The statistical parsing of morphologically rich languages is hindered by the inability of parsers to collect solid statistics because of the large number of word types in such languages. There are however two separate but connected problems, reducing data sparsity of known words and handling rare and unknown words. Methods for tackling one problem may inadvertently negatively impact methods to handle the other. We perform a tightly controlled set of experiments to reduce data sparsity through class-based representations in combination with unknown word signatures with two PCFG-LA parsers that handle rare and unknown words differently on the German TiGer treebank. We demonstrate that methods that have improved results for other languages do not transfer directly to German, and that we can obtain better results using a simplistic model rather than a more generalized model for rare and unknown word handling.