Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
This paper discusses nonparametric models for panels of time series. There is already a substantial literature on nonlinear models and nonparametric methods in a regression and time series setting. But almost without exception these developments have been limited to univariate and multivariate models of moderate dimensions. Very little has been done for panels, where the dimension, often corresponding to a number of individuals, typically is very large but where the number of observations for each individual may be small or moderate. It is the aim of this paper to start a systematic theoretical treatment of nonparametric models for panels of time series, in particular on additive models. Extending existing methodology to the panel situation is by no means trivial because already for the parametric case many problems are unsolved. Our estimation approach is based on backfitting methods.