Economics 421 - Econometrics

Topics for Final

We covered the following topics in the course:

1. Assumptions required for estimates to be BLUE

2. Hypothesis testing:

a. t- tests (both one-sided and two-sided)
b. Joint hypotheses (F-Tests, Chi-square tests, etc.)

i.  Exclusion restrictions
ii. Linear combinations of parameters

3. Heteroskedasticity

a. What is heteroskedasticity?
b. How heteroskedasticity occur?
c. The consequences of estimating a heteroskedastic model with OLS
d. Tests

i.   LaGrange Multiplier Tests (Models  1, 2, and 3)

Model 1:
Model 2:
Model 3:

ii.  Goldfeld-Quandt test
iii. White's test

e. Corrections/Estimation procedures

i.   Feasible GLS

Model 1:
Model 2:
Model 3:

iii. White’s correction

8. Autocorrelation

a. What is it and why might it occur?
b. Consequences of ignoring serial correlation and estimating with OLS

i.   Model with serially correlated errors (model 1 in class)
ii.  Model including a lagged dependent variable (model 2 in class)
iii. Model with both a lagged dependent variable and serial correlated errors (model 3 in class)

c. Tests for serial correlation

i.   The Durbin-Watson test.
ii.  Durbin's h-test.
iii. The Breusch-Godfrey test for higher order serial correlation.

d. Corrections

i.  Non-linear estimation

9. Testing for ARCH errors

10. Stochastic Regressors and Measurement Errors

a. Assessing the bias and consistency of an estimator
b. Errors in variables

i. Consequences of estimating with OLS when there are errors in measuring the right-hand side variables (i.e. errors in measuring the independent variables, the X's).
ii. Consequences of estimating with OLS when there are errors in measuring the dependent variable (i.e. in the measurement of Y).
iii. Application of errors in variables: Friedman's Permanent Income Hypothesis.

c. Instrumental variable estimation

i. What is an instrument.
ii. How is IV performed?
iii. Show how IV estimation can solve the problem of correlation of the right-hand side variables with the error term.

11. Simultaneous equation models

a. Structural equations (behavioral, identities, equilibrium conditions, technical equations) and reduced form equations. Endogenous, exogenous, and predetermined variables.
b. Consequences of ignoring simultaneity, i.e. demonstrate simultaneity bias.
c. Underidentified models, exactly identified models, and overidentified models

d. Estimation by 2SLS

12. Multicollinearity

a. What is multicollinearity and how does it affect OLS estimates and standard errors?
b. Detection of multicollinearity
c. What to do for perfect and imperfect multicollinearity.

13. Specification tests

a. LM test for adding a variable to a model
b. LM test for adding a variable in a system of equations
c. LM test for serial correlation in a system of equations
d. AIC and SIC criterion

14. Qualitative and limited dependent variables

a. Linear probability model

i. description of model, problems, and estimation

b. Probit model

i. description of model and estimation

c. Logit model

i. description of model, attractive properties, and estimation

d. Limited dependent variables

i. description of the model when the dependent variable is limited, problems with OLS, and estimation

15. Maximum likelihood

a. Brief description of what maximum likelihood estimation does.
b. properties of maximum likelihood estimators.

Topics for Midterm

[Note: Previous midterms are here.]

1. Assumptions required for estimates to be BLUE

2. Hypothesis testing:

a. t- tests (both one-sided and two-sided)
b. Joint hypotheses (F-Tests, Chi-square tests, etc.)

i.  Exclusion restrictions
ii. Linear combinations of parameters

3. Heteroskedasticity

a. What is heteroskedasticity?
b. How heteroskedasticity occur?
c. The consequences of estimating a heteroskedastic model with OLS
d. Tests

i.   LaGrange Multiplier Tests (Models  1, 2, and 3)

