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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. Mert Demirer & Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2018. "Estimating global bank network connectedness," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(1), pages 1-15, January.
    2. 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.
    3. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    4. Sokbae Lee & Myung Hwan Seo & Youngki Shin, 2016. "The lasso for high dimensional regression with a possible change point," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 193-210, January.
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    6. Liudas Giraitis & George Kapetanios & Anne Wetherilt & Filip ŽIKEŠ, 2016. "Estimating the Dynamics and Persistence of Financial Networks, with an Application to the Sterling Money Market," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 58-84, January.
    7. Giraitis, L. & Kapetanios, G. & Yates, T., 2014. "Inference on stochastic time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 179(1), pages 46-65.
    8. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    9. Li, Jiahan & Chen, Weiye, 2014. "Forecasting macroeconomic time series: LASSO-based approaches and their forecast combinations with dynamic factor models," International Journal of Forecasting, Elsevier, vol. 30(4), pages 996-1015.
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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

    Statistics

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