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Adaptive Models and Heavy Tails with an Application to Inflation Forecasting

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  • Delle Monache, Davide

    (Bank of Italy)

  • Petrella, Ivan

    (Warwick Business School and CEPR)

Abstract

This paper introduces an adaptive algorithm for time-varying autoregressive models in the presence of heavy tails. The evolution of the parameters is determined by the score of the conditional distribution, the resulting model is observation-driven and is estimated by classical methods. In particular, we consider time variation in both coeficients and volatility, emphasizing how the two interact with each other. Meaningful restrictions are imposed on the model parameters so as to attain local stationarity and bounded mean values. The model is applied to the analysis of in ation dynamics with the following results: allowing for heavy tails leads to signifficant improvements in terms of it and forecast, and the adoption of the Student-t distribution proves to be crucial in order to obtain well calibrated density forecasts. These results are obtained using the US CPI infl ation rate and are confirmed by other in ation indicators, as well as for CPI infl ation of the other G7 countries.

Suggested Citation

  • Delle Monache, Davide & Petrella, Ivan, 2016. "Adaptive Models and Heavy Tails with an Application to Inflation Forecasting," EMF Research Papers 13, Economic Modelling and Forecasting Group.
  • Handle: RePEc:wrk:wrkemf:13
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    Cited by:

    1. F Blasques & P Gorgi & S Koopman & O Wintenberger, 2016. "Feasible Invertibility Conditions for Maximum Likelihood Estimation for Observation-Driven Models ," Working Papers hal-01377971, HAL.
    2. Mogens Fosgerau & Jinwon Kim & Abhishek Ranjan, 2017. "Vickrey Meets Alonso: Commute Scheduling and Congestion in a Monocentric City," Discussion Papers 17-25, University of Copenhagen. Department of Economics.
    3. Paolo Gorgi & Siem Jan (S.J.) Koopman & Mengheng Li, 2018. "Forecasting economic time series using score-driven dynamic models with mixed-data sampling," Tinbergen Institute Discussion Papers 18-026/III, Tinbergen Institute.

    More about this item

    Keywords

    adaptive algorithm ; in flation ; score-driven models ; student-t ; timevarying parameters JEL Classification Numbers: C22 ; C51 C53 E31;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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