Model 1:
Model 2:
Model 3:

ii.  Goldfeld-Quandt test
iii. White's test

e. Corrections/Estimation procedures

i.   Feasible GLS

Model 1:
Model 2:
Model 3:

iii. White’s correction

8. Autocorrelation

a. What is it and why might it occur?
b. Consequences of ignoring serial correlation and estimating with OLS

i.   Model with serially correlated errors (model 1 in class)
ii.  Model including a lagged dependent variable (model 2 in class)
iii. Model with both a lagged dependent variable and serial correlated errors (model 3 in class)

c. Tests for serial correlation

i.   The Durbin-Watson test.
ii.  Durbin's h-test.
iii. The Breusch-Godfrey test for higher order serial correlation.

d. Corrections

i.  Non-linear estimation

9. Testing for ARCH errors

10. Stochastic Regressors and Measurement Errors

a. Assessing the bias and consistency of an estimator
b. Errors in variables

i. Consequences of estimating with OLS when there are errors in measuring the right-hand side variables (i.e. errors in measuring the independent variables, the X's).
ii. Consequences of estimating with OLS when there are errors in measuring the dependent variable (i.e. in the measurement of Y).

LM Tests for Heteroskedasticity

Here is an outline of the LM tests for Heteroskedasticity:

Topics for Final

We covered the following topics in the course:

1. Assumption required for estimates to be BLUE

2. Hypothesis testing:

a. t- tests (both one-sided and two-sided)
b. Joint hypotheses (F-Tests, Chi-square tests, etc.)

i.  Exclusion restrictions
ii. Linear combinations of parameters

3. Heteroskedasticity

a. What is heteroskedasticity?
b. How heteroskedasticity occur?
c. The consequences of estimating a heteroskedastic model with OLS
d. Tests

i.   LaGrange Multiplier Tests (Models  1, 2, and 3)

Model 1:
Model 2:
Model 3:

ii.  Goldfeld-Quandt test
iii. White's test

e. Corrections/Estimation procedures

i.   Multiplicative:
ii.  Feasible GLS

Model 1:
Model 2:
Model 3:

iii. White’s correction

8. Autocorrelation

a. What is it and why might it occur?
b. Consequences of ignoring serial correlation and estimating with OLS

i.   Model including a lagged dependent variable
ii.  Model with serially correlated errors
iii. Model with both a lagged dependent variable and serial correlated errors.

c. Tests for serial correlation

i.   The Durbin-Watson test.
ii.  Durbin's h-test.
iii. The Breusch-Godfrey test for higher order serial correlation.

d. Corrections

i.  Non-linear estimation
ii. The CORC procedure

9. Testing for ARCH errors

10. Stochastic Regressors and Measurement Errors

a. Assessing the bias and consistency of an estimator
b. Errors in variables

i. Consequences of estimating with OLS (differences in mismeasurement. of  the dependent variable and the independent variables).
ii. Application of errors in variables: Friedman's Permanent Income Hypothesis.

c. Instrumental variable estimation

i. What is an instrument.
ii. How is IV performed?
iii. Show how IV estimation can solve the problem of correlation of the right-hand side variables with the error term.

11. Simultaneous equation models

a. Structural equations (behavioral, identities, equilibrium conditions, technical equations) and reduced form equations. Endogenous, exogenous, and predetermined variables.
b. Consequences of ignoring simultaneity, i.e. demonstrate simultaneity bias.
c. Underidentified models, exactly identified models, and overidentified models

d. Estimation by 2SLS

12. Multicollinearity

a. What is multicollinearity and how does it affect OLS estimates and standard errors?
b. Detection of multicollinearity
c. What to do for perfect and imperfect multicollinearity.

13. Specification tests

a. LM test for adding a variable to a model

14. Qualitative and limited dependent variables

a. Linear probability model

i. description of model, problems, and estimation

b. Probit model

i. description of model and estimation

c. Logit model

i. description of model, attractive properties, and estimation

d. Limited dependent variables

i. description of the model when the dependent variable is limited, problems with OLS, and estimation

15. Maximum likelihood

a. Brief description of what maximum likelihood estimation does.
b. properties of maximum likelihood estimators.

