Econometrics Terms in simple words.

ekonometrics.com,
econometrics terms

Dependent Variable:

  • Definition: The outcome you’re trying to predict or explain in econometrics.
  • Example: In a study to predict students’ test scores, the dependent variable is the test score.
  • Equation: Y (where Y represents the dependent variable)

Independent Variable:

  • Definition: Factors that you believe have an impact on the dependent variable in econometrics.
  • Example: In the same study, independent variables might be the number of hours studied, the student’s age, and whether they had a tutor.
  • Equation: 𝑋1,𝑋2,𝑋3,…,π‘‹π‘˜β€‹ (where 𝑋1,𝑋2,𝑋3,…,π‘‹π‘˜β€‹ represent the independent variables)

Regression Analysis:

  • Definition: A method used in econometrics to understand the relationship between the dependent variable and one or more independent variables.
  • Example: Using data on hours studied and test scores to find out how much an additional hour of study impacts the test score.
  • Equation: π‘Œ=𝛽0+𝛽1𝑋1+𝛽2𝑋2+…+π›½π‘˜π‘‹π‘˜+πœ–
    • Y = dependent variable (test score)
    • Ξ²0​ = intercept
    • 𝛽1,𝛽2,…,π›½π‘˜ = coefficients of the independent variables
    • 𝑋1,𝑋2,…,π‘‹π‘˜β€‹ = independent variables (e.g., hours studied, age)
    • Ο΅ = error term

Coefficient:

  • Definition: A number that represents the strength and direction of the relationship between an independent variable and the dependent variable in econometrics.
  • Example: If the coefficient for hours studied is 2, then for every additional hour studied, the test score increases by 2 points.
  • Equation: Ξ²i​ (where i denotes the specific independent variable)

Intercept:

  • Definition: The expected value of the dependent variable in econometrics when all independent variables are zero.
  • Example: If the intercept in our test score study is 50, then a student who didn’t study at all is expected to score 50.
  • Equation: Ξ²0​

R-Squared (RΒ²):

  • Definition: A measure in econometrics of how well the independent variables explain the variation in the dependent variable. Values range from 0 to 1.
  • Example: An RΒ² of 0.8 in our study suggests that 80% of the variation in test scores can be explained by the number of hours studied and other variables included.
  • Equation: 𝑅2=1βˆ’π‘†π‘†π‘Ÿπ‘’π‘ π‘†π‘†π‘‘π‘œπ‘‘β€‹β€‹
    • π‘†π‘†π‘Ÿπ‘’π‘ β€‹ = sum of squares of residuals
    • π‘†π‘†π‘‘π‘œπ‘‘ = total sum of squares

P-Value:

  • Definition: The probability in econometrics that the observed results happened by chance. A lower p-value indicates stronger evidence against the null hypothesis.
  • Example: In our study, a p-value of 0.01 for hours studied means there’s a 1% chance the relationship observed is due to random variation, suggesting a significant relationship.

Standard Error:

  • Definition: A measure in econometrics of the accuracy of the coefficient estimates.
  • Example: If the standard error for the hours studied coefficient is 0.5, it means there’s some uncertainty in our estimate of the impact of hours studied on test scores.
  • Equation: 𝑆𝐸(𝛽^)=𝜎2π‘›β‹…π‘£π‘Žπ‘Ÿ(𝑋)
    • 𝜎2 = variance of the error term
    • 𝑛 = number of observations
    • π‘£π‘Žπ‘Ÿ(𝑋)= variance of the independent variable

Heteroskedasticity:

  • Definition: When the variability of the dependent variable in econometrics differs across levels of an independent variable.
  • Example: If students with different levels of prior knowledge show more varied test scores, our model might exhibit heteroskedasticity.
  • Equation: No specific equation, but can be detected with tests like Breusch-Pagan or White test.

