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Time-varying Lasso

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  • Kapetanios, George
  • Zikes, Filip

Abstract

This paper introduces a Lasso-type estimator for large linear models with time-varying parameters. The estimator is easy to implement in practice and standard algorithms developed for Lasso with fixed parameters can be readily used. We derive theoretical properties of the estimator, allowing for deterministic or stochastic smoothly varying parameter processes and discuss ways in which tuning parameters can be data dependent. Monte Carlo simulation and an application to forecasting inflation with macroeconomic variables illustrates the usefulness of our method.

Suggested Citation

  • Kapetanios, George & Zikes, Filip, 2018. "Time-varying Lasso," Economics Letters, Elsevier, vol. 169(C), pages 1-6.
    • Handle: RePEc:eee:ecolet:v:169:y:2018:i:c:p:1-6
    DOI: 10.1016/j.econlet.2018.04.029
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    References listed on IDEAS

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    1. Liudas Giraitis & George Kapetanios & Tony Yates, 2018. "Inference on Multivariate Heteroscedastic Time Varying Random Coefficient Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(2), pages 129-149, March.
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    More about this item

    Keywords

    Large datasets; Structural change; Penalised regressions; Lasso;
    All these keywords.

    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
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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