Additive Nonparametric Regression with Autocorrelated Errors

Michael Smith, Chi-Ming Wong and Robert Kohn

No 267921, Department of Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: A Bayesian approach is presented for nonparametric estimation of an additive regression model with autocorrelated errors. Each of the potentially nonlinear components is modelled as a regression spline using many knots, while the errors are modelled by a high order stationary autoregressive process parameterised in terms of its autocorrelations. The distribution of significant knots and partial autocorrelations is accounted for using subset selection. Our approach also allows the selection of a suitable transformation of the dependent variable. All aspects of the model are estimated simultaneously using Markov chain Monte Carlo. It is shown empirically that the proposed approach works well on a number of simulated and real examples.

Keywords: Research; Methods/Statistical; Methods (search for similar items in EconPapers)
Pages: 38
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Persistent link: https://EconPapers.repec.org/RePEc:ags:monebs:267921

DOI: 10.22004/ag.econ.267921

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