Thesis supervisor: Ádám Czelleng
Location of studies (in Hungarian): Budapest Business School Abbreviation of location of studies: BGE
Description of the research topic:
Modelling became one of the most emphasized tools for decision makers in the field of monetary policy, fiscal policy, microeconomic policies, investments and corporate strategies. Modelling is based to an increasing extent on available data with potential implications for almost every industry. This research topic combines forecasting and big data analysis.
The analytical tool of mainstream macroeconomics is mathematical modelling designed to describe the operation of the economy based on theoretical principles. Modelling is applied in every international institution (UN, OECD, IMF, European Commission, World Bank, etc.), by international and domestic companies. The research topic fits well to international research objectives and trends.
Big data based forecasting and decision making is not considered a privilege of large businesses but small and medium-sized companies can use and benefit from them as well. Impact assessment and/or forecasting (partly based on big data) can be crucial for sophisticated decision making processes regardless of corporate size.
Nevertheless, by using big data analysis and modelling, SMEs can make their production more efficient, plan their marketing channels better in order to raise their sales and optimize capacities to improve productivity and customer satisfaction.
The research area includes micro simulations, agent based modelling, econometric models relying on big data and the specification and estimation of dynamic stochastic general equilibrium (DSGE) models. The potential research topic can focus on business cycle dynamics and forecasting the structural changes in the economy with implications for the corporate sector in general and for strategic decisions in particular. Data are essential but performance improvements and competitive advantages arise from analytic models that allow managers to predict and optimize outcomes. More importantly, the most effective approach to building a model usually does not start with data, but with identifying a business opportunity and determining how the model can improve corporate performance. Corporate managers can take advantage of forecasting sectoral and spatial development for investment and strategic decisions.
The objective of the research topic is to identify the potential added value of big data and model-based decision making for SMEs; the barriers to the spread of big data and modelling methodologies; the development of big data and model-based methods to support corporate decision making with a particular emphasis on SMEs.
Required language skills: English Recommended language skills (in Hungarian): English
Deadline for application: 2023-05-31
2024. IV. 17. ODT ülés Az ODT következő ülésére 2024. június 14-én, pénteken 10.00 órakor kerül sor a Semmelweis Egyetem Szenátusi termében (Bp. Üllői út 26. I. emelet).
Login
