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Nonparametric regression for locally stationary time series

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If you have a question about this talk, please contact Richard Samworth .

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.

This talk is part of the Statistics series.

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Zoom (Meeting ID 926 3105 8994, passcode 345661) BAS conference room; https://bas-ac-uk.zoom.us/j/92407581181 SS03 William Gates Building

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