Chapter 4

Question 1

What are the 5 steps in demand estimation?

Answer 1

1. identification of variables
2. collection of data
3. specification of the demand model
4. estimation of the parameters of the model
5. development of forecasts based on the model

Question 2

Linear*

Answer 2

*

Question 3

Multiplicative*

Answer 3

*

Question 4

Simple linear regression assumptions**

Answer 4

1. Y is a random variable whose distribution depends on the value of X, which is measured without error
2. There is a linear relationship between the expected value of Y and each possible value of X…i.e. E(Y|X) = a + BX
3. The actual value of Y associated with each value of X is some of E(Y|X) and a stochastic error term E…i.e. Y = E(Y|X) + E = a + BX + E
4. The error term has the following properties for all observations: **

Question 5

Simple regression model*

Answer 5

Q = a + BP + E

Question 6

What does R^2 mean in simple regression?

Answer 6

That the model explains XX% of the total variation of Q

Question 7

What does the F significance in simple regression tell us?

Answer 7

We are essentially XX% sure the model predicts better than the sample mean of Q

Question 8

T-statistics, how to find* and what it tells us.

Answer 8

t = ( B - Bb) / se
2 sided: |t-stat| > critical value = reject Ho
1 sided: t-stat < -critical value, if hypothesizing ‘-‘ coefficient

Question 9

Multiple linear regression (assumptions continued)

Answer 9

5. The number of observations (n) must exceed the number of parameters to be estimated (m+1)
6. There is no exact linear relationship among any of the independent variables

Question 10

Multiple linear regression*

Answer 10

Q = a + B1P + B2Y + B3T + E

could have 3 different prices instead of time and income affecting it

Question 11

Multiple linear regression elasticity of demand*

Answer 11

E(Q,P) = (dQ/dP) * (P/E(Q|P)) = B1 * (P/E(Q|P))

Question 12

Multiplicative exponential model*

Answer 12

*

Question 13

Semilogarithmic model*

Answer 13

*

Question 14

Reciprocal model*

Answer 14

*

Question 15

Polynomial model*

Answer 15

*

Question 16

Exponential smoothing*

Answer 16

*

Question 17

Autocorrelation

Answer 17

Error terms are correlated over time, space, etc

- Parameter estimates are unbiased, but estimates of standard error are biased we lose efficiency

Question 18

Heteroscedasticity

Answer 18

Error terms do not have constant variance

- parameter estimates are unbiased, but estimates of se are biased we lose efficiency

Question 19

Measurement error

Answer 19

Random errors in measuring an explanatory variable or the dependent variable

• if measurement errors are correlated with an explanatory variable regression results are biased
• measurement errors in dependent variable are reflected in se of error term

Question 20

Multicollinearity

Answer 20

A high degree of correlation among explanatory variables

• results are not biases, but its difficult to sort out effects of each variable
• overall, model has good explanatory power, but few explanatory variables are statistically significant

Question 21

Simultaneous equation relationships

Answer 21

The dependent variable and one or more explanatory variables are simultaneously determined - i.e. price and quantity are simultaneously determined in a supply-demand system
- challenge is to recognize interdependence, then specify a model that takes it into account. adding other demand-supply shifters in one way to address this

Question 22

RMSE****

Answer 22

Standard deviation - always pos***

Question 23

What is the main difference between the linear and growth model?

Answer 23

linear shows a constant unit change, growth shows a % change