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Adaptive estimation of AR∞ models with time-varying variances

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  • Zhang, Erhua
  • Wu, Jilin

Abstract

This paper considers adaptive estimation of AR∞ models under time-varying variances of unknown forms. We utilize the sieve method to approximate the autoregressive model of infinite order, and then develop kernel-based estimators of the residual variances and associated adaptive least squares (ALS) estimators of the autoregressive coefficients. We prove the ALS estimator has the same efficiency as its infeasible counterpart. Simulation results show the adaptive procedure can help achieve efficiency gains in finite samples.

Suggested Citation

  • Zhang, Erhua & Wu, Jilin, 2020. "Adaptive estimation of AR∞ models with time-varying variances," Economics Letters, Elsevier, vol. 197(C).
    • Handle: RePEc:eee:ecolet:v:197:y:2020:i:c:s0165176520304018
    DOI: 10.1016/j.econlet.2020.109641
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    References listed on IDEAS

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    Cited by:

    1. Hwang, Eunju & Hong, Won-Tak, 2021. "A multivariate HAR-RV model with heteroscedastic errors and its WLS estimation," Economics Letters, Elsevier, vol. 203(C).

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    More about this item

    Keywords

    Time-varying variances; Sieve approximation; Adaptive estimation; Lag selection;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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