Glossary

Econometrics terms

Short definitions for econometrics vocabulary used across all Ceteris Lab modules, from math and Python foundations through robust regression inference.

Module 0Module 1Module 2Module 3Module 4Module 5Module 6Module 7Module 8

143 curated terms loaded from the live glossary and all-module fallback.

Absolute value
The distance of a number from zero, written without regard to sign.
Module 0
Adjusted R-squared
A goodness-of-fit measure that adjusts R-squared for the number of regressors in the model.
Module 3Module 6
Alternative hypothesis
The claim considered when the null hypothesis is not supported by the evidence.
Module 4
Argument
A reasoned claim supported by assumptions, logic, or evidence.
Module 0
Asymptotic bias
The limiting gap between an estimator and the true population value.
Module 5
Asymptotic efficiency
A large-sample comparison of estimator precision.
Module 5
Asymptotic normality
The property that a properly scaled estimator is approximately normal in large samples.
Module 5
Asymptotic standard error
A standard error based on large-sample approximation.
Module 5
Average
A typical value of a variable, often represented by the sample mean.
Module 0
Bad control
A control variable that can distort the research question or absorb part of the effect of interest.
Module 6
Base group
The omitted category used as the reference point for interpreting categorical indicators.
Module 7
Binary dependent variable
An outcome that takes only two values, often coded zero and one.
Module 7
Binary variable
A variable coded as 1 when a condition is true and 0 otherwise.
Module 7
BLUE
Best Linear Unbiased Estimator under the Gauss-Markov assumptions.
Module 3
Breusch-Pagan test
A diagnostic test that relates squared residuals to variables that may explain error variance.
Module 8
Causal claim
A statement that changing one factor would change an outcome.
Module 1
Causality
A cause-and-effect relationship where changing one factor changes another.
Module 1
Centering
Subtracting a reference value, often the mean, to make interactions or intercepts easier to read.
Module 6Module 7
Central Limit Theorem
A result explaining why many averages and estimators are approximately normal in large samples.
Module 5
Ceteris paribus
All else equal; comparing one factor while holding relevant factors fixed.
Module 1Module 3
Chow test
A joint test for whether regression coefficients differ across groups.
Module 7
Coefficient
An estimated number multiplying a variable in a regression equation.
Module 2Module 3
Complement
The event that a stated event does not happen.
Module 0
Confidence interval
A range of plausible coefficient values built from an estimate and its standard error.
Module 4
Confounder
A third factor related to both the explanatory variable and the outcome.
Module 1Module 3
Consistency
The property that an estimator gets closer to the true parameter as sample size grows.
Module 5
Control group
The group used as the comparison benchmark in a treatment or policy setting.
Module 7
Control variable
A variable added to account for another relevant factor in the comparison.
Module 3
Conventional standard error
A standard error computed under the usual homoskedasticity formula.
Module 8
Correlation
A standardized measure of linear association between two variables.
Module 0Module 1
Covariance
A measure of whether two variables tend to move above or below their means together.
Module 0
Critical value
A cutoff from a reference distribution used in a hypothesis test.
Module 4
Cross-sectional data
Data on many units observed at one point in time.
Module 1
DataFrame
A rectangular Python data object with named columns and rows.
Module 0
Degrees of freedom
The amount of independent information left after estimating model parameters.
Module 4
Dependent variable
The outcome variable the model is trying to explain or predict.
Module 1Module 2
Descriptive relationship
An observed sample pattern that does not by itself establish cause and effect.
Module 1
Distribution
A description of possible values of a variable and how frequently or likely they occur.
Module 0
Dummy variable
A zero-one variable representing a category, condition, or group.
Module 7
Dummy variable trap
Perfect multicollinearity caused by including every category indicator along with an intercept.
Module 7
Econometrics
The use of data and statistical methods to study economic relationships.
Module 1
Elasticity
The percent change in an outcome associated with a one percent change in an explanatory variable.
Module 6
Endogeneity
A problem where an explanatory variable is related to the error term.
Module 5
Equation
A statement that two mathematical expressions are equal.
Module 0
Error term
The part of the outcome not explained by the variables included in the model.
Module 1Module 2
Exact percent change
A percent interpretation for log-dependent models that uses exponentiation rather than a rough approximation.
