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73 Cards in this Set
 Front
 Back
nonexperimental data/observational data/retrospective data

data not accumulated through controlled experiment


experimental data

data collected in laboratory environments


empirical analysis

the use of data to test a theory or estimate a relationship


economic model

a model consisting of mathematical equations that describe various relationships


econometric model

a mathematic model founded on the theories of the economic model that solves its ambiguities


crosssectional data set

a sample of individuals, households, firms, cities, states, countries, or a variety of other units, taken at a given point in time


random sampling

a tool used to simplify the analysis of crosssectional data; a sampling scheme


time series data

observations on a variable or several variables over time. Difficult to study because economic observations can rarely be assumed to be independent across time.


data frequency

a feature of time series data that usually exists on a daily, weekly, monthly, quarterly, or annual schedule


pooled cross section

data sets with both crosssectional and time series features, in which sample size is increased by combining data gathered in multiple places in time


panel data/longitudinal data

a data set consisting of a time series for each crosssectional member in the data set. These are more difficult to obtain than pooled cross sections.


ceteris paribus

other relevant factors being equal; a factor in determining causal relationships by accounting for other variables


simple linear regression model/twovariable linear regression model/bivariate linear regression model

a method of measuring how two variables relate to one another


other names for "y"

dependent variable, explained variable, response variable, predicted variable, regressand


other names for "x"

independent variable, explanatory variable, control variable, predictor variable, regressor


variable "u"

error term; disturbance
factors other than x and y. Treated by simple regression analysis as unobserved. When assumption E(u given x)=E(u) holds, u is mean independent of x. 

Beta sub 1

the slope parameter for the linear relationship that exists when the change in u is 0. Central in applied economics.


Bet sub 0

intercept parameter, aka the constant term. Rarely central to analysis.


zero conditional mean assumption

E(u given x)=0.
This is the result of the assumption that E(u)=0 and that E(u given x)=E(u). 

population regression function (PRF)

E(y given x)


Ordinary Least Squares (answer is a formula in the book)

page 29, estimates 2.17 and 2.19
a method for estimating the parameters of a multiple linear regression model. These estimates are obtained by minimizing the sum of squared residuals 

fitted value (answer is in book)

page 30, 2.20
The estimated values of the dependent variable when the values of the independent variables for each observation are plugged into the OLS regression line. 

residual

difference between an actual variable and its fitted value


first order conditions for the OLS estimates (answer in book)

page 29, 2.14 & 2.15
The set of linear equations used to solve for the OLS estimates 

OLS regression line (answer in book)

page 32, 2.23
The equation relating the predicted value of the dependent variable to the independent variables, where the parameter estimates have been obtained by OLS 

sample regression function (SRF)

page 32, 2.23
The equation relating the predicted value of the dependent variable to the independent variables, where the parameter estimates have been obtained by OLS 

total sum of squares (SST)

the total sample variation in a dependent variable about its sample average


explained sum of squares (SSE)

the total sample variation of the fitted values in a multiple regression model


residual sum of squares (SSR)/sum of squared residual

in multiple regression analysis, the sum of the squared OLS residuals across all observations


rsquared/coefficient of determination

r^2=SSE/SST=1SSR/SST
in a multiple regression model, the proportion of the total sample variation in the dependent variable that is explained by the independent variable 

elasticity

the percentage change in one variable given a 1% ceteris paribus increase in another variable


heteroskedasticity

page 53, slr.5
The error u has the same variance given any value of the explanatory variable. The variance of the error term, given the explanatory variables, is not constant 

error variance/disturbance variance

sigma squared
the variance of the error term in a multiple regression model 

degrees of freedom

in multiple regression analysis, the number of observations minus the number of estimated parameters


standard error of the regression (SER)

the natural estimator of sigma; page 58 2.62
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. 

partial effect

the effect of an explanatory variable on the dependent variable, holding other factors in the regression model fixed


perfect collinearity

in multiple regression, one independent variable is an exact linear function of one or more other independent variables


exogenous explanatory variable

a variable that is uncorrelated with the error term in the model of interest and used to explain variation in the dependent variable


endogenous explanatory variable

an expalnatory variable in a multiple regression model that is correlated with the error term, either because of an omitted variable, measurement error, or simultaneity


inclusion of an irrelevant variable/overspecifying the model

the including of an explanatory variable in a regression model that has a zero population parameter in estimating an equation by OLS


excluding a relevant variable/underspecifying the model

in multiple regression analysis, leaving out a variable that has a nonzero partial effect on the dependent variable


misspecification analysis

the process of determining likely biases that can arise from omitted variables, measurement error, simultaneity, and other kinds of model misspecification


omitted variable bias

the bias that arises in the OLS estimators when a relevant variable is omitted from the regreession


upward bias

the expected value of an estimator is greater than the population parameter value


downward bias

the expected value of an estimator is below the population value of the parameter


biased toward zero

a description of an estimator whose expectation in absolute value is less than the absolute value of the population parameter


GaussMarkov assumptions

the set of assumptions under which OLS is BLUE


standard deviation of beta hat

pg. 102 3.58
the square root of the variance 

GaussMarkov Theorem

under the five GaussMarkov assumptions the OLS estimator is BLUE


normality assumption

the classical linear model assupmtion that states that the error has a normal distribution, conditional on the explanatory variables


minimum variance unbiased estimators

an estimator with the smallest variance in the class of all unbiased estimators, no JOKE Sherlock


null hypothesis

in classical hypothesis testing, we take this hypothesis as true and require the data to provide substantial evidence against it


t statistic/t ratio

the statistic used to test a single hypothesis about the parameters in an econometric model


alternative hypothesis

the hypothesis against which the alternative hypothesis is tested


onesided hypothesis

an alternative hypothesis that states that the parameter is greater than or less than the value hypothesized under the nul


significance level

the probability of Type I error in hypothesis testing


critical value

in hypothesis testing, the value against which a test statistic is compared to determine whether or not the null hypothesis is rejected


onetailed test

a hypothesis test against a onesided alternative


statistically significant

rejecting the null hypothesis that a parameter is equal to zero against the specified alternative, at the chosen significance level


economic significance/practical significance

the practice or economic importance of an estimate, which is measured by its sign and magnitude, as opposed to its statistical significance


exclusion restrictions

restrictions that state that certain variables are excluded from the model or have zero populaation coefficients


multiple restrictions

more than one restriction on the parameters in an econometric model


multiple hypotheses test/ joint hypotheses test

test of a null hypothesis involving more than one restriction on the parameters


restricted model

in hypothesis testing, the model obtained after imposing all of the restrictions required under the null


F statistic

a statistic used to test multiple hypotheses about the parameters in a multiple regression model


numerator degrees of freedom

in an F test, the number of restrictions being tested


denominator degrees of freedom

in an F test, the degrees of freedom in the unrestricted model


jointly statistically significant

the null hypothesis that two or more explanatory variables have zero population coefficients is rejected at the chosen significance level


Rsquared form of the F statistic

the F statistic for testing exclusion restrictions expressed in terms of the Rsquareds from the restricted and unrestricted models


overall significance of the regression

a test of the joint significance of all explanatory variables appearing in a multiple regression equation


asymptopic properties/large sample properties

properties of estimators and test statistics that apply when the sample size grows without bound


consistency

an estimator converges in probability to the correct population value as the sample size grows


asymptopic bias

bias in an estimator that is always toward zero; thus, the expected value of an estimator with attenuation bias is less in magnitude than the absolute value of the parameter