The Cochrane-Orcutt Procedure

The Cochrane-Orcutt procedure:

One note: In step 5 when it says to use the estimated betas obtained in step 4 in equation  (9.5),  this means to go back to the origanal equation (9.5) and find u_t = Y_t - b₁ - b₂X_2t  - ... - b_kX_kt where the b's are the estimated betas using the transformed ("starred") variables in step 4. However, be careful about obtaining the value of b₁. Note from above in equation (9.9) that you have to divide the estimate of the constant from the regression involving the "starred" values by 1-ρ in order to obtain b₁.

Topics for Midterm

[Note: We will cover the last two items on the list, CORC and ARCH, on Tuesday. Previous midterms are here.]

1. Assumptions required for estimates to be BLUE

2. Hypothesis testing:

a. t- tests (both one-sided and two-sided)
b. Joint hypotheses (F-Tests, Chi-square tests, etc.)

i.  Exclusion restrictions
ii. Linear combinations of parameters

3. Heteroskedasticity

a. What is heteroskedasticity?
b. How heteroskedasticity occur?
c. The consequences of estimating a heteroskedastic model with OLS
d. Tests

i.   LaGrange Multiplier Tests (Models  1, 2, and 3)

Model 1:
Model 2:
Model 3:

ii.  Goldfeld-Quandt test
iii. White's test

e. Corrections/Estimation procedures

i.   Multiplicative:
ii.  Feasible GLS

Model 1:
Model 2:
Model 3:

iii. White’s correction

8. Autocorrelation

a. What is it and why might it occur?
b. Consequences of ignoring serial correlation and estimating with OLS

i.   Model including a lagged dependent variable
ii.  Model with serially correlated errors
iii. Model with both a lagged dependent variable and serial correlated errors.

c. Tests for serial correlation

i.   The Durbin-Watson test.
ii.  Durbin's h-test.
iii. The Breusch-Godfrey test for higher order serial correlation.

d. Corrections

i.  Non-linear estimation
ii. The CORC procedure

9. Testing for ARCH errors

LM Tests for Heteroskedasticity

Here is an outline of the LM tests for Heteroskedasticity:

Topics for Final

We covered the following topics in the course:

1. Assumption required for estimates to be BLUE

2. Hypothesis testing:

a. t- tests (both one-sided and two-sided)
b. Joint hypotheses (F-Tests, Chi-square tests, etc.)

i.  Exclusion restrictions
ii. Linear combinations of parameters

3. Heteroskedasticity

a. What is heteroskedasticity?
b. How heteroskedasticity occur?
c. The consequences of estimating a heteroskedastic model with OLS
d. Tests

i.   LaGrange Multiplier Tests (Models  1, 2, and 3)

Model 1:
Model 2:
Model 3:

ii.  Goldfeld-Quandt test
iii. White's test

e. Corrections/Estimation procedures

i.   Multiplicative:
ii.  Feasible GLS

Model 1:
Model 2:
Model 3:

iii. White’s correction

8. Autocorrelation

a. What is it and why might it occur?
b. Consequences of ignoring serial correlation and estimating with OLS

i.   Model including a lagged dependent variable
ii.  Model with serially correlated errors
iii. Model with both a lagged dependent variable and serial correlated errors.

c. Tests for serial correlation

i.   The Durbin-Watson test.
ii.  Durbin's h-test.
iii. The Breusch-Godfrey test for higher order serial correlation.

d. Corrections

i.  Non-linear estimation
ii. The CORC procedure

9. Testing for ARCH errors

Material after Midterm

10. Stochastic Regressors and Measurement Errors

a. Assessing the bias and consistency of an estimator
b. Errors in variables

i. Consequences of estimating with OLS (differences in mismeasurement. of  the dependent variable and the independent variables).
ii. Application of errors in variables: Friedman's Permanent Income Hypothesis.

c. Instrumental variable estimation

i. What is an instrument.
ii. How is IV performed?
iii. Show how IV estimation can solve the problem of correlation of the right-hand side variables with the error term.

