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Large Time-Varying Parameter VARs: A Non-Parametric Approach

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  • Kapetanios, George
  • Marcellino, Massimiliano
  • Venditti, Fabrizio

Abstract

In this paper we introduce a nonparametric estimation method for a large Vector Autoregression (VAR) with time-varying parameters. The estimators and their asymptotic distributions are available in closed form. This makes the method computationally efficient and capable of handling information sets as large as those typically handled by factor models and Factor Augmented VARs (FAVAR). When applied to the problem of forecasting key macroeconomic variables, the method outperforms constant parameter benchmarks and large (parametric) Bayesian VARs with time-varying parameters. The tool can also be used for structural analysis. As an example, we study the time-varying effects of oil price innovations on sectoral U.S. industrial output. We find that the changing interaction between unexpected oil price increases and business cycle fluctuations is shaped by the durable materials sector, rather by the automotive sector on which a large part of the literature has typically focused.

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  • Kapetanios, George & Marcellino, Massimiliano & Venditti, Fabrizio, 2016. "Large Time-Varying Parameter VARs: A Non-Parametric Approach," CEPR Discussion Papers 11560, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:11560
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    Cited by:

    1. Delle Monache, Davide & Petrella, Ivan & Venditti, Fabrizio, 2019. "Price Dividend Ratio and Long-Run Stock Returns: a Score Driven State Space Model," EMF Research Papers 29, Economic Modelling and Forecasting Group.
    2. George Kapetanios & Fotis Papailias, 2018. "Big Data & Macroeconomic Nowcasting: Methodological Review," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-12, Economic Statistics Centre of Excellence (ESCoE).
    3. Markus Heinrich & Magnus Reif, 2020. "Real-Time Forecasting Using Mixed-Frequency VARS with Time-Varying Parameters," CESifo Working Paper Series 8054, CESifo.
    4. Jozef Barunik & Michael Ellington, 2020. "Dynamic Network Risk," Papers 2006.04639, arXiv.org, revised Jul 2020.
    5. S. Avouyi-Dovi & C. Labonne & R. Lecat & S. Ray, 2017. "Insight from a Time-Varying VAR Model with Stochastic Volatility of the French Housing and Credit Markets," Working papers 620, Banque de France.
    6. Wei, Jie & Zhang, Yonghui, 2020. "A time-varying diffusion index forecasting model," Economics Letters, Elsevier, vol. 193(C).
    7. Mike Tsionas & Marwan Izzeldin & Lorenzo Trapani, 2019. "Bayesian estimation of large dimensional time varying VARs using copulas," Papers 1912.12527, arXiv.org.
    8. Sergei Seleznev, 2019. "Truncated priors for tempered hierarchical Dirichlet process vector autoregression," Bank of Russia Working Paper Series wps47, Bank of Russia.
    9. César Castro & Rebeca Jiménez-Rodríguez, 2020. "Dynamic interactions between oil price and exchange rate," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-20, August.
    10. Philippe Goulet Coulombe, 2020. "Time-Varying Parameters as Ridge Regressions," Papers 2009.00401, arXiv.org, revised Mar 2021.

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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