This research thesis is devoted to the analysis of sentiments and expectations in the system of economic markets with Dynamic Partial Least Squares (DPLS) models with time-series. The analysis of complex systems with large data sets and discovering relevant patterns require the use of modern statistical methods. The DPLS models, a variant of structural equation models with latent variables, are methodically extended to include multiple lags simultaneously. The econometric models try to identify a large number of latent factors and their unobservable relations. The data consists of about 80 indicators from January 1991 to June 2010 to quantify sentiments, expectations and economically relevant variables and examine the relations in detail. The results show, that the sentiments, the assessment of the current economic situation, are clearly linked with economic variables like investments, incoming orders and stock market developments. The expectations, the assessment of future development, provide a weak to moderate predictive power for up to 18 months in the future. For shorter periods of six months sentiments and expectations are the best available forecast variables. The analysis of model deviations allows to draw conclusions about theoretical concepts such as rationality of expectations. There seem to be phases of systematic over- and undervaluation of the current situation and future development, especially before and after economic crisis. The results should provide a deeper insight into the empirical relationships between subjective assessments and real economic variables.