The objective of the current study is to develop a model that explains changes in the value of U.S. Gross Domestic Product (GDP) based on its constituents. The dataset for this study is prepared by extracting data from the website of the Bureau of Economic Analysis (BEA) (BEA, 2018). The variables included in this dataset include GDP, Exports, Imports, Investment, and Consumption Expenditure, which are continuous, ratio variables.

The data contains 88 entries from 1929 to 2016. All variables are measured in millions other than GDP, which is measured in billions. Therefore, the values of these variables are converted into billions. All missing values of 2017 are omitted from the dataset for all variables. The dependent variable is GDP, and other variables are independent.

The relationship between economic variables is determined by the multivariate regression model, which predicts changes in the value of the dependent variables affected by changes in the values of the independent variables (Epstein, 2011).

The multivariate regression model implemented in this study follows the given equation.

*GDP = ß*_{0 }*+ ß*_{1 }** Export + ß*_{2}* * Import + ß*_{3 }** Investment + ß*_{4 }** Consumption Expenditure*

The results of the regression model are provided in Table 1 given below.

*Table 1. Regression output.*

The model summary indicates that the results of the regression model carried out in this study are reliable and valid as the value of R-squared is 0.9992, which means that the model explains 99.92% of the total variations observed in 88 data entries. The ANOVA table also indicates that only 0.08% of the variations remain unexplained. Therefore, the output of the model is considered as significant as the value of Sig. F is less than the error term of 5%. The regression equation obtained from the analysis is given below.

*GDP = -53.35928256**– 0.096697899 * Export + 1.215720003 * Import + 2.166228052 * Investment + 2.961403928*** Consumption Expenditure*(II)

The coefficient of constant has a small negative value, which represents the proportion of variations not explained by the model. There is a negative relationship between GDP and Export as the coefficient value is negative, and it is not a significant relationship because the p-value is greater than 5%. There is a positive relationship between GDP and Import as the coefficient value is positive, and it is a significant relationship because the p-value is less than 5%. There is a positive relationship between GDP and Investment as the coefficient value is positive, and it is a significant relationship because the p-value is less than 5%. There is a positive relationship between GDP and Consumption as the coefficient value is positive, and it is a significant relationship because the p-value is less than 5%.

The findings of this study are relevant and important to understand the components of GDP. The fiscal policymakers use this data to determine the economic trend and formulate their policies to instigate further growth (Gosling & Eisner, 2015). The current study fails to indicate a positive relationship between GDP and Exports. However, all other findings are consistent with the existing literature on the relationship between GDP and its components.

## References

BEA. (2018). *U.S. economic accounts*. Web.

Epstein, R. J. (2011). An econometrics primer for lawyers. *Antitrust Magazine, 25*(3), pp. 29-33.

Gosling, J. J., & Eisner, M. A. (2015). *Economics, politics, and American public policy.* New York, NY: Routledge.