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Co-integration and common trends analysis with score-driven models : an application to the federal funds effective rate and US inflation rate

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  • Blazsek, Szabolcs
  • Licht, Adrian

Abstract

Co-integration and common trends are studied for time series variables, by introducing the new t-QVARMA (quasi-vector autoregressive moving average) model. t-QVARMA is an outlier-robust nonlinear score-driven model for the multivariate t-distribution. In t-QVARMA, the I(0) and I(1) components of the variables are separated in a way that is similar to the Granger-representation of VAR models. The relationship between the co-integrated federal funds effective rate and United States (US) inflation rate variables is studied for the period of July 1954 to January 2019. The in-sample statistical and out-of-sample forecasting performances of t-QVARMA are superior to those of the classical Gaussian-VAR model

Suggested Citation

  • Blazsek, Szabolcs & Licht, Adrian, 2019. "Co-integration and common trends analysis with score-driven models : an application to the federal funds effective rate and US inflation rate," UC3M Working papers. Economics 28451, Universidad Carlos III de Madrid. Departamento de Economía.
    • Handle: RePEc:cte:werepe:28451
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    References listed on IDEAS

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    Cited by:

    1. Giacomo Bormetti & Fulvio Corsi, 2021. "A Lucas Critique Compliant SVAR model with Observation-driven Time-varying Parameters," Papers 2107.05263, arXiv.org, revised Feb 2022.

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    More about this item

    Keywords

    Multivariate Dynamic Conditional Score (Dcs) Models;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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