Econometrics Review Flashcards

These flashcards review key vocabulary terms and definitions related to econometrics as discussed in the class lectures.

Econometrics

The use of statistical methods to analyze economic data.

Econometricians

Researchers or analysts who apply econometric methods.

Nonexperimental data

Data collected without controlled structures typical in experimental setups.

Estimating relationships

Determining the association or link between economic variables.

Testing economic theories

Evaluating hypotheses to see if they hold true in the real world.

Evaluating policies

Assessing the impact of government or business decisions using econometric models.

Forecasting

Predicting future values based on historical data.

Macroeconomic variables

Economic indicators that affect the economy as a whole, such as GDP.

Cross-sectional data

Data collected at a single point in time across multiple subjects.

Time series data

Data collected over a period of time on the same subject.

Pooled cross sections

Combining multiple cross-sectional data sets from different time periods.

Panel data

Data observed over multiple time periods for the same subjects.

Error specification

Identifying the nature of errors in a model for accurate analysis.

Hypothesis testing

The process of determining the validity of a presumed relationship or theory.

Parameter β3

Represents the effect of a specific variable, like training on wages.

Ceteris paribus

The assumption that all other variables remain constant when assessing the relationship between two variables.

Regression model

A mathematical model that describes the relationship between a dependent variable and one or more independent variables.

Ordinary Least Squares (OLS)

A method for estimating the parameters in a regression model by minimizing the sum of the squares of the residuals.

Fitted values

Predicted values derived from a regression model.

Residuals

The difference between observed and predicted values.

Goodness of fit

A measure of how well the model's predictions match the actual data.

R-squared

A statistical measure representing the proportion of variance for the dependent variable that's explained by the independent variables.

Conditional mean independence

Assumption that the average of the dependent variable is independent of the explanatory variable.

Bias

The difference between an estimator's expected value and the true value of the parameter being estimated.

Variance

A measure of the dispersion of the estimated coefficients.

Homoskedasticity

The condition in which the variance of the errors is constant across all levels of the independent variable.

Multicollinearity

A situation where two or more independent variables are highly correlated, making it difficult to assess their individual contributions.

Logarithmic transformation

A mathematical operation to normalize data and stabilize variance.

Interaction effects

When the effect of one variable on the dependent variable changes depending on the level of another variable.

Dummy variables

Binary variables indicating the presence or absence of a characteristic.

Statistical significance

The likelihood that the relationship observed in the data is not due to chance.

P-value

The probability that the results observed in the sample occurred by random chance; used to test the null hypothesis.

Null hypothesis

The default position that there is no relationship or effect.

Alternative hypothesis

The position that there is an effect or relationship.

Confidence intervals

A range of values derived from a sample that is likely to contain the population parameter.

Linear probability model

A type of regression used for binary outcome variables.

Average treatment effect (ATE)

The average difference in outcomes for participants and non-participants in a treatment.

Treatment group

The group of subjects that receives the intervention or treatment.

Control group

The group of subjects that does not receive the intervention but is otherwise similar.

Regression adjustment

Controlling for factors that might influence the outcome when estimating treatment effects.

Chow test

A statistical test to determine whether the coefficients in two regressions on different data sets are equal.

Omitted variable bias

The bias in estimates when a relevant variable is left out of the model.

Sample mean

The average of the observations in the sample.

Population mean

The average of all possible observations in the entire population.

Standard error

The standard deviation of the sampling distribution of a statistic.

Bivariate regression

A regression analysis involving two variables.

Multivariate regression

A regression analysis involving multiple independent variables.