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ECON305: Econometrics 1


206172 and 208272


The information in this website are designed for those who study with Dr Pairach Piboonrungroj. However students from other section could benefit from this website but please re-check with your lecturer.

Course Description

A review of matrices and statistics, Introduction to econometric models and techniques, The ordinary least squares estimators, Testing the assumptions of ordinary least squares estimators, Generalized least squares, Dummy dependent variable models, and non linear estimators.

Course Outline

  1. The Natural of Econometrics and Economic Data
  2. The Simple Regression Analysis
  3. Multiple Regression Analysis: Estimation
  4. Multiple Regression Analysis: Inference
  5. Multiple Regression Analysis: OLS Asymptotics
  6. Multiple Regression Analysis: Further Issues
  7. Multiple Regression Analysis with Qualitative Information Binary (or Dummy) Variables
  8. Heteroskedasticity
  9. Probit and Logit Model (Chapter 17)


  • 40% from Midterm Exam (Topics 1-6) on October 1st, 12:00 – 15:00 hours
  • 40% from Final Exam (Topics 7-11) on December 7th, 8:00 – 11:00 hours
  • 20% from Assignment, a report on applying multiple regression to predict an economic variable of your interest.

Assignments – Regression Model Development

Regression Model
Individually, please develop a multiple regression model to predict any economic variable using at least five predictors Then write a 1,000 words of report.
Due date: The Final exam, please bring a hard copy of the report and submit in front of the exam venue. The

Assignment Structure

  1. Part 1: Conceptual Framework
    1.1 Explaining the Economic Model and Econometric Model
    1.2 Providing supportive theories or logic.
  2. Part 2: Descriptive Statistics
    2.1 Explaining your variables
    2.2 How can you get them
  3. Part 3: Data Visulisation and Exploratory Analysis
    3.1 Normality check with Boxplot
    3.2 Relationship between variables using the plot or correlation / covariance
  4. Part 4: Model Results
    4.1 Single regression model
    4.2 Multiple regression model
    4.3 Statistics inference including t-test and F-test
    4.4 Goodness of Fit Indices e.g., R Square, Adjusted R Square and Chi-Square Test
  5. Part 5: Conclusions
    5.1 Learning form the model
    5.2 Policy suggestion or Managerial implication
    5.3 Model limitations
    5.4 Suggestions for the future study

Main TextbookimageServlet

Jeffrey M. Wooldridge (2015) Introductory Econometrics: A Modern Approach, 6th Ed., 789 pp.

Discover how empirical researchers today actually consider and apply econometric methods with the practical approach in Wooldridge’s INTRODUCTORY ECONOMETRICS: A MODERN APPROACH, 6E. Unlike traditional texts, this book uniquely demonstrates how econometrics has moved beyond a set of abstract tools to become genuinely useful for answering questions in business, policy evaluation, and forecasting. INTRODUCTORY ECONOMETRICS is organized around the type of data being analyzed with a systematic approach that only introduces assumptions as they are needed. This makes the material easier to understand and, ultimately, leads to better econometric practices. Packed with relevant applications, the text incorporates more than 100 intriguing data sets, available in six formats. Updates introduce the latest emerging developments in the field. Gain a full understanding of the impact of econometrics in practice today with the insights and applications found only in INTRODUCTORY ECONOMETRICS: A MODERN APPROACH, 6E.

How to get the book

  • via Cengage Website click here
  • via Amazon click here

R Programming


  • CRAN Task View: Econometrics (link)
  • Using R for Introductory Econometrics (link)



  • Python for Econometrics by Kevin Sheppard (link)
  • Data Analysis in Python/Econometrics (link)

Other resources



  • Introduction to Econometrics, by James H. Stock and Mark W. Watson
  • Basic Econometrics, by Damodar N. Gujarati


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