Midterm 1

Front 1

econometrics

Back 1

quantitative measurement and analysis of actual economic and business phenomena

Front 2

3 uses of econometrics

Back 2

describe economic reality
test hypotheses about economic theory
forecasting the future of economic activity

Front 3

hypothesis testing

Back 3

the evaluation of alternative theories with quantitative evidence

Front 4

dependent variable

Back 4

a function of movements in a set of other variables

Front 5

independent variable

Back 5

aka explanatory variable.

Front 6

beta zero

Back 6

constant/intercept term

Front 7

beta 1

Back 7

slope coefficient

Front 8

stochastic error term

Back 8

term that is added to a regression equation to introduce all of the variation in Y that cannot be explained by the included Xs

aka error term used to compensate for exogenous, unexplainable factors in a regression

Front 9

deterministic/stochastic component

Back 9

deterministic=expected value. indicated Y and X relationship

stochastic=

Front 10

estimated regression equation

Back 10

quantified version of the theoretical regression equation

Front 11

estimated regression coefficients

Back 11

beta hats

Front 12

residual

Back 12

difference between the observed Y and the estimated regression line

Front 13

error term

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difference between the observed Y and the true regression equation (expected value of Y)

Front 14

error term differences

Back 14

epsilon=true regression equation

Front 15

cross sectional data

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observations from same point in time and represent different individual economic entities from same point in time

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OLS

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regression technique that calculates beta hats so as to minimize the sum of the squared residuals

Front 17

partial regresion coefficent

Back 17

allow researcher to distinguish the impact of one variable on the dependent variable from that of other independent variables

Front 18

multivariate regression coefficient

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indicates change in the dependent variable associated with a one-unit increase in the independent variable, holding constant all other variables in the equation

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total sum of squares

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difference between observed Y value and the average Y value

Front 20

explained sum of squares

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difference between estimated Y value and the average Y value

Front 21

residual sum of squares

Back 21

difference between observed Y and the estimated Y value

Front 22

how to compute R squared

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ESS/TSS or 1-(RSS/TSS)

Front 23

R squared aka...

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coefficient of determination

Front 24

adjusted R squared

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measures % of variation of Y around its mean that is explained by the regression equation, adjusted for degrees of freedom.

Front 25

is it fair to use adjusted R squared as a true indicator of the regression strength

Back 25

no. Water demand example

Front 26

error term in true regression value, estimated regression value

Back 26

epsilon
ei or epsilon hat i

Front 27

what two things does OLS not do

Back 27

panel data, time series data

Front 28

3 reasons for OLS

Back 28

easy to use
minimizes error
each beta hats are useful characteristics

Front 29

regression analysis

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technique used to explain a linear relationship between independent and dependent variables

Front 30

expected value

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expected influence that the independent variable has on the dependent variable

Front 31

time series

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regression that studies the relationship between a dependent variable OVER time

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degrees of freedom

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number of observations minus the number of estimated coefficient or samples

Front 33

why not always use adjusted R squared

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theory and previous research are just as important

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Steps of Regression:

1. review literature and developt theoretical model

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-see what other people are doing
-apply previous models or develop new ones if you agree or disagree)

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Steps of Regression:

2. specify the model: select independent variables and functional form

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1. specify independent variable and how they should be measured
2. specify mathematical form of variables.
3. specify type of stochastic error term.

steps 1-3 avoid specification error

exclude variables that have little affect

use dummy variables

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Steps of Regression:

3. hypothesize expected sign of coefficients

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positive or negative

Qdemand=f(P,Y,P)+ e

add + or - to the top

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Steps of Regression:

4. collect, inspect, clean data

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determine units of measurement
look for typographical, conceptual, or definitional errors

Front 38

Steps of Regression:

5. estimate, evaluate equation

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walk fine line between going back and looking for more data vs trying to continue to fix the equation until it fits expcted theory

Front 39

Steps of Regression:

6. document results

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produce equation,N, adjusted R squared

Front 40

does beta zero mean anything?

Back 40

only when zero is included in the range of xi values

Front 41

The Classical Assumptions:

1. Regression model is linear, correctly specified, and has an additive error term

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-model is linear (or transformed)
-has no omitted variables or incorrect functional form
-additive error term that is not multiplied or divided

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The Classical Assumptions:

2. error term has zero population mean

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mean of the error distribution is zero

Front 43

The Classical Assumptions:

3. all explanatory variables are uncorrelated with the error term

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do not let an error term and an explanatory variable move together

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The Classical Assumptions:

4. observations of the error term are uncorrelated with each other

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observations are independently drawn from each other

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The Classical Assumptions:

5. error term has a constant variance

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constant variance- homoskedastic
not having constant variance (increasing or decreasing)-heteroskedastic

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The Classical Assumptions:

6. no explanatory variable is a perfect linear function of any other explanatory variable

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collinearity or multicollinearity between two variables implies that they are really the same variable.

(ex: tire sales and sales tax (which are a % of sales)

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The Classical Assumptions:

7. error term is normally distributed

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error must be drawn independently from a distribution with a mean of zero. as number of errors gets larger, distribution approaches a bell shaped curve (standard normal distribution)

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unbiasedness

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mean of sample distribution to be the same as the mean of the population

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biased estimator

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mean of the sample is not the same as the mean of the population

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how to decrease variance

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increase sample size

Front 51

mean square error

Back 51

variance + square of the bias