# 4.) The Classical Model Flashcards

1
Q

The term classical refers to a set of fairly basic assumptions required to hold in order for…

A

OLS to be considered the “best” estimator for regression models

2
Q

The CLASSICAL ASSUMPTIONS must be…

A

met in order for OLS estimators to be the best available

3
Q

The Classical Assumptions are…

A
1. ) The regression model is linear, is correctly specified, and has an additive error term.
2. ) The error term has zero population mean.
3. ) All explanatory variables are uncorrelated with the error term.
4. ) Observations of the error term are uncorrelated with each other (no serial correlation).
5. ) The error term has a constant variance (no heteroskadasticity).
6. ) No explanatory variable is a perfect linear function of any other explanatory variable(s) (no perfect multicollinearity)
7. ) The error term is normally distributed (this assumption is optional but usually is invoked).
4
Q

Assumption I

A

The regression model is linear, is correctly specified, and has an additive error term

5
Q

Assumption II

A

the error term has a zero population mean

6
Q

Econometricians add a stochastic (random) error term…

A

to regression equations to account variation in the dependent variable that is not explained by the model

7
Q

The properties of the OLS estimator of the betas still hold because the equation is linear. Two additional properties also must hold.

A
1. ) We assume that the equation is correctly specified. If an equation has omitted variable or an incorrect functional form, the odds are against that equation work ing well.
2. ) We assume that a stochastic error term has been added to the equation. This error term must be an additive one and cannot be multiplied by or divided into any of the variables in the equation.
8
Q

In essence, the constant term equals…

A

the fixed portion of Y that cannot be explained by independent variables whereas the error term equals the stochastic portion of the unexplained value of Y.

9
Q

Assumption III

A

All explanatory variables are uncorrelated with the error term. It is assumed that the observed values of the explanatory variables are independent of the values of the error term.

10
Q

Assumption IV

A

Observation of the error term are uncorrelated with each other. The observations of the error term are drawn independently from each other

11
Q

If an explanatory variable and the error term were instead correlated with each other, the OLS estimates would be likely to attribute…

A

to the X some of the variation in Y that actually came from the error term

12
Q

If the error term and X were positively correlated then…

A

the estimated coefficient would probably be higher than it would otherwise have been (biased upward). because the OLS program would mistakenly attribute the variation in Y caused by error term to X instead

13
Q

Classical Assumption III is violated most frequently when…

A

a researcher omits an important independent variable from an equation.

14
Q

Classical Assumption V

A

The error term has a constant variance

15
Q

To meet classical assumption V, the variance of the distribution from which the observations of the error term are drawn is…

A

constant

16
Q

The violation of assumption V is referred to as…

A

17
Q

Classical Assumption VI

A

No explanatory variable is a perfect linear function of any other explanatory variables(s).

18
Q

Perfect collinearity between two independent variables implies…

A

that they are really the same variable, or that one is a multiple of the other, and/or that a constant has been added to one of the variables

19
Q

Many instances of collinearity (or multicollinearity if more than two independent variables are involved)

A

are the result of the researcher not accounting for identities (definitional equaivalences) among the independent variables

20
Q

Classical Assumption VII

A

The Error Term is normally distributed

21
Q

Assumption VII states that …

A

the observations of the error term are drawn from a distribution that is normal

22
Q

The major application of normal distribution of the error term is…

A

in hypothesis test, which uses the estimated regression coefficient to investigate hypotheses about econ behavior

23
Q

Even though Assumption VII is optional, it’s usually advisable to add the assumption of normality fo the other six assumption for two reasons….

A
1. ) The error term can be though of as the sum of a number of minor influences or errors. As the number of these minor influences gets larger, the distribution of the error term tends to approach the normal distribution
2. ) The t-statistic and the F-statistic are not truly applicable unless the eror term is normally distributed
24
Q

The probability distribution of betas values across different samples

A

is called the sampling distribution of Beta

25
Q

An estimator is a…

A

formula, such as the OLS formula

26
Q

An estimate is…

A

the value of beta computed by the formula for a given sample

27
Q

A desirable property of a distribution of estimates is that its mean equals…

A

the true mean of the variable being estimated

28
Q

If an estimator produces betas that are not centered around the true beta…

A

the estimator is refered to as a biased estimator

29
Q

a estimator beta is an unbiased estimator if it sampling distribution has…

A

as its expected value the true value of beta

E(Beta Predicted) = Beta

30
Q

The Mean Square Error is equal to..

A

the variance plus the square of the bias.

31
Q

The lower the Mean Square Error (MSE) …

A

the better

32
Q

The Gauss-Markov Theorem States that…

A

Given Classical assumptions I through VI (Assumption VII, normality, is not needed for this theorem), the Ordinary Least Squares estimator of beta parameter is the minimum variance estimator from among the set of all linear unbiased estimates of beta k, for k = 0,1,2,…,K.

33
Q

Since the standard error of the estimated coefficient is the square root of the estimated variance of the betas…

A

it is similarly affected by the size of the sample and the other factors we’ve mentioned

34
Q

An unbiased estimator with the smallest variance is called…

A

efficient, and that estimator is said to have the property of efficiency

35
Q

If all seven assumptions are met, OLS is…

A

“BLUE”

36
Q

OLS is BLUE (stands for…)

A

Best (meaning minimum variance) Linear Unbiased Estimor