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Statistical models can be used to identify cause-effect relationships and dependencies within (economic) data.
In their simplest form, regression analyses can help quantify correlations between variables, for example. Depending on the kind of data available, time series or panel data models can be used to analyze temporal dynamics and causal relationships. We use regression and machine-learning models for predictive purposes.
Opportunities
We also identify hidden information in data. By doing so, we uncover relationships and identify dependencies that often prove useful in our studies and communications.
What share of companies can be seen as digital, innovative or resilient? We can answer these and other questions by applying cluster analyses and formulating recommendations tailored to your target group’s needs.
As a first step, we analyze correlations and other dependencies, often revealing exciting trends.
A deeper analysis enables us to distinguish between correlations and causal relationships.
Once a causal relationship has been identified, the potential outcome framework is in place. This allows us to answer questions such as: “What would Germany’s GDP be if there were 10% more startups?"
Projects, studies and rankings
We identify correlations and relationships, identify meaningful company clusters, and perform statistical significance tests.
Wirtschaftlich waren in Deutschland die letzten Jahre geprägt von einem langsamen Abstieg. Die Studie befasst sich mit Potenzialräumen für mehr…
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We operate and are responsible for the operational business of BCON² GmbH as a subsidiary of ECLASS e.V. and offer supporting services and advice…
We are developing a global data standard for goods and services for ECLASS e.V. in close cooperation with experts from industry and trade.
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