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Econ 420 CH. 2
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Terms in this set (26)
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
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 which affect the dependent variable. The error term may also include measurement errors in the observed dependent or independent variable
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
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 Value
The estimated values of the dependent variable when the values of the independent variables for each observation are plugged into the OLS regression line
Heteroskedasticity
The variance of the error term, given the explanatory variables, is not constant
Homodkedasticity
The errors in a regression model have constant variance conditional on the explanatory variables
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 Independence
The key requirement in multiple regression analysis, which 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)
The expected or average value of one random variable, called the dependent or explained variable, that depends on the values of one or more other variables, called the independent or explanatory variables.
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
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
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
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 β₁
An estimate of the standard deviation in the sampling distribution of β₁
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
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.
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