Module 6Module 7
Exogeneity
A condition that the error has no systematic relationship with the explanatory variables.
Module 2Module 3Module 5
Expected value
The long-run average value of a random variable.
Module 0Module 5
Explanatory variable
A variable used to explain or predict the dependent variable.
Module 1Module 2
F statistic
A test statistic used for joint hypotheses or overall regression significance.
Module 4
Feasible GLS
A weighted estimator that first estimates the variance pattern and then uses predicted weights.
Module 8
Finite-sample property
A property describing an estimator's behavior for a specific sample size.
Module 5
Fitted line
The line chosen by regression to summarize the sample relationship.
Module 2
Fitted value
The predicted value from an estimated regression equation.
Module 2Module 3
Function
A rule that maps one or more inputs to an output.
Module 0Module 6
Functional form
The mathematical shape used to connect the dependent variable to regressors.
Module 6
Gauss-Markov assumptions
Conditions under which OLS is the best linear unbiased estimator.
Module 3
Group mean difference
The difference in average outcomes between two groups.
Module 7
HC standard error
A family of heteroskedasticity-consistent standard errors such as HC0, HC1, HC2, and HC3.
Module 8
Heteroskedasticity
A condition where the error variance changes across observations, groups, or regressor values.
Module 2Module 8
Heteroskedasticity-robust inference
Tests and intervals that use standard errors valid under changing error variance in large samples.
Module 8
Holding other factors fixed
Interpreting one coefficient while keeping the other regressors unchanged.
Module 3
Homoskedasticity
A condition where the error variance is constant across observations or regressor values.
Module 2Module 8
Index
A counter or label that identifies an observation, variable, group, or time period.
Module 0
Interaction term
A variable created by multiplying variables to let one effect depend on another variable.
Module 6Module 7
Intercept
The predicted value of the dependent variable when explanatory variables equal zero.
Module 2
Intercept shift
A change in the intercept associated with a binary or categorical variable.
Module 7
Joint effect
The combined predicted change from changing more than one explanatory variable.
Module 3Module 4
Joint significance
Evidence that a group of coefficients matters collectively.
Module 4
Known-form heteroskedasticity
A case where the error variance pattern is known or defensibly specified.
Module 8
Lagrange Multiplier test
A test that uses a restricted model to check whether excluded information matters.
Module 5
Large-sample inference
Inference based on approximations that improve as sample size grows.
Module 5
Law of large numbers
A result showing that sample averages converge to population averages under suitable conditions.
Module 5
Level-log model
A model where the dependent variable is in levels and an explanatory variable is logged.
Module 6
Linear probability model
An OLS model used when the dependent variable is binary.
Module 7Module 8
Linear restriction
A hypothesis that places a linear condition on one or more coefficients.
Module 4
Log-level model
A model where the dependent variable is logged and an explanatory variable is in levels.
Module 6
Log-log model
A model where both the dependent variable and an explanatory variable are logged.
Module 6
Mean
The arithmetic average of a set of values.
Module 0
Median
The middle value after observations are sorted.
Module 0
Multicollinearity
A strong linear relationship among explanatory variables that can reduce estimation precision.
Module 3
Multiple regression
A regression model with one dependent variable and two or more explanatory variables.
Module 3
Natural logarithm
A transformation often used to interpret changes in approximate percentage terms.
Module 6
Nonnested models
Models where one specification is not a simple restricted version of the other.
Module 6
Normal distribution
A symmetric bell-shaped distribution described by a mean and standard deviation.
Module 0Module 4Module 5
Null hypothesis
The benchmark claim tested against the data, often that a coefficient equals zero.
Module 4
Observation
One row, case, person, firm, region, or time period in a dataset.
Module 0Module 1
OLS
Ordinary Least Squares, the method that chooses coefficients to minimize squared residuals.
Module 2Module 3
Omitted base category
The category left out so included dummy coefficients are interpreted relative to it.
Module 7
Omitted variable bias
Bias from leaving out a relevant variable that is correlated with an included regressor.
Module 3Module 5
Over-control
Adding controls that block or distort the relationship the analysis is trying to measure.
Module 6
p-value
A measure of how unusual the sample evidence would be if the null hypothesis were true.
Module 4
Panel data
Data that follow the same units across multiple time periods.