Multicollinearity:

  • Definition: When independent variables in econometrics are highly correlated with each other, making it difficult to assess their individual effects.
  • Example: In our test score study, if hours studied and having a tutor are highly correlated (because students who have a tutor also study more), it might be hard to determine each one’s individual impact on test scores.

Autocorrelation:

  • Definition: When residuals (errors) in a regression model in econometrics are correlated across observations, usually in time series data.
  • Example: If we were studying monthly sales data and found that the sales this month are similar to the sales last month, we might have autocorrelation.

Dummy Variable:

  • Definition: A binary variable (0 or 1) used in econometrics to include categorical data in regression models.
  • Example: In our study, having a tutor can be represented as a dummy variable, where 1 indicates the student has a tutor and 0 indicates they do not.

Endogeneity:

  • Definition: When an independent variable in econometrics is correlated with the error term, possibly due to omitted variable bias, measurement error, or simultaneity.
  • Example: If students who are naturally smarter study more (and we don’t control for innate intelligence), our hours studied variable might be endogenous.

Instrumental Variables (IV):

  • Definition: Variables used in econometrics to correct endogeneity by serving as proxies for the problematic independent variables.
  • Example: If we suspect that hours studied are endogenous, we might use access to a quiet study place as an instrumental variable if it affects study hours but not test scores directly.
  • Equation: Two-stage least squares (2SLS) method:
    1. First stage: 𝑋=πœ‹0+πœ‹1𝑍+𝜈 (where 𝑍 is the instrument)
    2. Second stage: π‘Œ=𝛽0+𝛽1𝑋^+πœ–

Time Series Data:

  • Definition: Data collected over time on a single entity or several entities in econometrics.
  • Example: Monthly unemployment rates over 10 years.
  • Equation: π‘Œπ‘‘=𝛽0+𝛽1𝑋1,𝑑+…+π›½π‘˜π‘‹π‘˜,𝑑+πœ–π‘‘β€‹ (where 𝑑 represents time periods)

Panel Data:

  • Definition: Data collected on multiple entities over multiple time periods in econometrics.
  • Example: Test scores of students from several schools over several years.
  • Equation: π‘Œπ‘–π‘‘=𝛽0+𝛽1𝑋1,𝑖𝑑+…+π›½π‘˜π‘‹π‘˜,𝑖𝑑+πœ–π‘–π‘‘ (where 𝑖 represents entities and 𝑑 represents time periods)

Fixed Effects Model:

  • Definition: A panel data model in econometrics that controls for variables that do not change over time within an entity.
  • Example: Controlling for individual student characteristics (like innate ability) that don’t change over the study period when analyzing the impact of study hours on test scores.
  • Equation: π‘Œπ‘–π‘‘=𝛼𝑖+𝛽𝑋𝑖𝑑+πœ–π‘–π‘‘ (where Ξ±i​ represents the entity-specific effect)

Random Effects Model:

  • Definition: A panel data model in econometrics that assumes individual entity characteristics are random and uncorrelated with the independent variables.
  • Example: Using random effects to analyze how school-level policies impact student test scores, assuming differences between schools are random.
  • Equation: π‘Œπ‘–π‘‘=𝛽0+𝛽1𝑋𝑖𝑑+𝑒𝑖+πœ–π‘–π‘‘β€‹ (where ui​ is the random effect).

If you want to learn more econometrics terms, then here you can find more econometrics concepts.

And if you plan to begin studying econometrics, here is a complete roadmap from basic to advanced levels.

FAQ:

Q. What are the key terms in econometrics?

Key terms in econometrics include Regression Analysis, Endogeneity, Exogeneity, Instrumental Variables (IV), Heteroscedasticity, Autocorrelation, Multicollinearity, Panel Data, Time Series, Cross-Sectional Data, OLS (Ordinary Least Squares), R-squared, p-value, Dummy Variable, and Fixed Effects.

Blog

Leave a Reply

Your email address will not be published. Required fields are marked *