Total Vehicle Sales Forecast
ECO 309 Economic Forecasting Final Project
Business & Finance, Economics, Statistics
| Author: | Alexander Hardt | ISBN: | 9783656735625 |
| Publisher: | GRIN Verlag | Publication: | September 5, 2014 |
| Imprint: | GRIN Verlag | Language: | English |
| Author: | Alexander Hardt |
| ISBN: | 9783656735625 |
| Publisher: | GRIN Verlag |
| Publication: | September 5, 2014 |
| Imprint: | GRIN Verlag |
| Language: | English |
Project Report from the year 2013 in the subject Economics - Statistics and Methods, grade: 1,0, , course: ECO 309, language: English, abstract: For this project I created a twelve month forecast for Total Vehicle Sales in the United States using four different methods. These four techniques are called exponential smoothing, decomposition, ARIMA, and multiple regression. To do so I picked one dependent (Y) variable along with two independent (X) variables and collected 80 monthly observations for each variable. This historical data allowed me to create four different forecasting models which predict future Vehicle Sales with low risk of error. The best model according to the lowest error measures was winter's exponential smoothing method because it had the lowest MAPE along with the lowest RMSE for the fit period as well as the forecast period.
Project Report from the year 2013 in the subject Economics - Statistics and Methods, grade: 1,0, , course: ECO 309, language: English, abstract: For this project I created a twelve month forecast for Total Vehicle Sales in the United States using four different methods. These four techniques are called exponential smoothing, decomposition, ARIMA, and multiple regression. To do so I picked one dependent (Y) variable along with two independent (X) variables and collected 80 monthly observations for each variable. This historical data allowed me to create four different forecasting models which predict future Vehicle Sales with low risk of error. The best model according to the lowest error measures was winter's exponential smoothing method because it had the lowest MAPE along with the lowest RMSE for the fit period as well as the forecast period.