Module 1
Panel unit
The person, firm, region, or country followed over time in panel data.
Module 1
Parameter
An unknown population value that a model tries to estimate.
Module 1Module 2
Partial effect
The relationship between one explanatory variable and the outcome, holding other included variables fixed.
Module 3
Partialling out
A way to isolate the part of a variable not explained by other controls.
Module 3
Policy indicator
A binary variable that marks exposure to a policy or program.
Module 7
Pooled cross section
Separate cross-sectional samples combined across multiple time periods.
Module 1
Population
The full group of units or outcomes the research question is about.
Module 0Module 1
Practical significance
Whether an estimated effect is economically meaningful in size, not only statistically detectable.
Module 4
Prediction interval
A range that accounts for uncertainty when predicting an individual outcome.
Module 6
PRF
Population regression function, the average relationship in the full population.
Module 2
Probability
A number from 0 to 1 that describes how likely an event is.
Module 0Module 7
Probability limit
The value an estimator converges toward as sample size grows.
Module 5
Python package
A reusable collection of Python tools for data work, statistics, or visualization.
Module 0
Quadratic term
A squared variable used to allow a curved relationship.
Module 6
R-squared
The share of total variation in the dependent variable explained by the regression.
Module 2Module 3Module 6
Random variable
A variable whose value is uncertain before it is observed.
Module 0Module 5
Regression
A method for estimating relationships between an outcome and one or more explanatory variables.
Module 1Module 2
Regression equation
A compact model that links an outcome to explanatory variables, coefficients, and an error term.
Module 2
Repeated survey
A survey collected in multiple periods, often with new respondents each time.
Module 1
Residual
The observed value minus the fitted value.
Module 2Module 3
Residual sum of squares
The sum of squared residuals left unexplained by the model.
Module 2
Robust F test
A joint test that uses a heteroskedasticity-robust covariance matrix.
Module 8
Robust standard error
A standard error designed to remain valid under heteroskedasticity in large samples.
Module 8
Robust t test
A t test computed with a heteroskedasticity-robust standard error.
Module 8
Sample
The observations actually used in the analysis.
Module 0Module 1
Sample size
The number of observations in the sample, often written as n.
Module 0Module 5
Sampling distribution
The distribution an estimator would have across repeated samples.
Module 4Module 5
Self-selection
A problem where people or units choose into treatment or groups in ways related to outcomes.
Module 7
Simple regression
A regression model with one dependent variable and one explanatory variable.
Module 2
Skewness
A measure of asymmetry in a distribution.
Module 5
Slope
The predicted change in the dependent variable for a one-unit increase in an explanatory variable.
Module 2
Slope shift
A change in a slope created by an interaction term.
Module 7
Special White test
A version of the White test that uses fitted values and squared fitted values in the auxiliary regression.
Module 8
SRF
Sample regression function, the fitted relationship estimated from sample data.
Module 2
Standard deviation
The square root of variance, measured in the original units of the variable.
Module 0
Standard error
A measure of sampling uncertainty around an estimated coefficient.
Module 2Module 4Module 8
Standardized coefficient
A coefficient measured in standard-deviation units for comparison across variables.
Module 6
Statistical significance
Evidence strong enough to reject a null hypothesis at a chosen significance level.
Module 4
Summation
A compact instruction to add values across observations.
Module 0
t statistic
A coefficient estimate divided by its standard error.
Module 4
Time-series data
Data on one unit or variable observed across multiple time periods.
Module 1
Treatment group
The group exposed to a policy, intervention, or condition of interest.
Module 7
Turning point
The value where a quadratic relationship changes direction.
Module 6
Two-sided test
A test that checks for evidence against the null in either direction.
Module 4
Variable
A measured item that can change across people, places, firms, or time.
Module 0Module 1
Variance
The average squared distance of values from their mean.
Module 0Module 8
Variance function
A model or description of how error variance changes with variables.
Module 8
Variance inflation factor
A diagnostic that summarizes how much multicollinearity inflates a coefficient's variance.
Module 3
Weighted least squares
A least-squares method that gives lower weight to observations with higher error variance.
Module 8
White test
A broad heteroskedasticity diagnostic using squares and interactions of regressors.
Module 8