This chapter is concerned with different approaches to accounting for trend and seasonal components. We consider both deterministic and stochastic approaches and show the overlap and contrast between these approaches. Estimation and inference are treated.

This chapter deals with features of data that suggest a certain model or method, but where this suggestion is erroneous. We highlight a few cases in which an econometrician could be directed in the wrong direction, and at the same time we show how this can be prevented from happening. These situations happen in cases where there is no strong prior information on how the model should be specified. The data are then used to guide model construction. This guidance can be in an inappropriate direction. We review a few empirical cases where some data features obscure a potentially proper view of the data and may suggest inappropriate models. We discuss spurious cycles and the impact of additive outliers on detecting ARCH and nonlinearity. We also focus on a time series that may exhibit recessions and expansions, allowing you to (wrongly) interpret the recession observations as outliers. Finally, we deal with structural breaks and trends and unit roots, and see how data with these features can look alike.