New Methods and Theory for the Comparison of Nonparametric Trend Curves

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The main purpose of the project is to develop new methods and theory for the analysis of nonparametric time trend curves. Recently, there has been a growing interest in econometric models with non- and semiparametric time trends. Non- and semiparametric trend modelling has attracted particular interest in a panel data context. Important questions are whether the observed time series in the panel all have the same trend or whether they can be clustered into groups with the same trend. A number of test and clustering methods have been developed in the literature to approach these questions, which are relevant in a variety of economic and financial applications. Most of the proposed methods, however, depend on a number of bandwidth or smoothing parameters whose optimal choice is a notoriously difficult problem. In our project, we tackle the challenge of developing new test and clustering methods which are free of classic bandwidth parameters and thus avoid the issue of bandwidth selection. To achieve this, we will build on techniques from statistical multiscale testing which have recently been introduced into the literature. The methodological and theoretical analysis of the project will be complemented by simulations and empirical applications. In particular, we intend to apply the developed methods to an empirical question of interest in macroeconomics, that is, the question of whether real GDP growth has been faster in some countries than in others.