Econometricscoursoutline MBAMS.doc

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IQRA NATIONAL UNIVERSITY

BUSINESS ADMINSTRATION DEPARTMENT

COURSE OUTLINE

Course Title: Econometrics (ASC-512)Credit Hours: Three (3)Number of Lectures: 16 (3 hours each)

Date of Outline: Aug, 2012Class MS-MS-1st

Course Instructor: Fawad Ahmad

Email: [email protected]

AIMS AND OBJECTIVES:

Moto of this course is learning by doing.

This course mainly revolves around regression analysis. The course commences with the

definition and historical perspective of simple regression equation. After discussing the

properties of OLS estimators we move on to topics of testing of hypothesis, applications of

Gauss-Markov theorem, and interval estimation. After introducing the topic of multiple

regressions we move on to issues such as multicollinearity, hetroskedasticity, and

autocorrelation. Throughout the course our main approach is to present the topics in an easily

understandable format with emphasizes on real-world examples and exercises. Still the

prerequisites for the course are to have familiarity with calculus, matrix algebra, introductory

statistics and macro and microeconomics.

Goals

1. The main goal of the course is to introduce econometric analysis to the students

and to enable them to apply various modern tools of this analysis in their future

work and studies like SPSS, GRETL and EVIEWS.

2. Enables students to thoroughly understand the theoretical foundation of

Regression

3. Enables students to derive the basic properties of OLS estimators

4. Enables students to estimate a regression equation with real data

5. Enables students to understand and handle topics such as Dummy Variables,

Multicolinearity, Hetroskedasticity, and Autocorrelation

Text Books

1. Gujarati, D.Basic Econometrics, (McGraw Hill, 2003) 4th edition (GJ)

Additional Readings

1. Maddala, G.S.Introduction to Econometrics (Prentice Hall, 1992) 2nd Edition

2. Pindyck and Rubinfeld.Econometric Models and Economic Forecasts (Mc-Graw Hill, 1991)

3rd edition3. Ramanathan, R.Introductory Econometrics with Applications (Dryden198) 4th edition.


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LECTURES SCHEDULE

1.0 Econometrics?

1.1 What is econometrics?

1.2 Why a separate subject?1.3 Methodology of econometrics.1. Statement of theory or hypothesis.

2. Specification of the mathematical model of the theory3. Specification of the statistical, or econometric, model

4. Collecting the data5. Estimation of the parameters of the econometric model

6. Hypothesis testing

7. Forecasting or prediction8. Using the model for control or policy purposes.

2. Introduction to SPSS, GRETL fordata analysis.

1.0 Two-Variable Regression Analysis:

1.1 Introduction

1.2 The Concept of Population Regression Function (PRF)1.3 Stochastic Specification of PRF

1.4 The Significance of the Stochastic Disturbance Term1.5 The Sample Regression Function (SRF)

Suggested Readings: GJ: Chap. 2,

2.0 Two-Variable Regression Model

2.1 The Method of Ordinary Least Squares (OLS)

2.2 The Classical Linear Regression Model2.3 Standard Errors of Least-Squares Estimates2.4 Properties of Least Squares Estimators

2.5 The Gauss-Markov Theorem

2.6 The Coefficient of Determination

2.7 Data Mining and Manipulations by SPSS, GRETL.


3.0 Classical Normal Linear Regression Model (CNLRM): The Normality Assumption,

IntervalEstimation and Hypothesis Testing

3.1 The Probability Distribution and the Normality Assumption of Disturbance iu

3.2 Properties of OLS Estimators under the Normality Assumption

3.3 Probability Distributions Related to the Normal Distribution: The t, Chi-square, andF Distribution.

3.4 Interval Estimation

3.5 Confidence Intervals for Regression Coefficients

3.6 Hypothesis Testing3.7 The Problem of Prediction

3.8 Reporting and Evaluating the Results of Regression Analysis3.9 Computer Practical

i. Formulation and Specification of Economic Modelii. Basic Statistical Analysis

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Suggested Readings: GJ: Chap. 4, 5

4.0 Extensions of the Two-Variable Linear Regression Model.

4.1 Regression through the Origin

4.2 Scaling and Units of Measurement4.3 Functional Forms of Regression Models

4.4 Log-Linear Regression Models4.5 Semilog Models

4.6 Reciprocal Models4.7 Computer Practical

i. Estimating Simple Linear Regression Model

ii. Hypothesis Testing


5.0 Multiple Regression Analysis

5.1 The Three-Variable Model5.2 OLS Estimation5.3 The Goodness of Fit

5.4 Dummy Variables5.5 Testing of Hypothesis

5.6 Computer Practicali. Estimating Extended Economic Model

ii. Interpretation of Results

Suggested Readings: GJ: Chap. 7, 8

6.0 Multicollinearity

6.1 The Nature of Multicollinearity6.2 Estimation in the Presence of Perfect Multicollinearity6.3 Estimation in the Presence of High but Imperfect Multicollinearity

6.4 Consequences of Multicollinearity

6.5 Computer Practical1. Tests for Multicollinearity

Suggested Readings: GJ: Chap. 10

7.0 Hetroskedasticity

7.1 The Nature of Hetroskedasticy

7.2 OLS Estimation in the Presence of Hetroskedasticity

7.3 The Method of Generalized Least Squares (GLS)7.4 Consequences of Hetroskedasticty7.5 Detection of Heteroskedasticity

7.6 Remedial Measures7.7 Computer Practical

i. Tests for Hetroskedasticity


8.0 Autocorrelation.

8.1 Introduction8.2 OLS Estimation in the Presence of Autocorrelation

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8.3 Properties of OLS Estimators in the Presence of Autocorrelation8.4 Detecting Autocorrelation

8.5 Remedial Measures8.6 Computer Practical

i. Tests for Hetroskedasticyii. Tests for Serial Correlation


9.0 Time series Econometrics.

9.1 A look at Economic time series Data.

9.2. Stochastic Process

9.3 Unit Root Stochastic Process9.4 trend stationarity and Differencing stationarity

9.5 Spurious Regression

9.6 Test for stationarity9.7 The Unit Root Test

9.8 Conversion of non stationary to stationary9.9 Co-integration Test

9.10 Computer Practical


10 Panel regression

Grading

Assignment presentation 10%

Quizzes 10%Mid term 30%

Final 50%

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Econometricscoursoutline MBAMS.doc

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