US Auto Production and Price Prediction in the Context of Multiple Regression Analysis

Abstract

After the eruption of COVID-19, the worldwide supply chain had been seriously affected and further impaired. Logistics and semiconductors production are not going smoothly during this period and the supply and demand supply balance had been weakened by this pandemic. There is a noticeable trend that the US auto production rate is declining year after year, whereas the car purchasing price as well as the gasoline price is rising each year. This paper investigates US auto prices based on multiple factors regression model. According to the analysis, there is a direct correlation between semiconductor production and the auto car production rate. In addition, US car production rate follows along with CPI (Consumer Price Index). By comparing this paper’s results with the movements of CPI and unemployment rate, the study will derive an approximate prediction upon the future auto car prices in the US. This methodology for car price prediction is not very precise, yet it can still reflect some general opinions or human being’s faith in the future car industry. The discussed model is the one to capture this attitude and translate it into figures and graphs which can give a direct assumption regarding to the people’s attitude. Otherwise, it can simply reflect possible future investing move one rational investor may take. These results shed light on guiding further exploration of stock price variation through multiple factors varies from raw material to people’s faith in the stock markets.