Econometrics

Question 1

1.3: Time Series Data Set

Answer 1

A time series data set consists of observations on a variable or several variables over
time.

Question 2

1.3: Cross-Sectional Data Set

Answer 2

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

Question 3

1.3: Pooled Cross Section Data Set

Answer 3

A data configuration where
independent cross sections, usually collected at different
points in time, are combined to produce a single
data set.

Question 4

1.3: Panel Data Set

Answer 4

A data set constructed from repeated cross
sections over time. With a balanced panel, the same
units appear in each time period. With an unbalanced
panel, some units do not appear in each time period,
often due to attrition.

Question 5

2.1: Simple Linear Regression Model

Answer 5

Question 6

2.1: The Zero Conditional Mean Assumption

Answer 6

Question 7

2.1: Population Regression Function (PRF)

Answer 7

Question 8

2.2: Equation of the Slope Parameter

Answer 8

Question 9

2.2: Equation for the Intercept Parameter

Answer 9

Question 10

2.2: OLS Regression Line/ Sample Regression Function

Answer 10

Question 11

2.3: Total Sum of Squares, Explained Sum of Squares and Residual Sum of Squares

Answer 11

Question 12

2.3: Coefficient of Determination

Answer 12

Question 13

2.4: Δy in level-level, log-level, level-log, and log-log models

Answer 13

Question 14

2.5: The Four Assumptions for Unbiasedness of OLS

Answer 14

Question 15

2.5: Proof of Unbiasedness of the OLS Slope Parameter

Answer 15

Question 16

2.5: Proof of Unbiasedness of the OLS Intercept Parameter

Answer 16

Question 17

2.5: Sample Variances of the OLS Estimators

Answer 17

Question 18

2.5: Definition of Homoskedasticity

Answer 18

Question 19

2.5: Definition of Heteroskedasticity

Answer 19

Question 20

2.5: Unbiased Estimator of the Error Variance and Standard Error of Regression

Answer 20

Question 21

2.5: Standard Error of the Estimated Slope Parameter

Answer 21

Question 22

2.6: Regression Through the Origin

Answer 22

Question 23

3.1: General Multiple Linear Regression Model

Answer 23

Question 24

3.1: The Zero Conditional Mean Assumption for Multiple Regression

Answer 24

Question 25

3.2: Sample Regression Function for Mutiple Regression

Answer 25

Question 26

3.2: Equation for the Slope Parameter for Multiple Regression

Answer 26

Question 27

3.2: Comparison of Simple and Multiple Regression Estimators

Answer 27

Question 28

3.3: The Four Assumptions for Unbiasedness of Multiple OLS

Answer 28

Question 29

3.4: Sample Variance of Simple OLS Estimator

Answer 29

Question 30

3.4: Sampling Variances of Multiple OLS Slope Parameters

Answer 30

Question 31

3.4: Standard Deviation and Standard Error of Parameters

Answer 31

Question 32

4.1: Assumption MLR.6: Normality

Answer 32

Question 33

4.1: Normal Sampling Distribution Theorem

Answer 33

Question 34

4.2: *t* Distribution for the Standardized Estimators

Answer 34

Question 35

4.2: The *t* Statistic for a Parameter

Answer 35

Question 36

4.2: Testing Against One-Sided Alternatives

Answer 36

Question 37

4.2: Testing Against Two-tailed Alternatives

Answer 37

Question 38

4.3: Confidence Interval for Population Parameters

Answer 38

Question 39

4.4: Test Statistic for Testing Parameters

Answer 39

Question 40

4.4: Comparing Two Parameters Method 2

Answer 40

Question 41

4.5: F Statistic for the Multiple Linear Restrictions Test

Answer 41

Question 42

4.5: Steps for Testing Multiple Linear Restrictions

Answer 42

Question 43

4.5: R-Squared Form of the F Statistic

Answer 43

Question 44

4.5: Testing the Overall Significance of Resgression

Answer 44

Question 45

5.1: Assumption MLR.4' Zero Mean and Zero Correlation

Answer 45

Question 46

5.1: Consistency of OLS Theorem

Answer 46

Question 47

5.1: Deriving the Inconsistenct in OLS

Answer 47

Question 48

5.2: Asymptotic Normality of OLS

Answer 48

Question 49

5.2: Langrange Multiplier Statistic

Answer 49

Question 50

6.1: Beta Coeffiecients

Answer 50

Question 51

6.2: Making Logarithmic Approximations Accurate

Answer 51

Question 52

6.2: Models with Interaction Terms

Answer 52

Question 53

6.3: Adjusted R-Squared

Answer 53

Question 54

6.3: Using Adjusted R-Squared to Choose between Functional Forms

Answer 54

Question 55

6.4: Confidence Interval for Prediction

Answer 55

Question 56

6.4: Confidence Interval for a Particular Value

Answer 56

Question 57

6.4: Predicting y with a Logarithmic Dependent Variable

Answer 57

Question 58

7.2: Dummy Variables on the Intercept

Answer 58

Question 59

7.3: Uncentered Coefficient of Determination

Answer 59

Question 60

7.3: Ordinal Information Using Dummy Variables

Answer 60

Question 61

7.4: Dummy Variables on the Slope

Answer 61

Question 62

7.4: Testing for Differences in Regression Functions across Groups

Answer 62

Question 63

7.5: Linear Probability Model (LPM)

Answer 63

Question 64

8.2: Heteroskedasticity-Robust Variance for Simple Regression

Answer 64

Question 65

8.2: Heteroskedasticity-Robust Variance for Multiple Regression

Answer 65

Question 66

8.2: Computing Heteroskedasticity-Robust LM Tests

Answer 66

Question 67

8.3: The Breusch-Pagan Test for Heteroskedasticity

Answer 67

Question 68

8.3: A Special Case of the White Test for Heteroskedasticity

Answer 68

Question 69

9.1: Regression Specification Error Test (RESET)

Answer 69

Question 70

9.1: The Davidson-MacKinnon Test

Answer 70