11. Simultaneous equation models

a. Structural equations (behavioral, identities, equilibrium conditions, technical equations) and reduced form equations. Endogenous, exogenous, and predetermined variables.
b. Consequences of ignoring simultaneity, i.e. demonstrate simultaneity bias.
c. Underidentified models, exactly identified models, and overidentified models

d. Estimation by 2SLS

12. Multicollinearity

a. What is multicollinearity and how does it affect OLS estimates and standard errors?
b. Detection of multicollinearity
c. What to do for perfect and imperfect multicollinearity.

13. Specification tests

a. LM test for adding a variable to a model (with and without endogeneity)

14. Qualitative and limited dependent variables

a. Linear probability model

i. description of model, problems, and estimation

b. Probit model

i. description of model and estimation

c. Logit model

i. description of model, attractive properties, and estimation

d. Limited dependent variables

i. description of the model when the dependent variable is limited, problems with OLS, and estimation

15. Maximum likelihood

a. Brief description of what maximum likelihood estimation does.
b. properties of maximum likelihood estimators.

Topics for Midterm

1. Assumption required for estimates to be BLUE

2. Hypothesis testing:

a. t- tests (both one-sided and two-sided)
b. Joint hypotheses (F-Tests, Chi-square tests, etc.)

i.  Exclusion restrictions
ii. Linear combinations of parameters

3. Heteroskedasticity

a. What is heteroskedasticity?
b. How heteroskedasticity occur?
c. The consequences of estimating a heteroskedastic model with OLS
d. Tests

i.   LaGrange Multiplier Tests (Models  1, 2, and 3)

Model 1:
Model 2:
Model 3:

ii.  Goldfeld-Quandt test
iii. White's test

e. Corrections/Estimation procedures

i.   Multiplicative:
ii.  Feasible GLS

Model 1:
Model 2:
Model 3:

iii. White’s correction

8. Autocorrelation

a. What is it and why might it occur?
b. Consequences of ignoring serial correlation and estimating with OLS

i.   Model including a lagged dependent variable
ii.  Model with serially correlated errors
iii. Model with both a lagged dependent variable and serial correlated errors.

c. Tests for serial correlation

i.   The Durbin-Watson test.
ii.  Durbin's h-test.
iii. The Breusch-Godfrey test for higher order serial correlation.

d. Corrections

i.  Non-linear estimation
ii. The CORC procedure

9. Testing for ARCH errors

The Cochrane-Orcutt Procedure

The Cochrane-Orcutt procedure:

One note: In step 5 when it says to use the estimated betas obtained in step 4 in equation  (9.5),  this means to go back to the origanal equation (9.5) and find u_t = Y_t - b₁ - b₂X_2t  - ... - b_kX_kt where the b's are the estimated betas using the transformed ("starred") variables in step 4. However, be careful about obtaining the value of b₁. Note from above in equation (9.9) that you have to divide the estimate of the constant from the regression involving the "starred" values by 1-ρ in order to obtain b₁.

LM Tests for Heteroskedasticity

Here is an outline of the LM tests for Heteroskedasticity:

Topics for Final

We have covered the following topics in the course:

1. Uses of regression models

a. Hypothesis testing
b. Prediction

2. Assumptions required for OLS estimator to be BLUE

3. Hypothesis testing:

a. t- tests (both one-sided and two-sided)
b. F-Tests (tests that coefficients are jointly zero and tests involving linear combinations of the coefficients)
c.  Chi-Squared tests

4. The Types of Specification Error

5. Consequences of including an irrelevant variable

6. Consequences of excluding a relevant variable

7. Heteroskedasticity

a. How heteroskedasticity might arise
b. The consequences of estimating a heteroskedastic model with OLS
c. Tests

i. Goldfeld-Quandt
ii. White's
iii. LaGrange Multiplier Tests (Models  1, 2, and 3)

d. Corrections

i. Multiplicative:
ii. Model 1:
iii. Model 2:
iv. Model 3:
v. White’s (We didn’t give an explicit function for this one)

8. Autocorrelation

a. Assumptions required for estimators to be BLUE.
b. Assessing potential bias of an estimator.
c. Consequences of ignoring serial correlation and estimating with OLS.

