Data science team
CPB always strives to enrich its scientific and policy-oriented research with data science techniques, thus offering more insight into important social issues. To this end, we are always gathering new knowledge on new data-driven forecasting methods (machine learning) and disseminate this knowledge both within CPB and to the outside world.
March 1, 2023
Predicting Firm Exits with Machine Learning
In this paper, we use machine learning techniques to predict whether a company would have left the market in a world without corona. These predictions show that unhealthy companies applied for support less often than healthy companies. But we also show that the COVID-19 support has prevented most exits among unhealthy companies. This indicates that the corona support measures have had a negative impact on productivity growth. →
September 29, 2022
Temporal Patterns in Economics Research
May 25, 2022
Meerjarenonderzoeksplan 2022-2024
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Related
Predicting Firm Exits with Machine Learning: Implications for Selection into COVID-19 Support and Productivity GrowthForecasting World Trade Using Big Data and Machine Learning TechniquesTemporal Patterns in Economics ResearchMeerjarenonderzoeksplan 2022-2024Addressing Unemployment Rate Forecast Errors in Relation to the Business Cycle