The goal of the thesis is to accurately predict future values of a company’s website visits and to estimate the uncertainty of those predictions. To achieve this, a dynamic regression model with an ARIMA error term is considered, using advertisement spending with lags and dummy variables for Black Friday and weekdays as predictors. After dividing the data into a training set and a test set, the order of the ARIMA error term is specified using the Box-Jenkins methodology. The initial model is then run through a backward elimination algorithm, which selects two models based on the Akaike Information Criterion and Bayes Information Criterion. As expected, the model selected using Bayes Information Criterion is more conservative in its choice of variables than the model specified using the Akaike Information Criterion. The forecasts made on the test set are complemented with normal and bootstrap-based prediction intervals in order to estimate the uncertainty of the predictions. These are then compared to the forecasts made using a simpler model, consisting of only the ARIMA error term. The thesis concludes that the dynamic regression models are twice as accurate as the simpler model and that they were on average off by 14% from the actual values. The prediction intervals for the dynamic regression models are slightly too pessimistic as they overstate the uncertainty of the model by about 10 percentage points in the 80% prediction interval and by 5 percentage points in the 95% prediction interval. There is no practical discrepancy in prediction power between the model selected using the Akaike Information Criterion and the one using Bayes Information Criterion. The accuracy of the prediction intervals is higher than in the simpler model even though both dynamic regression models have more residual autocorrelation.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-446073 |
| Date | January 2021 |
| Creators | Björklund, Martin, Hasselblad, Felix |
| Publisher | Uppsala universitet, Statistiska institutionen, Uppsala universitet, Statistiska institutionen |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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