

Banco de Portugal

Published

February 2019

Doi

10.1016/j.jedc.2018.12.002

Abstract

I propose two measures of the impact of calibration on the estimation of macroeconomic models. The first quantifies the amount of information introduced with respect to each estimated parameter as a result of fixing the value of one or more calibrated parameters. The second is a measure of the sensitivity of parameter estimates to perturbations in the calibration values. The purpose of the measures is to show researchers how much and in what way calibration affects their estimation results – by shifting the location and reducing the spread of the marginal posterior distributions of the estimated parameters. Such analysis is often appropriate since macroeconomists do not always agree on whether and how to calibrate structural parameters in macroeconomic models. The methodology is illustrated using the models estimated in Smets and Wouters (2007) and Schmitt-Grohé and Uribe (2012).

Citation

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@article{ISKREV201954,
title = {What to expect when you're calibrating: Measuring the effect of calibration on the estimation of macroeconomic models},
journal = {Journal of Economic Dynamics and Control},
volume = {99},
pages = {54-81},
year = {2019},
issn = {0165-1889},
doi = {https://doi.org/10.1016/j.jedc.2018.12.002},
url = {https://www.sciencedirect.com/science/article/pii/S0165188918303907},
author = {Nikolay Iskrev},
keywords = {DSGE models, Information content, Calibration, Estimation, Identification},
abstract = {I propose two measures of the impact of calibration on the estimation of macroeconomic models. The first quantifies the amount of information introduced with respect to each estimated parameter as a result of fixing the value of one or more calibrated parameters. The second is a measure of the sensitivity of parameter estimates to perturbations in the calibration values. The purpose of the measures is to show researchers how much and in what way calibration affects their estimation results – by shifting the location and reducing the spread of the marginal posterior distributions of the estimated parameters. Such analysis is often appropriate since macroeconomists do not always agree on whether and how to calibrate structural parameters in macroeconomic models. The methodology is illustrated using the models estimated in Smets and Wouters (2007) and Schmitt-Grohé and Uribe (2012).}
}