Specifying Your Econometrics Regression Model
In econometrics, the regression model is a common starting point of an analysis. As you define your regression model, you need to consider several elements:

Economic theory, intuition, and common sense should all motivate your regression model.

The most common regression estimation technique, ordinary least squares (OLS), obtains the best estimates of your model if the CLRM assumptions hold.

Assuming a normal distribution of the error term is important for hypothesis testing and prediction/forecasting.
When a regression model is estimated, applied econometricians and readers of the research assume that the researcher chose the correct independent variables, meaning they’re truly likely to cause changes in the dependent variable (the outcome of interest). The data and estimation of your model will ultimately reveal which independent variables are important factors and which ones are not. However, prior to obtaining results, you need to provide a sound justification for the variables you’ve chosen.
After you’ve specified the model and acquired your data, regression analysis allows you to estimate the economic relationships you’ve defined in the model. The estimation results provide a quantitative approximation of the relationship between the independent and dependent variables. OLS is the most common technique used for these calculations. Typically, you rely on specialized software to produce your estimates. However, by initially using manual calculations in situations with only one independent variable and relatively few observations, you can gain familiarity with the OLS technique and obtain a better understanding of the software’s algorithms and output.
No regression model is perfect. The error term contains the influence of any factors (variables) that affect your dependent variable and aren’t captured by your independent variable(s). The characteristics of the error term are of critical importance in econometrics. You need several assumptions about the error term to prove that the OLS results are precise. The assumption that the error term is normally distributed isn’t required for performing OLS estimation, but it is necessary when you want to produce confidence intervals and/or perform hypothesis tests with your OLS estimates.