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http://hdl.handle.net/10419/64773
  
Title:Nonparametric regression for locally stationary time series PDF Logo
Authors:Vogt, Michael
Issue Date:2012
Series/Report no.:cemmap working paper CWP22/12
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 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.
Subjects:local stationarity
nonparametric regression
smooth backfitting
Persistent Identifier of the first edition:doi:10.1920/wp.cem.2012.2212
Document Type:Working Paper
Appears in Collections:cemmap working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies (IFS)

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