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Econometrics basics
A brief introduction to elementary terms in econometrics
| Term | Definition |
|---|---|
| Dependent variable (Avhengig variabel) | The variable that you measure or observe to see how it changes in response to changes in the independent variable(s). |
| Independent variable (Uavhengig variabel) | The variable(s) we manipulate or change to observe it's effect on another variable (the dependent variable). |
| Regression Analysis | A method to examine the relationship between a dependent variable and one or more independent variables. Helps us understand how dependent variable changes when independent variable(s) varies. |
| Error term (u) | Represents factors other than independent variables that might affect the dependent variable (u = unobserved, or unobserved factors) |
| Multicollinearity | Situation where two or more independent variables are highly linearly related to each other. When present it can it difficult to determine the individual effect of the independent variable on dependent variable -> Causing large standard errors |
| Heteroskedasticity | Variance of the errors varies across levels of the independent variables leading to inefficient estimators (but not biased) - OLS is not BLUE |
| Homoskedasticity | Variance of the errors are constant across all levels of the independent variables, leading to efficient and reliable estimators -> Also required for BLUE |
| Time-Series data | Time-series data set consists of observations on a variable - or several - over different time periods. |
| Ordinary Least Squares (OLS) | Method for estimating coefficients (Basic answer, learn more in-depth later) |
| R-squared statistic (R^2) | Coefficient of determination, ranges from 0-1. Value of 1 = model perfectly explains the variance in the dependent variable, value of 0 = model explains nothing. |
| P-value | |
| T-value | A statistical measure used for hypothesis testing, and to determine the significance of a coefficient in a regression model. The higher the t-value, the higher the probabilities that the coefficient or difference is statistically significant. |
| Endogeneity | If independent variable Xj correlates with the error term u (for any reason at all), the variable Xj is said to be endogenous. Thus leading to biasedness and inconsistent estimates of the regression coefficients. |
| Exogeneity | Opposite of endogeneity, i.e. no correlation between independent variable Xj and error term u. Leading to consistent and unbiased estimates of the coefficients. (Change in variable is determined outside of model, and not by other indep. variables included |
| Dummy variable | Variable that |
| Multiple regression | |
| Instrumental variable | |
| Biased and unbiased estimator | |
| Residual | |
| Intercept | |
| Unbiased OLS estimators - Assumptions | 1. Linearity in parameters 2. Random sampling 3. No perfect collinearity 4. Zero conditional mean |
| Coefficient |
Created by:
Victorpetrov
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