Arbeitspapier

The common and speci fic components of inflation expectation across European countries

Inflation expectation (IE) is often considered to be an important determinant of actual inflation in modern economic theory, we are interested in investigating the main risk factors that determine its dynamics. We fiirst apply a joint arbitrage-free term structure model across different European countries to obtain estimate for country-specific IE. Then we use the two-component and three-component models to capture the main risk factors. We discover that the extracted common trend for IE is an important driver for each country of interest. Moreover a spatial-temporal copula model is tted to account for the non-Gaussian dependency across countries. This paper aims to extract informative estimates for IE and provide good implications for monetary policies.

Sprache
Englisch

Erschienen in
Series: IRTG 1792 Discussion Paper ; No. 2020-023

Klassifikation
Wirtschaft
Mathematical Methods
Estimation: General
Multiple or Simultaneous Equation Models: Classification Methods; Cluster Analysis; Principal Components; Factor Models
Price Level; Inflation; Deflation
Interest Rates: Determination, Term Structure, and Effects
Thema
in ation expectation
joint yield-curve modeling
factor model
common trend
spatial-temporal copulas

Ereignis
Geistige Schöpfung
(wer)
Chen, Shi
Härdle, Wolfgang Karl
Wang, Weining
Ereignis
Veröffentlichung
(wer)
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
(wo)
Berlin
(wann)
2020

Handle
Letzte Aktualisierung
10.03.2025, 11:41 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Chen, Shi
  • Härdle, Wolfgang Karl
  • Wang, Weining
  • Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

Entstanden

  • 2020

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