Total Vehicle Sales Forecast
Total Vehicle Sales Forecast
Final Project
Alexander Hardt
Dr. Holmes
Economic Forecasting 309-01W
Summer II
8/6/2013
Executive Summary 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.
Introduction I chose the Y variable to be Total Vehicle Sales in the United States because I have a strong interest in the auto industry and would like to work for a German car maker in the future. The auto industry is very vulnerable to the state of the economy because people tend to postpone high-item purchases like a car when times are tough. Therefore, the variables that cause a change in vehicle sales numbers must be indicators of economic performance. In order to forecast the dependent variable Y (Total Vehicle Sales), I chose two independent variables, X1 and X2 that are closely related to Y. These are going to be Employment non-farm and the Personal Saving Rate. The hypothesis I make for the first X variable is that employment numbers are logically related to vehicle sales because the more people are in the workforce, the more people earn an income which is necessary to make high-item purchases like a personal car. The hypothesis for the second X variable is that the personal saving rate has an inverse linear relationship to vehicle sales because the more people hold on to their
Final Project
Alexander Hardt
Dr. Holmes
Economic Forecasting 309-01W
Summer II
8/6/2013
Executive Summary 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.
Introduction I chose the Y variable to be Total Vehicle Sales in the United States because I have a strong interest in the auto industry and would like to work for a German car maker in the future. The auto industry is very vulnerable to the state of the economy because people tend to postpone high-item purchases like a car when times are tough. Therefore, the variables that cause a change in vehicle sales numbers must be indicators of economic performance. In order to forecast the dependent variable Y (Total Vehicle Sales), I chose two independent variables, X1 and X2 that are closely related to Y. These are going to be Employment non-farm and the Personal Saving Rate. The hypothesis I make for the first X variable is that employment numbers are logically related to vehicle sales because the more people are in the workforce, the more people earn an income which is necessary to make high-item purchases like a personal car. The hypothesis for the second X variable is that the personal saving rate has an inverse linear relationship to vehicle sales because the more people hold on to their