Topics covered after Midterm 1:

d. Tests for serial correlation

i. The Durbin-Watson test.
ii. Durbin's h-test.
iii. The Breusch-Godfrey test for higher order serial correlation.

e. The CORC procedure

f. Corrections for higher-order serial correlation and search procedures

i. Estimating a model with higher order serial correlation using a generalized CORC procedure.
ii. Hildreth-Lu search procedure.

9. Stochastic Regressors and Measurement Errors

a. Assessing the bias and consistency of an estimator
b. Errors in variables

i. Consequences of estimating with OLS (differences in mismeasurement. of  the dependent variable and the independent variables).
ii. Application of errors in variables: Friedman's Permanent Income Hypothesis.

c. Instrumental variable estimation

i. What is an instrument.
ii. How is IV performed?
iii. Show how IV estimation can solve the problem of correlation of the right-hand side variables with the error term.

10. Simultaneous equation models

a. Structural equations (behavioral, identities, equilibrium conditions, technical equations) and reduced form equations. Endogenous, exogenous, and predetermined variables.
b. Consequences of ignoring simultaneity, i.e. demonstrate simultaneity bias.
c. Underidentified models, exactly identified models, and overidentified models

Topics covered after Midterm 2:

d. Estimation by 2SLS

11. Multicollinearity

a. What is multicollinearity and how does it affect OLS estimates and standard errors?
b. Detection of multicollinearity
c. What to do for perfect and imperfect multicollinearity.

12. Specification tests

a. LM test for adding a variable to a model (with and without endogeneity)

13. Qualitative and limited dependent variables

a. Linear probability model

i. description of model, problems, and estimation

b. Probit model

i. description of model and estimation

c. Logit model

i. description of model, attractive properties, and estimation

d. Limited dependent variables

i. description of the model, OLS when the dependent variable is limited, and estimation

14. Maximum likelihood

a. Brief description of what maximum likelihood estimation does.
b. properties of maximum likelihood estmators.

 

Topics for Exam 2

We have covered the following topics since the last midterm:

Serial Correlation (continued)

1. Tests for serial correlation

a. The Durbin-Watson test.
b. Durbin's h-test.
c. The Breusch-Godfrey test for higher order serial correlation.

2. The CORC procedure

3. Corrections for higher-order serial correlation and search procedures

a. Estimating a model with higher order serial correlation using a generalized CORC procedure.
b. Hildreth-Lu search procedure.

Stochastic Regressors and Measurement Errors

3. Assessing the bias and consistency of an estimator

4. Errors in variables

a. Consequences of estimating with OLS (differences in mismeasurement. of  the dependent variable and the independent variables).
b. Application of errors in variables: Friedman's Permanent Income Hypothesis.

5. Instrumental variable estimation

a. What is an instrument.
b. How is IV performed?
c. Show how IV estimation can solve the problem of correlation of the right-hand side variables with the error term.

Simultaneous equation models

6. Structural equations (behavioral, identities, equilibrium conditions, technical equations) and reduced form equations. Endogenous, exogenous, and predetermined variables

7. Consequences of ignoring simultaneity, i.e. demonstrate simultaneity bias

8. Underidentified models, exactly identified models, and overidentified models

Topics for Exam 1

We have covered the following topics

1. Uses of regression models

a. Hypothesis testing
b. Prediction

2. Assumptions required for OLS estimator to be BLUE

3. Hypothesis testing:

a. t- tests (both one-sided and two-sided)
b. F-Tests (tests that coefficients are jointly zero and tests involving linear combinations of the coefficients)
c.  Chi-Squared tests

4. The Types of Specification Error

5. Consequences of including an irrelevant variable

6. Consequences of excluding a relevant variable

7. Heteroskedasticity

a. How heteroskedasticity might arise
b. The consequences of estimating a heteroskedastic model with OLS
c. Tests

Goldfeld-Quandt
White
LaGrange Multiplier Tests (Models  1, 2, and 3)

d. Corrections

Multiplicative         
Model 1                     
Model 2                     
Model 3                     
White’s                       (We didn’t give an explicit function for this one)

8. Autocorrelation

a. Assumptions required for estimators to be BLUE.
b. Assessing potential bias of an estimator.
c. Consequences of ignoring serial correlation and estimating with OLS.

