A master thesis from Aalborg University
Energikrisens Implikationer for en Dansk Plastvirksomhed: En analyse af priselasticitet og forecasting
[The Implications of the Energy Crisis for a Danish Plastic Company: An analysis of price elasticity of demand and forecasting]
Forfatter(e)
Semester
4. semester
Uddannelse
Udgivelsesår
2022
Afleveret
2022-06-02
Antal sider
114 pages
Abstract
This paper seeks to examine how the energy crisis impacts a Danish company in the plastic molding industry through a cooperation agreement. The problem area is approached using a variety of econometric and statistic modeling techniques to analyze the implications for the company’s revenue and construct forecasts hereof. Based on the fact that the company can change its product pool between every time step, the paper has utilized proxies for its sales amount and price due to the heterogeneous structure of the company. A short- and long-term price elasticity is estimated to study the implications of the energy crisis on the company’s revenue. The SVAR-model yields that a 1% increase in the firm’s sales price causes the sales of plastic products to fall by 0,15% and 0,17%, respectively. The analytical results turn out to be consistent with the hypothesis, which states the appearance of a relatively more elastic estimate in the long run. Based on the inelastic results, the energy crisis has a positive impact on the company’s revenue. By considering the combination of RMSE and graphical illustration, the VAR-model provides the best forecast of the company’s revenue. The forecast obtains a total average deviation of 9,7% from the observed values, which is 0,5% point lower than the simpler univariate Prophet-model with XGBoost Errors. Therefore, it can be discussed whether the company needs such complex models for forecasting. Overall, the neural network models perform the worst, which might be due to the simplistic nature of regression and the shorter length of the dataset.
Emneord
Forecasting ; Price Elasticity of Demand ; SVAR ; VAR ; Machine Learning ; Neural Networks ; LSTM ; SARIMA ; Prophet ; XGBoost ; Structural Equation Model ; Energy Crisis ; Russia ; Ukraine ; Invasion ; Impulse Response Analysis ; Estimation ; Implications ; Revenue ; Plastic Industry ; Company ; Firm ; Plastic
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