Econ 388 Test 1

Front 1

nonexperimental data/observational data/retrospective data

Back 1

data not accumulated through controlled experiment

Front 2

experimental data

Back 2

data collected in laboratory environments

Front 3

empirical analysis

Back 3

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

Front 4

economic model

Back 4

a model consisting of mathematical equations that describe various relationships

Front 5

econometric model

Back 5

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

Front 6

cross-sectional data set

Back 6

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

Front 7

random sampling

Back 7

a tool used to simplify the analysis of cross-sectional data; a sampling scheme

Front 8

time series data

Back 8

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

Front 9

data frequency

Back 9

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

Front 10

pooled cross section

Back 10

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

Front 11

panel data/longitudinal data

Back 11

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

Front 12

ceteris paribus

Back 12

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

Front 13

simple linear regression model/two-variable linear regression model/bivariate linear regression model

Back 13

a method of measuring how two variables relate to one another

Front 14

other names for "y"

Back 14

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

Front 15

other names for "x"

Back 15

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

Front 16

variable "u"

Back 16

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.

Front 17

Beta sub 1

Back 17

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

Front 18

Bet sub 0

Back 18

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

Front 19

zero conditional mean assumption

Back 19

E(u given x)=0.

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

Front 20

population regression function (PRF)

Back 20

E(y given x)

Front 21

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

Back 21

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

Front 22

fitted value (answer is in book)

Back 22

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.

Front 23

residual

Back 23

difference between an actual variable and its fitted value

Front 24

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

Back 24

page 29, 2.14 & 2.15

The set of linear equations used to solve for the OLS estimates

Front 25

OLS regression line (answer in book)

Back 25

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

Front 26

sample regression function (SRF)

Back 26

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

Front 27

total sum of squares (SST)

Back 27

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

Front 28

explained sum of squares (SSE)

Back 28

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

Front 29

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

Back 29

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

Front 30

r-squared/coefficient of determination

Back 30

r^2=SSE/SST=1-SSR/SST

in a multiple regression model, the proportion of the total sample variation in the dependent variable that is explained by the independent variable

Front 31

elasticity

Back 31

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

Front 32

heteroskedasticity

Back 32

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

Front 33

error variance/disturbance variance

Back 33

sigma squared

the variance of the error term in a multiple regression model

Front 34

degrees of freedom

Back 34

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

Front 35

standard error of the regression (SER)

Back 35

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.

Front 36

partial effect

Back 36

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

Front 37

perfect collinearity

Back 37

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

Front 38

exogenous explanatory variable

Back 38

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

Front 39

endogenous explanatory variable

Back 39

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

Front 40

inclusion of an irrelevant variable/overspecifying the model

Back 40

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

Front 41

excluding a relevant variable/underspecifying the model

Back 41

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

Front 42

misspecification analysis

Back 42

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

Front 43

omitted variable bias

Back 43

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

Front 44

upward bias

Back 44

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

Front 45

downward bias

Back 45

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

Front 46

biased toward zero

Back 46

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

Front 47

Gauss-Markov assumptions

Back 47

the set of assumptions under which OLS is BLUE

Front 48

standard deviation of beta hat

Back 48

pg. 102 3.58

the square root of the variance

Front 49

Gauss-Markov Theorem

Back 49

under the five Gauss-Markov assumptions the OLS estimator is BLUE

Front 50

normality assumption

Back 50

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

Front 51

minimum variance unbiased estimators

Back 51

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

Front 52

null hypothesis

Back 52

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

Front 53

t statistic/t ratio

Back 53

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

Front 54

alternative hypothesis

Back 54

the hypothesis against which the alternative hypothesis is tested

Front 55

one-sided hypothesis

Back 55

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

Front 56

significance level

Back 56

the probability of Type I error in hypothesis testing

Front 57

critical value

Back 57

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

Front 58

one-tailed test

Back 58

a hypothesis test against a one-sided alternative

Front 59

statistically significant

Back 59

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

Front 60

economic significance/practical significance

Back 60

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

Front 61

exclusion restrictions

Back 61

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

Front 62

multiple restrictions

Back 62

more than one restriction on the parameters in an econometric model

Front 63

multiple hypotheses test/ joint hypotheses test

Back 63

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

Front 64

restricted model

Back 64

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

Front 65

F statistic

Back 65

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

Front 66

numerator degrees of freedom

Back 66

in an F test, the number of restrictions being tested

Front 67

denominator degrees of freedom

Back 67

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

Front 68

jointly statistically significant

Back 68

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

Front 69

R-squared form of the F statistic

Back 69

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

Front 70

overall significance of the regression

Back 70

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

Front 71

asymptopic properties/large sample properties

Back 71

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

Front 72

consistency

Back 72

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

Front 73

asymptopic bias

Back 73

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