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Specification and structural break tests for additive models with applications to realized variance data

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  • Fengler, M.R.
  • Mammen, E.
  • Vogt, M.

Abstract

We study two types of testing problems in a nonparametric additive model setting: We develop methods to test (i) whether an additive component function has a given parametric form and (ii) whether an additive component has a structural break. We apply the theory to a nonparametric extension of the linear heterogeneous autoregressive model which is widely employed to describe realized variance data. We find that the linearity assumption is often rejected, but actual deviations from linearity are mild.

Suggested Citation

  • Fengler, M.R. & Mammen, E. & Vogt, M., 2015. "Specification and structural break tests for additive models with applications to realized variance data," Journal of Econometrics, Elsevier, vol. 188(1), pages 196-218.
    • Handle: RePEc:eee:econom:v:188:y:2015:i:1:p:196-218
    DOI: 10.1016/j.jeconom.2015.04.002
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    Cited by:

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    6. Audrino, Francesco & Sigrist, Fabio & Ballinari, Daniele, 2020. "The impact of sentiment and attention measures on stock market volatility," International Journal of Forecasting, Elsevier, vol. 36(2), pages 334-357.
    7. Lu, Botao & Ma, Feng & Wang, Jiqian & Ding, Hui & Wahab, M.I.M., 2021. "Harnessing the decomposed realized measures for volatility forecasting: Evidence from the US stock market," International Review of Economics & Finance, Elsevier, vol. 72(C), pages 672-689.

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    More about this item

    Keywords

    Additive models; Backfitting; Nonparametric time series analysis; Specification tests; Structural break tests; Realized variance; Heterogeneous autoregressive model;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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