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Score-driven non-linear multivariate dynamic location models

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  • Blazsek, Szabolcs
  • Escribano, Álvaro
  • Licht, Adrian

Abstract

In this paper, we introduce a new model by extending the dynamic conditional score(DCS) model of the multivariate t-distribution and name it as the quasi-vectorautoregressive (QVAR) model. QVAR is a score-driven nonlinear multivariatedynamic location model, in which the conditional score vector of the log-likelihood (LL)updates the dependent variables. For QVAR, we present the details of theeconometric formulation, the computation of the impulse response function, and themaximum likelihood (ML) estimation and related conditions of consistency andasymptotic normality. As an illustration, we use quarterly data for period 1987:Q1 to2013:Q2 from the following variables: quarterly percentage change in crude oil realprice, quarterly United States (US) inflation rate, and quarterly US real gross domesticproduct (GDP) growth. We find that the statistical performance of QVAR is superior tothat of VAR and VARMA. Interestingly, stochastic annual cyclical effects withdecreasing amplitude are found for QVAR, whereas those cyclical effects are notfound for VAR or VARMA.

Suggested Citation

  • Blazsek, Szabolcs & Escribano, Álvaro & Licht, Adrian, 2017. "Score-driven non-linear multivariate dynamic location models," UC3M Working papers. Economics 25739, Universidad Carlos III de Madrid. Departamento de Economía.
    • Handle: RePEc:cte:werepe:25739
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    References listed on IDEAS

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    1. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107630024, July.
    2. Lutz Kilian, 2008. "A Comparison of the Effects of Exogenous Oil Supply Shocks on Output and Inflation in the G7 Countries," Journal of the European Economic Association, MIT Press, vol. 6(1), pages 78-121, March.
    3. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575, December.
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    Cited by:

    1. Blazsek, Szabolcs & Licht, Adrian, 2018. "Seasonal quasi-vector autoregressive models for macroeconomic data," UC3M Working papers. Economics 26316, Universidad Carlos III de Madrid. Departamento de Economía.
    2. Blazsek, Szabolcs & Licht, Adrian, 2018. "Seasonal Quasi-Vector Autoregressive Models with an Application to Crude Oil Production and Economic Activity in the United States and Canada," UC3M Working papers. Economics 27484, Universidad Carlos III de Madrid. Departamento de Economía.
    3. Blazsek Szabolcs & Escribano Alvaro & Licht Adrian, 2021. "Identification of Seasonal Effects in Impulse Responses Using Score-Driven Multivariate Location Models," Journal of Econometric Methods, De Gruyter, vol. 10(1), pages 53-66, January.
    4. Blazsek, Szabolcs & Licht, Adrian, 2018. "Seasonality Detection in Small Samples using Score-Driven Nonlinear Multivariate Dynamic Location Models," UC3M Working papers. Economics 27483, Universidad Carlos III de Madrid. Departamento de Economía.

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    More about this item

    Keywords

    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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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