Sensitivity Analysis of Taylor Curve Estimation: A Comparison of GARCH and Stochastic Volatility Models

Dominik Kavřík (kavd00@vse.cz)
Additional contact information
Dominik Kavřík: Prague University of Economics and Business

A chapter in New Perspectives and Paradigms in Applied Economics and Business, 2024, pp 17-24 from Springer

Abstract: Abstract The Taylor curve is a concept in macroeconomic policy analysis that represents the efficiency frontier of monetary policy. Previous studies have used generalized autoregressive conditional heteroskedasticity (GARCH) models to estimate the conditional volatility of inflation and output gap, which are key inputs for estimating the Taylor curve. However, the sensitivity of these results to the choice of the volatility model can be substantial. The purpose of this study is to compare the performance of the GARCH and stochastic volatility models for estimating the conditional volatilities of output gap and inflation, which are key inputs to the time-varying parameter model used to empirically test the Taylor curve. Sensitivity analysis is conducted by estimating the conditional volatilities using both models and comparing the resulting estimates of the Taylor curve. The results show that the choice of the volatility model can have an impact on the estimated efficiency frontier for the evaluation of pre-financial crisis period in the United States.

Keywords: Monetary policy; Volatility models; Taylor curve (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-49951-7_2

Ordering information: This item can be ordered from
http://www.springer.com/9783031499517

DOI: 10.1007/978-3-031-49951-7_2

Access Statistics for this chapter

More chapters in Springer Proceedings in Business and Economics from Springer
Bibliographic data for series maintained by Sonal Shukla (sonal.shukla@springer.com) and Springer Nature Abstracting and Indexing (indexing@springernature.com).