ECON302 - Intro. to Econometrics II (2014Fall)
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Syllabus


The course provides an elementary but comprehensive look to the practice of modern econometrics. The main topics covered include heteroscedasticity, consequences of heteroscedasticity, detecting heteroscedasticity, Breusch-Pagan LM Test, Harvey-Godfrey LM Test, Goldfeld-Quandt Test, White Test, resolving heteroscedasticity, Generalized or Weighted Least Squares, White’s heteroscedasticity consistent variances and standard errors,  autocorrelation, consequences of autocorrelation, detecting autocorrelation, graphical method, Durbin-Watson Test, Breusch-Godfrey LM test for serial correlation, Durbin’s h test, resolving autocorrelation, Generalized Least Squares (GLS), Estimated GLS (EGLS), Newey-West method, common-factor test, apparent autocorrelation,  autoregressive conditional heteroscedasticity (ARCH) model, testing for ARCH effects, estimation of ARCH models, generalized autoregressive conditional heteroscedasticity (GARCH) model, estimation of GARCH Models, estimating dynamic models, adjustment lags, problems in the estimation of dynamic models, formation of Expectations: adaptive expectations, rational expectations, consequences and detection of errors of specification, data mining, alternative approaches to selecting the best model, selecting models: some important criteria, general to specific modelling, stationarity and non-stationarity, unit roots and spurious regressions, testing for unit roots, Dickey-Fuller test, Augmented Dickey-Fuller test, Phillips-Perron test, cointegration, cointegration and error-correction model (ECM), cointegration in single equations (The Engle-Granger approach, drawbacks of Engle Granger approach), vector autoregressive (VAR) models, causality tests (Granger causality test, Sims causality test), cointegration in multiple equations, Johansen approach, steps of Johansen approach, identification in standard and cointegrated systems (order condition, rank condition), traditional panel data models, fixed effects model, random effects model, Hausman test, non-stationary panels, panel unit root tests (Levin and Lin test; Im, Pesaran and Shin test, Maddala and Wu test), panel cointegration tests (Kao test, McCoskey test, Pedroni tests, Larsson test), simultaneous equation models, simultaneity, identification problem, conditions of identification, estimation of an exactly identified equation: indirect least squares (ILS), and estimation of an over-identified equation: two-stage least squares (TSLS).

 

Lecturer                                Dr. H. Ozan ERUYGUR

Gazi University, FEAS, Department of Economics, Room: 112.

E-mail: oeruygur@gmail.com, Phone: 216 1112

 

Research Assistant                 

 

Course Schedule                     

 

Rec. and Lab. Hours               To be announced.

 

Office Hours                           To be announced.

 

Recommended Books             - Thomas, R. L. (1996) Modern Econometrics, Prentice Hall, New York.

- Asteriou, D., and Hall, S. G. (2011) Applied Econometrics, Second Edition, Palgrave Macmillan, New York.

- Gujarati, D., and Porter, D. (2009) Basic Econometrics, Fifth Edition, McGraw-Hill.

- Charemza, W.W., and Deadman, D. F. (1999) New Directions in Econometric Practice, Second Edition, Edward Elgar.

                                                                                                                                               

Assessment                        The course is assessed by two midterm exams, a lab exam, a term project and a final examination. The weights are as follows:

 

Lab Exam                                % 5

Term Project                            % 10

First Midterm                          % 20                       

Second Midterm                      % 30                       

Final Exam                              % 35

 

Course Home Page                 

The class web site can be accessed through online.metu.edu.tr. The home page will be used primarily to post lecture notes, data sets, assignments, and announcements. 

 

Softwares                               

Gretl, Eviews and Excel.

 

Further Requirements             

You are expected to attend classes regularly.

 

Detailed Course Outline*

Week

Subjects

 1.1

Non-Spherical Disturbances: Heteroscedasticity I

What is heteroscedasticity, consequences of heteroscedasticity, detecting heteroscedasticity (Informal way, Breusch-Pagan LM Test, Harvey-Godfrey LM Test, Goldfeld-Quandt Test, White Test)

1.2

Non-Spherical Disturbances: Heteroscedasticity II

Resolving Heteroscedasticity (when variance is known: Generalized or Weighted Least Squares, when variance is not known: White’s heteroscedasticity consistent variances and standard errors)

2.1

Non-Spherical Disturbances: Autocorrelation I

What is autocorrelation, consequences of autocorrelation, detecting autocorrelation (graphical method, Durbin-Watson Test, Breusch-Godfrey LM test for serial correlation, Durbin’s h test)

2.2

Non-Spherical Disturbances: Autocorrelation II

Resolving autocorrelation (when r is known: Generalized Least Squares, when r is unknown: Estimated GLS)

3.1

Non-Spherical Disturbances: Autocorrelation III

Newey-West Method, Common-factor test, apparent autocorrelation.

3.2

Modelling the Volatility: ARCH-GARCH Models I

The autoregressive conditional heteroscedasticity (ARCH) model, testing for ARCH effects, Estimation of ARCH models

4.1

Modelling the Volatility: ARCH-GARCH Models II

The generalized autoregressive conditional heteroscedasticity (GARCH) model, Estimation of GARCH Models

4.2

Estimating Dynamic Models I

Basic Ideas, Adjustment Lags, Problems in the estimation of Dynamic Models

5.1

Estimating Dynamic Models II

The formation of Expectations: Adaptive Expectations, Rational Expectations

5.2

Choosing the Appropriate Model I

The Consequences and Detection of Errors of Specification, Data Mining

6.1

Choosing the Appropriate Model II

Alternative Approaches to Selecting the Best Model, Selecting Models: Some Important Criteria

6.2

Time Series Econometrics: Non-Stationarity and Unit Root Tests I

Stationarity and non-stationarity, Unit Roots and Spurious Regressions

7.1

Time Series Econometrics: Non-Stationarity and Unit Root Tests II

Testing For Unit Roots (Dickey-Fuller Test, Augmented Dickey-Fuller Test, Phillips-Perron Test)

7.2

Cointegration and Error-Correction Models I

What is cointegration, cointegration and error-correction model (ECM)

8.1

Cointegration and Error-Correction Models II

Cointegration in Single Equations  (The Engle-Granger Approach, Drawbacks of Engle Granger Approach)

8.2

Vector Autoregressive (VAR) Models and Causality Tests I

Vector Autoregressive (VAR) Models

9.1

Vector Autoregressive (VAR) Models and Causality Tests II

Causality Tests (The Granger Causality Test, The Sims Causality Test)

9.2

Cointegration in Multiple Equations I

Cointegration in Multiple Equations, Johansen Approach

10.1

Cointegration in Multiple Equations II

Steps of Johansen Approach

10.2

Cointegration in Multiple Equations III

Identification in Standard and Cointegrated Systems (Order Condition, Rank Condition)

11.1

Panel Data Econometrics: Traditional Panel Data Models

Fixed Effects Model, Random Effects Model and Hausman Test

11.2

Panel Data Econometrics: Non-Stationary Panels I

Panel Unit Root Tests (Levin and Lin Test; Im, Pesaran and Shin Test, Maddala and Wu Test)

12.1

Panel Data Econometrics: Non-Stationary Panels II

Panel Cointegration Tests (Kao Test, McCoskey Test, Pedroni Tests, The Larsson Test)

12.2

Simultaneous Equation Models I

Simultaneity, The Identification Problem, Conditions of Identification

13.1

Simultaneous Equation Models II

Estimation of an Exactly Identified Equation: the Indirect Least Squares (ILS), Estimation of an Over-Identified Equation: The Two-Stage Least Squares (TSLS)

* Tentaive, subject to change.

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