Review for Final

[pdf file]

Midterm 1 will cover the following topics:

• The two uses of regression models
• The assumptions underlying the Guass-Markov Theorem
• The Guass-Markov Theorem and what BLUE means
• Reasons for an error in a regression model
• How t-tests and z-tests differ (when to use each one)
• Single hypothesis tests: z-tests and t-tests
• Multiple hypothesis tests: F-tests and Chi-Square tests
• [Should be able to do hypothesis tests with these four distributions]
• Functional form: changes in units
• Functional form: log-linear, semi-log, reciprocal, and log reciprocal models (see page 190 for a summary).
• Use of dummy variables in regression models
• Dummy variable trap
• Chow tests and dummy variables
• Piecewise linear regression
• Model selection criteria
• Types of specification errors
• Consequences of omitting a relevant variable
• Consequences of including an irrelevant variable
• LM test for adding variables to a regression model
• How to do an AIC test
• Consequences of errors in measuring y
• Consequences of errors in measuring the x variables.
• Perfect and imperfect multicollinearity
• Consequences of multicollinearity
• Detection of multicollinearity
• What to do about multicollinearity

Here's an outline of the material for the second exam:

Heteroskedasticity (Chapter 11)

• How it is defined
• How it might arise
• Effect on estimator, test statistics, etc. if OLS used
• Testing for Heteroskedasticity
  • Graphs (suggestive only)
  • La Grange Multiplier tests
    • (i)   Breusch-pagan
    • (ii)  Glesjer
    • (iii) Park
  • Goldfeld-Quandt test
  • White’s test (recommended if N large enough)

• Estimation procedures
  • White’s correction
  • GLS
  • FGLS (Feasible GLS)
    • (i)   Known proportional factor
    • (ii)  Breusch-pagan
    • (iii) Glesjer
    • (iv) Park

Autocorrelation (Chapter 12)

• How it is defined and how it expresses itself in a regression model
  •   Show how the corr(u_t , u_t-s) changes with s
• How serial correlation might arise
• Effect on estimator, test statistics, etc. if OLS used
• Testing for autocorrelation
  • Graphs (suggestive only)
  • Durbin-Watson Test
    • Show statistic is between 0 and 4
    • Advantages and disadvantages relative to Breusch-Godfrey
  • Breusch-Godfrey LM test

• Estimation procedures
  • GLS
  • FGLS (Feasible GLS)
    • Cochrane-Orcutt
    • Grid search
  • Note: I didn't quite finish this section and will add more after the exam.

Material since Exam #2

Chapter 12 [cont.]

• Use of lagged dependent variables to solve the autocorrelation problem

Simultaneous Equation Models (Chapter 18)

• Simultaneous equation models
  • endogenous, exogenous, and predetermined   variables
  • structural models and reduced forms
• simultaneous equation bias

Identification (Chapter 19)

• Explaining identification intuitively with, say, a supply and demand model
• Under, exact, and overidentification
• Rules for identification
  • Order condition explicitly
  • Rank condition intuitively

Estimation of Simultaneous Equation Models (Chapter 20)

• Indirect least squares (ILS)
  • Exact identification (works)
  • Over or under identified (won't work)
• Two-stage least squares (2SLS)
  
  • Exact or over identified (works)
  • Over or under identification (won't work)
  • Stage 1
  • Stage 2
  • Standard errors

White's Test for Heteroskedasticity

FGLS for Heteroskedasticity

Note:  Weighted least squares means to divide through by the estimated standard error.

LM Tests for Heteroskedasticity

There seems to be confusion over the LM tests for Heteroskedasticity. They are Chi-Square tests the way we learned them, not t-tests and not F-tests. Here are the procedures (the tests are named a bit differently here):

The Cochrane-Orcutt Procedure

The Cochrane-Orcutt procedure:

One note: In step 5 when it says to use the estimated betas obtained in step 4 in equation  (9.5),  this means to go back to the origanal equation (9.5) and find u_t = Y_t - b₁ - b₂X_2t  - ... - b_kX_kt where the b's are the estimated betas using the transformed ("starred") variables in step 4.

