Total Vehicle Sales Forecast

ECO 309 Economic Forecasting Final Project

Business & Finance, Economics, Statistics
Cover of the book Total Vehicle Sales Forecast by Alexander Hardt, GRIN Verlag
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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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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