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| Authors | Objective | Findings |
| 1. | | Forecasting in Marketing | Proposed vector autoregressive moving average models (VARMA) as a useful tool for forecasting in marketing. They also revealed that competition plays a significant role in marketing. The study favoured simulation methods of forecasting since there are advanced methods of data collection that require the adoption of econometric models to assist in decision making. |
| 2. | | Identify four developments that may significantly affect the future use of time-series techniques in marketing. | Availability of larger datasets with a large number of variables, high-frequency data from the Internet and fast-changing and turbulent markets were considered as future challenges of time series. VAR models may efficiently exploit the advantages of longer time-series and provide a important tool for understanding the evolving behavior of a market |
| 3. | | Dynamic Pricing Recognition on E-Commerce Platforms with VAR Processes | The authors also proposed Vector Auto Regressive processes and Lasso Penalization in online pricing. They proposed a model with the ability to detect real time price variations in individual vendors based on variations of their immediate competition. |