Review for Midterm 2

Here's an outline of the material for the second exam:

Heteroskedasticity (Chapter 11)

• How it is defined
• How it might arise
• Effect on estimator, test statistics, etc. if OLS used
• Testing for Heteroskedasticity
  • Graphs (suggestive only)
  • La Grange Multiplier tests
    • (i)   Breusch-pagan
    • (ii)  Glesjer
    • (iii) Park
  • Goldfeld-Quandt test
  • White’s test (recommended if N large enough)

• Estimation procedures
  • White’s correction
  • GLS
  • FGLS (Feasible GLS)
    • (i)   Known proportional factor
    • (ii)  Breusch-pagan
    • (iii) Glesjer
    • (iv) Park

Autocorrelation (Chapter 12)

• How it is defined and how it expresses itself in a regression model
  • Show how the corr(u_t , u_t-s) changes with s
• How serial correlation might arise
• Effect on estimator, test statistics, etc. if OLS used
• Testing for autocorrelation
  • Graphs (suggestive only)
  • Durbin-Watson Test
    • Show statistic is between 0 and 4
    • Advantages and disadvantages relative to Breusch-Godfrey
  • Breusch-Godfrey LM test

• Estimation procedures
  • GLS
  • FGLS (Feasible GLS)
    • Cochrane-Orcutt
    • Grid search
  • Note: I didn't quite finish this section and will add more after the exam.

What We've Covered Since the Midterm

What we've covered since the midterm:

Heteroskedasticity (Chapter 11)

• How it is defined
• How it might arise
• Effect on estimator, test statistics, etc. if OLS used
• Testing for Heteroskedasticity
  • Graphs (suggestive only)
  • La Grange Multiplier tests
    • (i)   Breusch-pagan
    • (ii)  Glesjer
    • (iii) Park
  • Goldfeld-Quandt test
  • White’s test (recommended if N large enough)

• Estimation procedures
• White’s correction
• GLS
• FGLS (Feasible GLS)
  • (i)   Know proportional factor
  • (ii)  Breusch-pagan
  • (iii) Glesjer
  • (iv) Park

List of Study Topics for Exam

The midterm will cover the following topics:

The two uses of regression models

The assumptions underlying the Guass-Markov Theorem

The Guass-Markov Theorem and what BLUE means

Reasons for an error in a regression model

How t-tests and z-tests differ (when to use each one)

Single hypothesis tests: z-tests and t-tests

Multiple hypothesis tests: F-tests and Chi-Square tests

[Should be able to do hypothesis tests with these four distributions]

Functional form: changes in units

Functional form: log-linear, semi-log, reciprocal, and log reciprocal models (see page 190 for a summary).

Use of dummy variables in regression models

Dummy variable trap

Chow tests and dummy variables

Piecewise linear regression

Model selection criteria

Types of specification errors

Consequences of omitting a relevant variable

Consequences of including an irrelevant variable

LM test for adding variables to a regression model

How to do an AIC test

Consequences of errors in measuring y

Consequences of errors in measuring the x variables.

Perfect and imperfect multicollinearity

Consequences of multicollinearity

Detection of multicollinearity

What to do about multicollinearity

Summary of Topics Covered to Date

Review Topics

So far we have covered:

The two uses of regression models

The assumptions underlying the Guass-Markov Theorem

The Guass-Markov Theorem and what BLUE means

Reasons for an error in a regression model

How t-tests and z-tests differ (when to use each one)

Single hypothesis tests: z-tests and t-tests

Multiple hypothesis tests: F-tests and Chi-Square tests

[Should be able to do hypothesis tests with these four distributions]

Functional form: changes in units

Functional form: log-linear, semi-log, reciprocal, and log reciprocal models (see page 190 for a summary).

Use of dummy variables in regression models

Dummy variable trap

Chow tests and dummy variables

Piecewise linear regression

Model selection criteria

Types of specification errors

Consequences of omitting a relevant variable