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dc.contributor.authorVogt, Michaelen_US
dc.description.abstractIn 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 satis ed 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 su er from the curse of dimensionality. We complement the technical analysis of the paper by an application to financial data.en_US
dc.publisher|aCentre for Microdata Methods and Practice (cemmap) |cLondonen_US
dc.relation.ispartofseries|acemmap working paper |xCWP22/12en_US
dc.subject.keywordlocal stationarityen_US
dc.subject.keywordnonparametric regressionen_US
dc.subject.keywordsmooth backfittingen_US
dc.titleNonparametric regression for locally stationary time seriesen_US
dc.typeWorking Paperen_US

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