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Nonparametric regression for locally stationary time series

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  • Michael Vogt

    (Institute for Fiscal Studies)

Abstract

In this paper, we study nonparametric models allowing for locally stationary regressors and a regression function that changes smoothly over time. These models are a natural extension of time series models with time-varying coefficients. We introduce a kernel-based method to estimate the time-varying regression function and provide asymptotic theory for our estimates. Moreover, we show that the main conditions of the theory are satisfied for a large class of nonlinear autoregressive processes with a time-varying regression function. Finally, we examine structured models where the regression function splits up into time-varying additive components. As will be seen, estimation in these models does not suffer from the curse of dimensionality. We complement the technical analysis of the paper by an application to financial data.

Suggested Citation

  • Michael Vogt, 2012. "Nonparametric regression for locally stationary time series," CeMMAP working papers CWP22/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:22/12
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    References listed on IDEAS

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    Keywords

    local stationarity; nonparametric regression; smooth backfitting;
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