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Gravity
Terms in this set (40)
Coefficient of Determination
See R-squared.
Constant Elasticity Model
A model where the elasticity of the dependent variable, with respect to an explanatory variable, is constant; in multiple regression, both variables appear in logarithmic form.
Control Variable
See explanatory variable.
Degrees of Freedom
In multiple regression analysis, the number of observations minus the number of estimated parameters.
Dependent Variable
The variable to be explained in a multiple regression model (and a variety of other models).
Elasticity
The percentage change in one variable given a 1% ceteris paribus increase in another variable.
Error Term (Disturbance)
The variable in a simple or multiple regression equation that contains unobserved factors that affect the dependent variable. The error term may also include measurement errors in the observed dependent or independent variables.
Error Variance
The variance of the error term in a multiple regression model.
Explained Sum of Squares (SSE)
The total sample variation of the fitted values in a multiple regression model.
Explained Variable
See dependent variable.
Explanatory Variable
In regression analysis, a variable that is used to explain variation in the dependent variable.
First Order Conditions
The set of linear equations used to solve for the OLS estimates.
Fitted Values
The estimated values of the dependent variable when the values of the independent variables for each observation are plugged into the OLS regression line.
Gauss-Markov Assumptions
The set of assumptions (Assumptions MLR.1 through MLR.5 or TS.1 through TS.5) under which OLS is BLUE.
Heteroskedasticity
The variance of the error term, given the explanatory variables, is not constant.
Homoskedasticity
The errors in a regression model have constant variance conditional on the explanatory variables.
Independent Variable
See explanatory variable.
Intercept Parameter
The parameter in a multiple linear regression model that gives the expected value of the dependent variable when all the independent variables equal zero.
Mean Independent
The key requirement in multiple regression analysis that says the unobserved error has a mean that does not change across subsets of the population defined by different values of the explanatory variables.
OLS Regression Line
The equation relating the predicted value of the dependent variable to the independent variables, where the parameter estimates have been obtained by OLS.
Ordinary Least Squares (OLS)
A method for estimating the parameters of a multiple linear regression model. The ordinary least squares estimates are obtained by minimizing the sum of squared residuals.
Population Regression Function (PRF)
See conditional expectation.
Predicted Variable
See dependent variable.
Predictor Variable
See explanatory variable.
R-squared
In a multiple regression model, the proportion of the total sample variation in the dependent variable that is explained by the independent variable.
Regressand
See dependent variable.
Regression through the Origin
Regression analysis where the intercept is set to zero; the slopes are obtained by minimizing the sum of squared residuals, as usual.
Regressor
See explanatory variable.
Residual
The difference between the actual value and the fitted (or predicted) value; there is a residual for each observation in the sample used to obtain an OLS regression line.
Residual Sum of Squares (SSR)
See sum of squared residuals.
Response Variable
See dependent variable.
Sample Regression Function (SRF)
See OLS regression line.
Semi-elasticity
The percentage change in the dependent variable given a one-unit increase in an independent variable.
Simple Linear Regression Model
A model where the dependent variable is a linear function of a single independent variable, plus an error term.
Slope Parameter
The coefficient on an independent variable in a multiple regression model.
Standard Error of the Regression (SER)
In multiple regression analysis, the estimate of the standard deviation of the population error, obtained as the square root of the sum of squared residuals over the degrees of freedom.
Standard Error of Æ bj
An estimate of the standard deviation in the sampling distribution of Æ bj.
Sum of Squared Residuals (SSR)
In multiple regression analysis, the sum of the squared OLS residuals across all observations.
Total Sum of Squares (SST)
The total sample variation in a dependent variable about its sample average.
Zero Conditional Mean Assumption
A key assumption used in multiple regression analysis that states that, given any values of the explanatory variables, the expected value of the error equals zero. (See Assumptions MLR.4, TS.3, and TS.39.)
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